diff --git a/ML_Model/b-n-data.ipynb b/ML_Model/b-n-data.ipynb new file mode 100644 index 0000000..9a513c6 --- /dev/null +++ b/ML_Model/b-n-data.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8490121,"sourceType":"datasetVersion","datasetId":5065184}],"dockerImageVersionId":30775,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **1)INTRODUCTION**\n dealing with bio-degradable and non-biodegradable data where waste are beign seggregated by taking photo as input and counting how many stuff fall under bio-degradable and non-biodegradable . \n ","metadata":{}},{"cell_type":"markdown","source":"# **2)Preparation of data** \n","metadata":{}},{"cell_type":"code","source":"# Importing necessary libraries\n\n# Building deep learning models\nimport tensorflow as tf \nfrom tensorflow import keras \n# For accessing pre-trained models\nimport tensorflow_hub as hub \n# For separating train and test sets\nfrom sklearn.model_selection import train_test_split\n\n# For visualizations\nimport matplotlib.pyplot as plt\nimport matplotlib.image as img\nimport PIL.Image as Image\nimport cv2\n\nimport os\nimport numpy as np\nimport pathlib","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:53:57.698537Z","iopub.execute_input":"2024-10-02T16:53:57.699077Z","iopub.status.idle":"2024-10-02T16:53:57.706954Z","shell.execute_reply.started":"2024-10-02T16:53:57.699031Z","shell.execute_reply":"2024-10-02T16:53:57.705401Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"markdown","source":"**Preparing dataset** ","metadata":{}},{"cell_type":"code","source":"data_dir = \"../input/waste-dataset/dataset\" # Datasets path\ndata_dir = pathlib.Path(data_dir)\ndata_dir","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:53:58.403966Z","iopub.execute_input":"2024-10-02T16:53:58.404514Z","iopub.status.idle":"2024-10-02T16:53:58.414200Z","shell.execute_reply.started":"2024-10-02T16:53:58.404430Z","shell.execute_reply":"2024-10-02T16:53:58.412900Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"PosixPath('../input/waste-dataset/dataset')"},"metadata":{}}]},{"cell_type":"markdown","source":"**seperating categories**","metadata":{}},{"cell_type":"code","source":"biodegradable = list(data_dir.glob('biodegradable/*'))[:600]\ncardboard = list(data_dir.glob('cardboard/*'))[:600]\nelectronics = list(data_dir.glob('electronic/*'))[:600]\nglass = list(data_dir.glob('glass/*'))[:600]\nmetal = list(data_dir.glob('metal/*'))[:600]\npaper = list(data_dir.glob('paper/*'))[:600]\nplastic = list(data_dir.glob('plastic/*'))[:600]\ntrash = list(data_dir.glob('trash/*'))[:600]","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:53:59.038937Z","iopub.execute_input":"2024-10-02T16:53:59.039413Z","iopub.status.idle":"2024-10-02T16:53:59.088328Z","shell.execute_reply.started":"2024-10-02T16:53:59.039358Z","shell.execute_reply":"2024-10-02T16:53:59.086914Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":"**Checking samples**","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport matplotlib.image as img\nimport pathlib\n\n# Load image paths from each waste category\nbiodegradable = list(data_dir.glob('biodegradable/*'))[:600]\ncardboard = list(data_dir.glob('cardboard/*'))[:600]\nelectronics = list(data_dir.glob('electronic/*'))[:600]\nglass = list(data_dir.glob('glass/*'))[:600]\nmetal = list(data_dir.glob('metal/*'))[:600]\npaper = list(data_dir.glob('paper/*'))[:600]\nplastic = list(data_dir.glob('plastic/*'))[:600]\ntrash = list(data_dir.glob('trash/*'))[:600]\n\n# Create a figure with subplots to display images from each category\nfig, ax = plt.subplots(ncols=8, figsize=(24, 7))\nfig.suptitle('Waste Categories')\n\n# Helper function to display images if available, otherwise display a blank subplot\ndef display_image(category_list, index, title):\n if len(category_list) > 0:\n image = img.imread(category_list[0])\n ax[index].imshow(image)\n else:\n ax[index].text(0.5, 0.5, 'No Image', horizontalalignment='center', verticalalignment='center')\n ax[index].set_title(title)\n ax[index].axis('off')\n\n# Display images or placeholders for each category\ndisplay_image(biodegradable, 0, 'Biodegradable')\ndisplay_image(cardboard, 1, 'Cardboard')\ndisplay_image(electronics, 2, 'Electronics')\ndisplay_image(glass, 3, 'Glass')\ndisplay_image(metal, 4, 'Metal')\ndisplay_image(paper, 5, 'Paper')\ndisplay_image(plastic, 6, 'Plastic')\ndisplay_image(trash, 7, 'Trash')\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:53:59.655836Z","iopub.execute_input":"2024-10-02T16:53:59.656315Z","iopub.status.idle":"2024-10-02T16:54:00.692923Z","shell.execute_reply.started":"2024-10-02T16:53:59.656271Z","shell.execute_reply":"2024-10-02T16:54:00.691486Z"},"trusted":true},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Assigning a separate dictionary for images and their corresponding labels**","metadata":{}},{"cell_type":"code","source":"# Contains the images path for each waste category\ndf_images = {\n 'biodegradable' : biodegradable,\n 'cardboard' : cardboard,\n 'electronics' : electronics,\n 'glass' : glass,\n 'metal' : metal,\n 'paper' : paper,\n 'plastic' : plastic,\n 'trash' : trash\n}\n\n# Contains numerical labels for the categories\ndf_labels = {\n 'biodegradable' : 0,\n 'cardboard' : 1,\n 'electronics' : 2,\n 'glass' : 3,\n 'metal' : 4,\n 'paper' : 5,\n 'plastic' : 6,\n 'trash' : 7\n}\n\n# Verify the structure\nprint(df_images)\nprint(df_labels)\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:00.695128Z","iopub.execute_input":"2024-10-02T16:54:00.695599Z","iopub.status.idle":"2024-10-02T16:54:00.727681Z","shell.execute_reply.started":"2024-10-02T16:54:00.695553Z","shell.execute_reply":"2024-10-02T16:54:00.725962Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"{'biodegradable': [PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2091.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1805.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_38.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1473.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1038.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1663.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_411.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2089.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2443.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2017.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1287.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_316.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1178.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1309.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_360.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_170.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1691.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1100.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2333.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2373.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2440.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2561.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1856.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_112.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1943.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1889.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1570.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1359.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1021.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_245.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1342.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2427.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1582.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_414.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1953.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1610.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1979.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_395.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1398.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2236.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2563.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1490.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1514.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1551.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1713.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2279.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1520.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_298.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1577.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2252.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1232.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1783.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1243.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2297.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1302.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1253.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1134.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1140.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2147.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2250.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1639.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_391.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_407.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_290.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1003.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2026.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2409.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1498.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_102.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1651.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_407(1).jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1121.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2530.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1276.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2506.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2303.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1097.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1227.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1114.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_23.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1292.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2459.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1595.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_113.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_378.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1145.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_262.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1115.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_235.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1009.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1908.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1711.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2478.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2215.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_374.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1894.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_27.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1223.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_229.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_152.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2429.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1949.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2523.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1991.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2322.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2202.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1572.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1951.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1008.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2177.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_104.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1821.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1280.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1325.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1785.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1006.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1383.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1409.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_241.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1144.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_275.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2328.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1607.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1947.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1688.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1529.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_432.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1088.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2401.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2281.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1439.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_179.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2029.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2280.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1101.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2135.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_135.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1917.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1403.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1235.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1270.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1994.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2299.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2481.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1200.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1362.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1295.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1229.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_418.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1973.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2564.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2015.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1708.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2379.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_167.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_442.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1353.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_208.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_350.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1411.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1286.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_394.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1895.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2174.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_429.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1987.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1811.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1089.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2275.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_140.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_414(1).jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_396.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1584.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1675.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1004.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1127.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1547.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1802.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1191.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2366.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1753.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1756.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2549.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_344.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_274.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_312.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1619.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_143.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1245.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1400.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1480.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_164.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2423.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_21.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1394.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1489.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_18.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_162.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1850.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2084.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_365.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1587.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2151.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1237.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2330.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1326.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1625.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2300.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1896.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1345.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1474.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_403.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2314.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1549.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2122.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1304.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2132.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1544.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1553.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2475.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_303.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2267.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2327.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2006.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1535.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_357.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2411.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2003.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2382.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1647.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_392.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2378.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1234.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2182.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1726.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1561.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_111.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1372.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1697.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1077.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1788.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_337.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_19.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2278.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1481.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1396.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_232.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1974.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1012.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2464.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2178.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2508.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2176.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2431.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2096.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1023.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1250.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1294.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1450.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1010.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2264.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1404.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_154.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1458.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_144.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2150.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_281.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1329.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1530.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2118.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1684.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1387.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1911.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1182.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2302.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1665.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1598.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1880.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2390.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_255.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1591.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2223.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_268.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1098.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2347.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2100.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2386.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1417.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1626.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1615.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2492.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2323.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1521.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2395.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1428.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_439.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1151.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2294.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1522.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1946.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2525.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1148.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1215.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2173.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1869.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2397.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2509.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2414.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1966.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1886.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1882.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2266.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2268.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2036.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1786.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1867.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1029.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1816.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1705.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2368.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_299.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2028.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1162.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_380.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1893.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1721.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1281.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2034.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1898.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1637.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2462.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1031.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2405.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1759.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1899.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_118.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2065.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_151.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1596.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2075.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2289.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1563.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_131.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_141.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1249.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1483.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_376.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2550.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1437.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1035.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2069.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2078.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1517.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1297.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1668.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1071.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2463.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_123.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1447.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1052.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2225.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1045.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1091.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_194.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_121.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2227.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_366.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1984.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1367.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2162.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2025.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2288.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2376.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1414.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1602.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1180.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2024.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2148.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2329.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_240.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2496.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1714.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1592.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_284.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2419.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_261.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2441.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_132.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2208.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_295.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2312.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_317.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1420.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2136.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2157.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2107.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1554.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1658.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1519.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2241.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1631.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1277.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1929.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2391.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2340.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_215.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_116.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1384.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1361.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_192.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1935.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_346.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1812.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_146.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_410.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1268.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_153.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_148.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2230.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2334.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_368.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2346.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1888.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1831.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1452.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1916.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_369.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_277.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_373.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2115.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1196.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_425.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2110.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1696.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_330.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2504.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2408.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1030.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2011.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2090.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2113.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_175.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_355.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1478.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1897.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2338.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1954.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2203.jpeg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_422.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2074.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2163.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1748.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_202.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1734.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1914.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_436.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1252.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1502.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_16.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_399.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1357.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2128.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2454.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1944.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1611.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1707.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1301.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1221.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2286.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1451.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2285.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1667.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_328.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2351.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1838.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2101.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1807.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1198.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1374.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2121.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2342.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1885.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1373.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2304.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1571.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2058.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2545.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1846.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1324.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_419.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2037.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1695.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1995.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1781.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_381.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2064.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_413(1).jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1166.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1497.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1453.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1661.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1206.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1855.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1136.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1267.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2465.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1333.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1833.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2249.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1679.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1212.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_427.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1157.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1288.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_372.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_314.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2316.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2138.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1902.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1934.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1912.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1678.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1057.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2488.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1860.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_397.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_257.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1884.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1218.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2068.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2560.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2513.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2510.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_230.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2335.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1901.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1233.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1389.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_127.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1388.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2007.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1997.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2190.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1013.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1175.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1154.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2319.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1731.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1905.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_41(1).jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2071.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1019.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2213.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1787.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2046.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_205.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2516.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1852.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1488.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1560.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2051.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_267.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2546.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_430.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1532.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1399.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1950.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2514.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1343.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_435.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1284.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2001.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_269.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1177.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1174.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1129.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1369.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_315.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2228.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1132.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1137.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2125.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2296.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1730.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_172.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1047.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2389.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1111.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1692.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1810.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2167.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1792.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1440.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_42.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_2537.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1808.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_308.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1471.jpg'), PosixPath('../input/waste-dataset/dataset/biodegradable/TEST_BIODEG_ORI_1105.jpg')], 'cardboard': [PosixPath('../input/waste-dataset/dataset/cardboard/cardboard112.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard47.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard359.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard352.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard200.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard300.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard166.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard71.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard301.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard397.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard337.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard403.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard22.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard249.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard17.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard146.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard132.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard131.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard121.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard20.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard73.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard314.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard3.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard92.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard364.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard306.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard89.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard126.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard33.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard255.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard389.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard330.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard320.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard388.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard400.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard98.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard382.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard372.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard108.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard367.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard215.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard147.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard7.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard307.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard334.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard316.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard2.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard168.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard193.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard402.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard111.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard248.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard293.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard287.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard45.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard52.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard148.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard335.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard49.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard110.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard88.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard117.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard186.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard253.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard262.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard75.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard219.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard120.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard233.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard100.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard29.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard9.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard392.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard366.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard190.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard184.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard395.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard194.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard401.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard375.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard216.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard268.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard63.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard322.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard26.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard238.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard386.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard170.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard331.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard251.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard50.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard274.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard345.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard237.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard62.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard145.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard267.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard157.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard84.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard321.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard167.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard398.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard380.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard175.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard67.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard123.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard336.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard305.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard396.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard181.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard128.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard87.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard144.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard257.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard241.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard353.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard210.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard44.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard59.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard201.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard105.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard254.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard223.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard243.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard163.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard78.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard82.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard239.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard228.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard30.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard203.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard51.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard357.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard245.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard10.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard317.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard153.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard109.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard57.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard159.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard360.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard141.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard31.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard354.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard21.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard373.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard68.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard260.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard328.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard339.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard90.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard289.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard95.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard266.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard4.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard54.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard37.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard304.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard36.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard271.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard176.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard329.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard107.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard178.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard298.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard180.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard74.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard116.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard312.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard42.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard346.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard285.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard165.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard355.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard28.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard155.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard171.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard303.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard207.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard208.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard349.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard378.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard192.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard229.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard282.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard376.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard149.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard244.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard23.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard41.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard137.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard83.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard188.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard138.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard143.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard338.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard220.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard295.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard58.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard142.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard150.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard222.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard205.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard371.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard230.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard164.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard393.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard350.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard169.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard102.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard136.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard327.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard363.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard197.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard189.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard221.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard25.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard199.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard311.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard212.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard122.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard309.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard315.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard182.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard1.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard368.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard177.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard235.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard326.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard124.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard297.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard277.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard196.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard347.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard252.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard93.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard94.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard286.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard174.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard12.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard275.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard39.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard161.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard265.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard384.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard323.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard13.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard40.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard381.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard258.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard139.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard65.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard283.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard226.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard236.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard225.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard256.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard162.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard19.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard391.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard172.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard370.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard103.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard158.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard91.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard115.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard191.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard14.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard125.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard217.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard56.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard99.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard299.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard279.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard308.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard290.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard333.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard160.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard264.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard97.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard390.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard383.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard209.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard211.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard113.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard250.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard133.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard16.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard234.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard80.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard61.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard127.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard399.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard53.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard15.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard231.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard344.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard119.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard278.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard313.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard284.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard206.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard291.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard35.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard60.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard269.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard77.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard140.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard341.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard361.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard296.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard46.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard374.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard272.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard104.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard310.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard377.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard348.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard202.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard32.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard195.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard18.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard151.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard394.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard173.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard118.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard340.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard156.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard76.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard34.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard81.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard240.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard135.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard227.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard259.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard198.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard218.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard43.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard66.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard101.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard281.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard351.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard152.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard79.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard64.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard204.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard385.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard86.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard154.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard332.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard273.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard187.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard387.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard185.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard318.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard319.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard70.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard270.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard288.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard24.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard213.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard324.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard106.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard224.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard369.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard365.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard183.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard242.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard343.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard358.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard6.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard342.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard72.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard27.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard247.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard294.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard362.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard8.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard379.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard38.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard179.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard325.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard263.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard114.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard5.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard96.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard11.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard214.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard280.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard130.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard261.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard232.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard134.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard55.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard292.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard85.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard356.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard302.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard246.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard48.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard69.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard276.jpg'), PosixPath('../input/waste-dataset/dataset/cardboard/cardboard129.jpg')], 'electronics': [], 'glass': [PosixPath('../input/waste-dataset/dataset/glass/glass141.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass374.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass184.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass75.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass409.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass207.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass433.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass311.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass185.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass342.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass397.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass467.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass35.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass178.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass170.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass218.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass93.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass7.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass183.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass132.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass18.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass254.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass475.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass457.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass94.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass202.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass91.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass339.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass484.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass250.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass277.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass283.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass346.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass166.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass334.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass52.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass422.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass151.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass482.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass367.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass4.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass175.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass427.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass200.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass338.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass30.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass233.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass198.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass331.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass210.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass290.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass256.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass319.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass294.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass209.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass463.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass28.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass153.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass317.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass438.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass390.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass383.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass163.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass161.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass104.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass224.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass179.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass21.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass123.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass146.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass495.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass401.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass61.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass349.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass264.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass106.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass41.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass69.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass332.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass271.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass130.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass445.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass368.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass473.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass270.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass194.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass129.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass214.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass71.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass167.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass421.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass108.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass115.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass110.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass152.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass309.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass335.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass196.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass34.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass494.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass282.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass372.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass489.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass2.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass340.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass323.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass145.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass109.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass58.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass410.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass14.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass247.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass324.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass76.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass238.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass43.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass102.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass239.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass190.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass103.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass78.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass92.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass80.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass72.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass375.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass133.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass42.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass147.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass192.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass341.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass415.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass142.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass384.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass416.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass95.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass90.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass321.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass285.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass499.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass171.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass302.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass429.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass230.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass435.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass235.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass380.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass412.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass419.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass219.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass114.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass137.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass158.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass442.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass201.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass39.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass436.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass492.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass237.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass25.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass366.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass413.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass169.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass274.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass318.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass203.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass468.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass388.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass465.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass23.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass308.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass280.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass497.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass289.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass15.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass13.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass371.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass191.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass471.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass281.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass263.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass97.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass470.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass398.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass449.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass154.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass362.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass1.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass269.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass243.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass312.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass481.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass396.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass5.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass87.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass488.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass229.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass400.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass496.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass350.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass113.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass279.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass446.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass68.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass441.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass404.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass9.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass227.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass343.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass83.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass197.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass258.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass241.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass33.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass26.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass359.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass193.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass300.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass407.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass444.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass136.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass344.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass373.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass325.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass128.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass477.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass333.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass381.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass469.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass242.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass56.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass55.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass221.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass65.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass431.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass40.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass411.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass213.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass88.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass181.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass232.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass182.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass249.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass287.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass3.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass36.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass101.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass451.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass231.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass345.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass307.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass298.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass428.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass116.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass262.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass434.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass50.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass159.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass112.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass358.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass150.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass329.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass337.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass49.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass293.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass476.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass278.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass195.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass176.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass47.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass478.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass418.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass443.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass135.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass67.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass121.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass480.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass355.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass222.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass267.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass134.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass215.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass22.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass305.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass117.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass85.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass118.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass32.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass382.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass19.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass392.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass303.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass98.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass105.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass160.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass174.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass74.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass316.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass275.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass31.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass453.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass394.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass244.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass234.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass357.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass364.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass172.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass466.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass226.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass464.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass360.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass501.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass498.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass306.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass288.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass16.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass119.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass320.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass53.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass255.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass248.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass348.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass460.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass286.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass79.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass395.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass223.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass491.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass149.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass455.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass253.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass212.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass500.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass77.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass304.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass122.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass246.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass387.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass454.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass424.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass64.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass82.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass84.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass291.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass187.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass208.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass352.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass493.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass313.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass12.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass139.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass452.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass89.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass314.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass472.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass124.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass399.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass45.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass17.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass391.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass406.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass111.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass189.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass251.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass385.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass353.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass430.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass461.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass38.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass216.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass425.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass487.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass220.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass157.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass44.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass273.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass450.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass127.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass62.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass296.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass284.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass46.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass120.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass456.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass462.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass266.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass24.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass376.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass265.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass276.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass211.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass474.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass299.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass168.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass240.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass354.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass301.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass432.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass186.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass245.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass6.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass126.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass86.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass326.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass156.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass315.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass361.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass37.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass336.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass225.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass257.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass459.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass310.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass268.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass252.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass155.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass448.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass485.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass73.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass272.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass217.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass10.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass297.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass162.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass8.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass205.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass408.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass99.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass199.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass177.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass228.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass125.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass330.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass20.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass260.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass206.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass389.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass236.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass402.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass423.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass328.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass440.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass437.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass259.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass29.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass188.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass261.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass370.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass393.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass66.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass327.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass57.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass351.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass59.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass479.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass143.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass27.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass292.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass447.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass140.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass483.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass48.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass63.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass439.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass414.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass107.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass165.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass96.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass148.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass420.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass295.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass81.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass100.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass365.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass131.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass347.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass426.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass144.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass386.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass173.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass363.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass322.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass403.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass458.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass486.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass356.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass138.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass379.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass11.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass369.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass54.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass51.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass377.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass378.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass405.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass70.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass490.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass180.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass60.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass204.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass417.jpg'), PosixPath('../input/waste-dataset/dataset/glass/glass164.jpg')], 'metal': [PosixPath('../input/waste-dataset/dataset/metal/metal375.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal341.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal374.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal383.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal215.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal113.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal213.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal226.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal147.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal90.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal130.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal63.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal149.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal344.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal8.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal68.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal323.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal129.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal37.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal7.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal6.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal145.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal405.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal140.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal114.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal182.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal263.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal366.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal154.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal208.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal315.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal355.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal403.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal317.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal347.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal162.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal278.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal368.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal15.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal309.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal62.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal336.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal210.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal282.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal200.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal69.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal18.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal389.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal195.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal119.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal35.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal51.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal120.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal188.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal19.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal76.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal346.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal122.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal353.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal98.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal251.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal92.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal268.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal138.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal261.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal228.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal356.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal102.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal391.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal202.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal236.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal354.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal65.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal217.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal253.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal82.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal57.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal277.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal58(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal233.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal158.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal33.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal408.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal170.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal352.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal184.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal46.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal394.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal402.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal29.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal238.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal387.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal54.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal36.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal201.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal340.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal348.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal290.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal328.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal13.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal183.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal267.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal12.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal243.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal148.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal283.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal49.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal22.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal248.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal314.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal189.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal373.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal163.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal74.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal30.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal287.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal337.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal11.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal96.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal269.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal171.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal135.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal38.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal295.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal271.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal313.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal56(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal206.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal227.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal91.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal27.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal237.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal316.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal250.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal105.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal111.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal128.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal99.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal77.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal194.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal401.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal225.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal265.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal360.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal109.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal14.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal47.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal157.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal279.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal136.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal179.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal87.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal307.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal378.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal20.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal81.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal249.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal2.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal390.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal396.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal385.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal134.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal319.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal52.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal173.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal326.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal54(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal104.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal288.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal75.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal205.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal112.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal308.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal150.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal57(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal174.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal192.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal78.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal272.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal212.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal118.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal404.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal26.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal204.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal291.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal186.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal144.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal407.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal93.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal386.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal131.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal338.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal21.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal152.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal406.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal48.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal155.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal311.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal361.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal244.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal9.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal327.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal252.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal31.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal259.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal42.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal399.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal86.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal388.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal41.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal321.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal382.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal284.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal345.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal50.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal221.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal165.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal72.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal141.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal280.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal203.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal240.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal334.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal274.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal168.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal151.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal191.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal220.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal363.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal142.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal181.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal242.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal28.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal230.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal223.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal95.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal324.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal301.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal185.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal339.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal180.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal178.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal254.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal349.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal276.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal133.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal89.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal235.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal60.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal70.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal116.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal207.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal333.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal335.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal17.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal55(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal88.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal325.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal365.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal40.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal270.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal296.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal60(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal306.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal85.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal218.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal71.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal224.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal132.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal209.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal293.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal305.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal39.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal292.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal409.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal358.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal264.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal369.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal343.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal214.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal320.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal153.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal398.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal55.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal312.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal156.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal381.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal32.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal289.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal143.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal94.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal159.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal331.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal257.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal169.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal53.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal275.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal61.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal299.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal392.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal260.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal239.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal234.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal61(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal53(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal83.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal362.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal16.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal318.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal199.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal108.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal384.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal103.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal351.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal117.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal304.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal56.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal197.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal187.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal241.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal64.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal372.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal59.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal23.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal196.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal370.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal342.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal58.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal124.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal177.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal10.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal364.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal371.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal127.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal125.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal393.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal161.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal330.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal164.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal123.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal175.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal193.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal6(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal167.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal101.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal246.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal34.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal100.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal110.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal258.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal400.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal256.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal367.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal44.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal160.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal176.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal329.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal245.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal397.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal395.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal266.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal262.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal377.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal73.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal198.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal231.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal107.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal219.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal137.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal66.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal139.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal286.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal5.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal97.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal146.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal172.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal211.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal300.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal350.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal24.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal121.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal310.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal62(1).jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal4.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal80.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal380.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal1.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal322.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal410.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal285.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal302.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal45.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal126.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal273.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal3.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal115.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal222.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal247.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal216.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal67.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal379.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal357.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal376.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal232.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal303.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal298.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal25.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal84.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal359.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal166.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal229.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal190.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal43.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal255.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal281.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal297.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal106.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal79.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal294.jpg'), PosixPath('../input/waste-dataset/dataset/metal/metal332.jpg')], 'paper': [PosixPath('../input/waste-dataset/dataset/paper/paper508.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper20.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper574.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper213.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper14.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper181.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper134.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper578.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper156.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper309.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper562.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper525.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper536.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper103.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper538.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper222.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper81.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper101.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper445.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper482.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper10.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper341.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper404.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper215.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper96.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper7.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper376.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper114.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper1.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper122.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper464.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper12.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper124.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper39.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper424.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper53.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper528.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper560.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper80.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper371.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper496.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper343.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper502.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper483.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper159.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper594.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper11.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper333.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper529.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper410.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper347.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper495.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper567.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper563.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper467.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper155.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper334.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper219.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper262.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper414.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper136.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper150.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper69.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper329.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper436.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper368.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper84.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper257.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper363.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper542.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper260.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper439.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper275.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper207.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper366.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper82.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper552.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper300.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper553.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper231.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper227.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper176.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper433.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper466.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper358.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper246.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper547.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper232.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper431.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper72.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper161.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper382.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper541.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper274.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper398.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper485.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper438.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper515.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper90.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper78.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper70.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper568.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper23.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper373.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper355.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper411.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper30.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper474.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper151.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper310.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper516.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper294.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper443.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper54.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper535.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper277.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper139.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper210.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper42.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper520.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper229.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper233.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper361.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper128.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper209.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper545.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper437.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper367.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper409.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper102.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper417.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper548.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper582.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper132.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper534.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper263.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper490.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper452.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper236.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper320.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper518.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper190.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper62.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper186.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper315.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper444.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper418.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper104.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper357.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper426.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper157.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper281.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper194.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper403.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper13.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper296.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper457.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper505.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper589.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper539.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper79.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper312.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper249.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper524.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper428.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper97.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper28.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper497.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper447.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper245.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper117.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper60.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper268.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper235.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper465.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper374.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper390.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper228.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper577.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper581.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper64.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper364.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper555.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper171.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper523.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper108.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper201.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper401.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper360.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper337.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper448.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper369.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper37.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper420.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper212.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper463.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper322.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper46.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper451.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper493.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper292.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper304.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper110.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper217.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper83.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper415.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper111.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper352.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper57.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper413.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper278.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper191.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper335.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper393.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper579.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper344.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper89.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper106.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper204.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper251.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper306.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper272.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper526.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper112.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper205.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper187.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper179.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper123.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper583.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper317.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper77.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper340.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper203.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper488.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper65.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper549.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper318.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper319.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper165.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper241.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper152.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper15.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper75.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper313.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper544.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper396.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper87.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper487.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper287.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper22.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper113.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper517.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper537.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper400.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper276.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper453.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper311.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper455.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper395.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper267.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper503.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper402.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper510.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper50.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper512.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper192.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper405.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper237.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper531.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper504.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper302.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper44.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper182.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper133.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper73.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper154.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper164.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper31.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper566.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper6.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper527.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper500.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper378.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper586.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper197.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper4.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper172.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper324.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper365.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper264.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper254.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper588.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper498.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper472.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper584.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper284.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper348.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper342.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper462.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper331.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper55.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper477.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper259.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper76.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper119.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper61.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper506.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper91.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper591.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper362.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper226.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper385.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper195.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper121.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper5.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper432.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper211.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper471.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper265.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper26.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper221.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper354.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper252.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper587.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper478.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper303.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper105.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper33.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper356.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper34.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper130.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper533.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper370.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper308.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper242.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper99.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper41.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper93.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper338.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper440.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper327.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper558.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper116.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper305.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper125.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper71.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper407.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper202.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper40.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper261.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper98.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper280.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper49.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper332.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper391.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper178.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper35.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper107.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper543.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper167.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper224.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper576.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper285.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper163.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper131.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper169.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper66.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper258.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper383.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper193.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper397.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper316.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper3.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper511.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper282.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper557.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper126.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper43.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper118.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper214.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper513.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper289.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper59.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper269.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper86.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper225.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper489.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper476.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper206.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper441.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper138.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper149.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper556.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper142.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper36.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper561.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper144.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper307.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper170.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper416.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper359.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper389.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper162.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper199.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper423.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper244.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper143.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper51.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper174.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper330.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper24.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper137.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper349.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper248.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper351.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper519.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper399.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper532.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper115.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper74.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper273.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper188.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper196.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper446.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper379.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper135.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper372.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper572.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper377.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper408.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper427.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper239.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper288.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper17.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper430.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper425.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper442.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper540.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper392.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper406.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper380.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper323.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper421.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper412.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper473.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper507.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper514.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper198.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper220.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper434.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper247.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper336.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper298.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper585.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper461.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper593.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper52.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper16.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper565.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper491.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper184.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper422.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper8.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper223.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper501.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper68.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper286.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper216.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper270.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper346.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper45.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper475.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper554.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper200.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper570.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper166.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper2.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper109.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper449.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper481.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper480.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper580.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper92.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper238.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper339.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper140.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper460.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper168.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper353.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper326.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper458.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper521.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper266.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper147.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper590.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper564.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper435.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper189.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper479.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper314.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper291.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper375.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper94.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper492.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper173.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper569.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper243.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper88.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper180.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper388.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper325.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper386.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper559.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper21.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper290.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper183.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper345.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper218.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper429.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper95.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper573.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper67.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper419.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper470.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper29.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper328.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper271.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper350.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper454.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper129.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper459.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper283.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper141.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper47.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper185.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper158.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper58.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper234.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper384.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper145.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper486.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper153.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper48.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper279.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper592.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper19.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper456.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper56.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper250.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper256.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper18.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper509.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper387.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper38.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper321.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper25.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper394.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper146.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper550.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper255.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper297.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper175.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper230.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper295.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper499.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper575.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper301.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper63.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper253.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper127.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper571.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper120.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper177.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper494.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper450.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper469.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper32.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper240.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper299.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper293.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper9.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper546.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper522.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper530.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper160.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper100.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper148.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper381.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper27.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper208.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper85.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper551.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper468.jpg'), PosixPath('../input/waste-dataset/dataset/paper/paper484.jpg')], 'plastic': [PosixPath('../input/waste-dataset/dataset/plastic/plastic453.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic395.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic26.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic13.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic380.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic160.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic294.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic451.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic330.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic122.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic76.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic141.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic170.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic67.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic439.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic108.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic307.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic118.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic88.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic361.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic33.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic97.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic77.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic366.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic79.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic403.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic37.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic178.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic482.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic256.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic426.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic136.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic217.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic322.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic301.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic264.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic68.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic31.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic192.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic258.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic48.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic249.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic167.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic218.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic215.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic152.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic299.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic243.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic321.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic12.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic204.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic435.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic66.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic306.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic452.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic255.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic465.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic53.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic171.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic188.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic186.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic83.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic137.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic448.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic195.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic123.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic146.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic90.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic262.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic459.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic52.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic339.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic305.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic449.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic244.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic189.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic85.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic349.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic374.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic157.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic259.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic101.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic220.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic156.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic309.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic65.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic181.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic63.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic253.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic351.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic318.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic58.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic8.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic300.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic235.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic89.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic430.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic407.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic180.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic387.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic17.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic460.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic468.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic441.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic289.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic93.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic442.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic467.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic384.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic438.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic323.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic369.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic211.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic427.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic471.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic266.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic199.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic112.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic320.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic75.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic82.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic133.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic359.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic72.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic263.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic109.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic32.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic284.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic413.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic210.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic329.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic131.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic417.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic164.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic125.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic382.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic285.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic44.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic190.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic433.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic334.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic135.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic45.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic154.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic313.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic139.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic1.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic226.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic21.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic372.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic436.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic303.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic479.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic20.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic469.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic470.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic247.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic335.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic179.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic375.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic34.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic103.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic232.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic115.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic337.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic213.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic240.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic248.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic159.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic401.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic193.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic332.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic326.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic24.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic30.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic420.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic111.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic254.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic9.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic110.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic84.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic40.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic355.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic127.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic197.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic292.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic409.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic134.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic198.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic250.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic267.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic373.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic245.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic51.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic476.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic196.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic18.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic360.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic105.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic78.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic423.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic437.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic233.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic224.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic340.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic398.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic389.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic276.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic331.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic480.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic399.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic428.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic342.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic377.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic187.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic114.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic175.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic311.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic429.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic415.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic205.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic173.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic463.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic302.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic209.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic425.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic208.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic440.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic283.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic325.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic62.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic457.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic410.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic412.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic391.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic56.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic319.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic214.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic333.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic478.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic257.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic4.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic443.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic381.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic445.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic397.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic446.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic140.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic206.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic288.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic421.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic304.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic414.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic454.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic239.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic22.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic147.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic481.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic404.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic41.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic338.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic388.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic418.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic119.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic432.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic336.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic477.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic64.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic281.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic462.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic316.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic328.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic271.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic394.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic155.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic416.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic126.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic124.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic49.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic310.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic129.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic47.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic324.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic95.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic35.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic278.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic367.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic472.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic227.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic282.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic408.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic185.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic194.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic166.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic347.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic169.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic396.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic270.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic383.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic165.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic354.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic237.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic392.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic431.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic386.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic202.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic358.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic207.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic344.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic346.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic42.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic128.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic121.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic148.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic92.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic357.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic286.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic212.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic314.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic19.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic151.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic353.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic461.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic464.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic145.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic2.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic43.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic28.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic274.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic238.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic312.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic14.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic327.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic163.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic411.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic38.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic71.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic291.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic246.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic138.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic104.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic370.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic50.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic406.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic251.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic456.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic107.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic379.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic98.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic424.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic298.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic55.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic350.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic25.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic144.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic23.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic174.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic3.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic150.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic365.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic203.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic99.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic222.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic265.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic161.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic272.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic182.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic230.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic15.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic162.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic61.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic363.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic473.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic183.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic73.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic80.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic390.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic59.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic368.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic341.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic236.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic273.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic11.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic260.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic60.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic117.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic191.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic317.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic378.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic46.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic184.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic348.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic57.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic466.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic252.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic352.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic280.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic293.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic290.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic400.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic27.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic279.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic295.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic102.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic450.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic419.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic223.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic91.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic402.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic200.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic86.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic229.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic405.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic7.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic106.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic455.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic422.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic297.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic376.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic6.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic221.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic296.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic343.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic362.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic36.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic176.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic142.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic100.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic177.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic216.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic96.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic29.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic242.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic16.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic364.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic74.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic277.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic225.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic39.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic5.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic172.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic231.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic87.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic143.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic269.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic268.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic315.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic345.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic228.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic474.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic132.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic444.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic393.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic69.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic447.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic94.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic261.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic81.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic434.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic385.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic475.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic201.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic158.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic241.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic168.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic153.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic458.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic219.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic113.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic70.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic234.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic275.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic371.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic356.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic116.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic149.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic10.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic130.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic54.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic308.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic287.jpg'), PosixPath('../input/waste-dataset/dataset/plastic/plastic120.jpg')], 'trash': [PosixPath('../input/waste-dataset/dataset/trash/trash79.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash84.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash55.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash109.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash90.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash10.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash23.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash81.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash20.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash13.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash40.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash33.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash52.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash104.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash114.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash133.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash86.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash97.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash83.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash95.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash71.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash73.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash45.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash72.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash105.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash87.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash75.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash129.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash43.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash5.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash28.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash103.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash68.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash31.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash48.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash56.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash16.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash39.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash67.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash7.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash121.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash54.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash22.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash111.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash9.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash11.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash102.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash17.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash113.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash108.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash135.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash38.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash120.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash6.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash21.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash130.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash44.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash122.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash136.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash123.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash76.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash62.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash64.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash98.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash88.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash128.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash63.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash30.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash42.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash50.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash51.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash34.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash4.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash100.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash59.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash65.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash127.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash115.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash18.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash99.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash78.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash60.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash53.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash70.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash132.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash126.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash92.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash80.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash8.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash118.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash137.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash112.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash119.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash74.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash1.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash41.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash47.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash14.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash57.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash125.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash61.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash82.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash36.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash27.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash49.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash89.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash134.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash131.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash94.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash101.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash69.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash93.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash2.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash91.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash66.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash26.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash106.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash85.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash25.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash24.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash116.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash58.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash46.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash3.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash29.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash19.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash35.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash37.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash107.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash124.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash110.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash96.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash77.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash12.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash117.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash15.jpg'), PosixPath('../input/waste-dataset/dataset/trash/trash32.jpg')]}\n{'biodegradable': 0, 'cardboard': 1, 'electronics': 2, 'glass': 3, 'metal': 4, 'paper': 5, 'plastic': 6, 'trash': 7}\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Since the MobileNetv2 training images dimensions are 224 by 224 by 3, we have to reshape our categories into that**","metadata":{}},{"cell_type":"code","source":"import cv2\nimport numpy as np\n\n# Example: Reshaping one image (arborio category)\nimg = cv2.imread(str(df_images['biodegradable'][0])) # Load the first image from 'biodegradable' category\nprint(\"Original shape:\", img.shape) # Print the original dimensions\n\n# Resize the image to 224x224 (MobileNetV2 required dimensions)\nresized_img = cv2.resize(img, (224, 224))\n\nprint(\"Reshaped dimensions:\", resized_img.shape) # Print the new dimensions\n\n# Converting the image to numerical array\nresized_img = np.array(resized_img)\n\n# Repeat the resizing for all images in the dataset\ndef resize_images(image_list, target_size=(224, 224)):\n resized_images = []\n for image_path in image_list:\n img = cv2.imread(str(image_path))\n resized_img = cv2.resize(img, target_size)\n resized_images.append(resized_img)\n return np.array(resized_images)\n\n# Reshape all categories to 224x224\nresized_biodegradable = resize_images(df_images['biodegradable'])\nresized_cardboard = resize_images(df_images['cardboard'])\nresized_electronics = resize_images(df_images['electronics'])\nresized_glass = resize_images(df_images['glass'])\nresized_metal = resize_images(df_images['metal'])\nresized_paper = resize_images(df_images['paper'])\nresized_plastic = resize_images(df_images['plastic'])\nresized_trash = resize_images(df_images['trash'])\n\n# Example: Display the shape of one resized image category\nprint(\"Reshaped biodegradable image set shape:\", resized_biodegradable.shape)\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:00.915447Z","iopub.execute_input":"2024-10-02T16:54:00.915942Z","iopub.status.idle":"2024-10-02T16:54:12.375196Z","shell.execute_reply.started":"2024-10-02T16:54:00.915898Z","shell.execute_reply":"2024-10-02T16:54:12.373461Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"Original shape: (200, 200, 3)\nReshaped dimensions: (224, 224, 3)\nReshaped biodegradable image set shape: (600, 224, 224, 3)\n","output_type":"stream"}]},{"cell_type":"code","source":"import cv2\nimport numpy as np\n\n# X will hold the images, y will hold the corresponding labels\nX, y = [], []\n\n# Iterate through each category and its images\nfor label, images in df_images.items():\n for image in images:\n img = cv2.imread(str(image)) # Load the image\n if img is not None: # Check if the image is loaded correctly\n resized_img = cv2.resize(img, (224, 224)) # Resize to 224x224\n X.append(resized_img) # Append resized image to X\n y.append(df_labels[label]) # Append the label to y\n\n# Convert X and y to NumPy arrays for further processing\nX = np.array(X)\ny = np.array(y)\n\n# Example: Display the shapes of X and y\nprint(\"X shape (images):\", X.shape) # Should be (num_images, 224, 224, 3)\nprint(\"y shape (labels):\", y.shape) # Should be (num_images,)\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:12.377606Z","iopub.execute_input":"2024-10-02T16:54:12.378121Z","iopub.status.idle":"2024-10-02T16:54:20.476265Z","shell.execute_reply.started":"2024-10-02T16:54:12.378074Z","shell.execute_reply":"2024-10-02T16:54:20.474446Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"X shape (images): (3137, 224, 224, 3)\ny shape (labels): (3137,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Splitting the data and standarization**","metadata":{}},{"cell_type":"code","source":"# Standarizing\nX = np.array(X)\nX = X/255\ny = np.array(y)","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:20.478577Z","iopub.execute_input":"2024-10-02T16:54:20.479204Z","iopub.status.idle":"2024-10-02T16:54:23.308086Z","shell.execute_reply.started":"2024-10-02T16:54:20.479142Z","shell.execute_reply":"2024-10-02T16:54:23.305745Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"# Separating data into training, test and validation sets\nX_train, X_test_val, y_train, y_test_val = train_test_split(X, y)\nX_test, X_val, y_test, y_val = train_test_split(X_test_val, y_test_val)","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:23.312225Z","iopub.execute_input":"2024-10-02T16:54:23.312898Z","iopub.status.idle":"2024-10-02T16:54:26.405584Z","shell.execute_reply.started":"2024-10-02T16:54:23.312814Z","shell.execute_reply":"2024-10-02T16:54:26.404008Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"markdown","source":"# 3)**Creating the model**","metadata":{}},{"cell_type":"code","source":"mobile_net = 'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4' # MobileNetv4 link\nmobile_net = hub.KerasLayer(\n mobile_net, input_shape=(224,224, 3), trainable=False) # Removing the last layer","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:26.407937Z","iopub.execute_input":"2024-10-02T16:54:26.408544Z","iopub.status.idle":"2024-10-02T16:54:28.871042Z","shell.execute_reply.started":"2024-10-02T16:54:26.408472Z","shell.execute_reply":"2024-10-02T16:54:28.869526Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras.applications import MobileNetV2\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n\n# Number of labels in the dataset\nnum_label = 8 # e.g., 'biodegradable', 'cardboard', etc.\n\n# Load the pre-trained MobileNetV2 without the top classification layer\nmobile_net = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n\n# Freeze the MobileNetV2 base model\nmobile_net.trainable = False\n\n# Create the full model using MobileNetV2 as the base\nmodel = keras.Sequential([\n mobile_net, # MobileNetV2 as base\n GlobalAveragePooling2D(), # Global pooling to reduce the dimensions\n Dense(num_label, activation='softmax') # Final classification layer with 'num_label' outputs\n])\n\n# Display the model architecture\nmodel.summary()\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:28.872941Z","iopub.execute_input":"2024-10-02T16:54:28.873391Z","iopub.status.idle":"2024-10-02T16:54:29.897742Z","shell.execute_reply.started":"2024-10-02T16:54:28.873347Z","shell.execute_reply":"2024-10-02T16:54:29.896179Z"},"trusted":true},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_2\"\u001b[0m\n","text/html":"
Model: \"sequential_2\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ mobilenetv2_1.00_224 │ ? │ \u001b[38;5;34m2,257,984\u001b[0m │\n│ (\u001b[38;5;33mFunctional\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_2 │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ mobilenetv2_1.00_224            │ ?                      │     2,257,984 │\n│ (Functional)                    │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_2      │ ?                      │   0 (unbuilt) │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (Dense)                 │ ?                      │   0 (unbuilt) │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,257,984\u001b[0m (8.61 MB)\n","text/html":"
 Total params: 2,257,984 (8.61 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"
 Trainable params: 0 (0.00 B)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,257,984\u001b[0m (8.61 MB)\n","text/html":"
 Non-trainable params: 2,257,984 (8.61 MB)\n
\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4) **Training the Model**","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\nfrom tensorflow.keras import Sequential\nimport tensorflow as tf\n\n# Number of labels (categories)\nnum_label = 8 # If there are 7 distinct categories\n\n# Define the model\nmodel = Sequential([\n mobile_net, # Pretrained MobileNetV2 model\n GlobalAveragePooling2D(), # Pooling layer to reduce dimensions\n Dense(num_label, activation='softmax') # Output layer with 7 categories\n])\n\n# Compile the model\nmodel.compile(\n optimizer=\"adam\",\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), # Using softmax so from_logits=False\n metrics=['acc'] # Accuracy as a performance metric\n)\n\n# Train the model\nhistory = model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))\n\n# After training, you can plot or analyze the history object for the results\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T16:54:29.899233Z","iopub.execute_input":"2024-10-02T16:54:29.899611Z","iopub.status.idle":"2024-10-02T17:06:19.754860Z","shell.execute_reply.started":"2024-10-02T16:54:29.899564Z","shell.execute_reply":"2024-10-02T17:06:19.752437Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"Epoch 1/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 913ms/step - acc: 0.4681 - loss: 1.4364 - val_acc: 0.7868 - val_loss: 0.6266\nEpoch 2/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 878ms/step - acc: 0.8006 - loss: 0.5617 - val_acc: 0.8325 - val_loss: 0.4844\nEpoch 3/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 884ms/step - acc: 0.8571 - loss: 0.4250 - val_acc: 0.8579 - val_loss: 0.4259\nEpoch 4/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 906ms/step - acc: 0.8914 - loss: 0.3413 - val_acc: 0.8629 - val_loss: 0.4131\nEpoch 5/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 882ms/step - acc: 0.9182 - loss: 0.2918 - val_acc: 0.8782 - val_loss: 0.3894\nEpoch 6/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 895ms/step - acc: 0.9228 - loss: 0.2594 - val_acc: 0.8782 - val_loss: 0.3885\nEpoch 7/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 886ms/step - acc: 0.9380 - loss: 0.2258 - val_acc: 0.8477 - val_loss: 0.4283\nEpoch 8/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 879ms/step - acc: 0.9404 - loss: 0.2178 - val_acc: 0.8832 - val_loss: 0.3617\nEpoch 9/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 878ms/step - acc: 0.9553 - loss: 0.1902 - val_acc: 0.8883 - val_loss: 0.3602\nEpoch 10/10\n\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 913ms/step - acc: 0.9597 - loss: 0.1716 - val_acc: 0.8934 - val_loss: 0.3622\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 5)**Evaluate the Model**","metadata":{}},{"cell_type":"code","source":"# Check the unique labels in the dataset\nprint(set(y_train))","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:06:19.759411Z","iopub.execute_input":"2024-10-02T17:06:19.760121Z","iopub.status.idle":"2024-10-02T17:06:19.771089Z","shell.execute_reply.started":"2024-10-02T17:06:19.760064Z","shell.execute_reply":"2024-10-02T17:06:19.769208Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"{0, 1, 3, 4, 5, 6, 7}\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the model on the test data\ntest_loss, test_acc = model.evaluate(X_test, y_test)\n\n# Print the results\nprint(f\"Test Loss: {test_loss}\")\nprint(f\"Test Accuracy: {test_acc}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:06:19.783808Z","iopub.execute_input":"2024-10-02T17:06:19.784319Z","iopub.status.idle":"2024-10-02T17:06:35.902511Z","shell.execute_reply.started":"2024-10-02T17:06:19.784276Z","shell.execute_reply":"2024-10-02T17:06:35.900753Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stdout","text":"\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 786ms/step - acc: 0.7994 - loss: 0.5176\nTest Loss: 0.49666041135787964\nTest Accuracy: 0.8163265585899353\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\n\ny_pred = model.predict(X_test, batch_size=64, verbose=1)\ny_pred_bool = np.argmax(y_pred, axis=1)\n\nprint(classification_report(y_test, y_pred_bool))","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:06:35.907968Z","iopub.execute_input":"2024-10-02T17:06:35.908447Z","iopub.status.idle":"2024-10-02T17:06:56.522564Z","shell.execute_reply.started":"2024-10-02T17:06:35.908404Z","shell.execute_reply":"2024-10-02T17:06:56.520721Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step\n precision recall f1-score support\n\n 0 0.98 0.99 0.98 98\n 1 0.87 0.87 0.87 63\n 3 0.72 0.77 0.74 105\n 4 0.82 0.88 0.85 78\n 5 0.84 0.86 0.85 114\n 6 0.75 0.69 0.72 101\n 7 0.50 0.34 0.41 29\n\n accuracy 0.82 588\n macro avg 0.78 0.77 0.78 588\nweighted avg 0.81 0.82 0.81 588\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 6)Visualizing the Model","metadata":{}},{"cell_type":"code","source":"from plotly.offline import iplot, init_notebook_mode\nimport plotly.express as px\nimport pandas as pd\n\ninit_notebook_mode(connected=True)\n\nacc = pd.DataFrame({'train': history.history['acc'], 'val': history.history['val_acc']})\n\nfig = px.line(acc, x=acc.index, y=acc.columns[0::], title='Training and Evaluation Accuracy every Epoch', markers=True)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:06:56.524734Z","iopub.execute_input":"2024-10-02T17:06:56.525261Z","iopub.status.idle":"2024-10-02T17:06:56.632035Z","shell.execute_reply.started":"2024-10-02T17:06:56.525214Z","shell.execute_reply":"2024-10-02T17:06:56.630480Z"},"trusted":true},"execution_count":53,"outputs":[{"output_type":"display_data","data":{"text/html":" \n "},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"data":[{"hovertemplate":"variable=train
index=%{x}
value=%{y}","legendgroup":"train","line":{"color":"#000001","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines+markers","name":"train","orientation":"v","showlegend":true,"x":[0,1,2,3,4,5,6,7,8,9],"xaxis":"x","y":[0.6258503198623657,0.8167517185211182,0.8609693646430969,0.8852040767669678,0.9102891087532043,0.9192177057266235,0.928146243095398,0.942176878452301,0.9511054158210754,0.9579081535339355],"yaxis":"y","type":"scatter"},{"hovertemplate":"variable=val
index=%{x}
value=%{y}","legendgroup":"val","line":{"color":"#000002","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines+markers","name":"val","orientation":"v","showlegend":true,"x":[0,1,2,3,4,5,6,7,8,9],"xaxis":"x","y":[0.7868020534515381,0.8324872851371765,0.8578680157661438,0.8629441857337952,0.8781725764274597,0.8781725764274597,0.8477157354354858,0.8832487463951111,0.8883248567581177,0.893401026725769],"yaxis":"y","type":"scatter"}],"layout":{"template":{"data":{"candlestick":[{"decreasing":{"line":{"color":"#000033"}},"increasing":{"line":{"color":"#000032"}},"type":"candlestick"}],"contourcarpet":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"contourcarpet"}],"contour":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"contour"}],"heatmap":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"heatmap"}],"histogram2d":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"histogram2d"}],"icicle":[{"textfont":{"color":"white"},"type":"icicle"}],"sankey":[{"textfont":{"color":"#000036"},"type":"sankey"}],"scatter":[{"marker":{"line":{"width":0}},"type":"scatter"}],"table":[{"cells":{"fill":{"color":"#000038"},"font":{"color":"#000037"},"line":{"color":"#000039"}},"header":{"fill":{"color":"#000040"},"font":{"color":"#000036"},"line":{"color":"#000039"}},"type":"table"}],"waterfall":[{"connector":{"line":{"color":"#000036","width":2}},"decreasing":{"marker":{"color":"#000033"}},"increasing":{"marker":{"color":"#000032"}},"totals":{"marker":{"color":"#000034"}},"type":"waterfall"}]},"layout":{"coloraxis":{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]]},"colorscale":{"diverging":[[0,"#000021"],[0.1,"#000022"],[0.2,"#000023"],[0.3,"#000024"],[0.4,"#000025"],[0.5,"#000026"],[0.6,"#000027"],[0.7,"#000028"],[0.8,"#000029"],[0.9,"#000030"],[1,"#000031"]],"sequential":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"sequentialminus":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]]},"colorway":["#000001","#000002","#000003","#000004","#000005","#000006","#000007","#000008","#000009","#000010"]}},"xaxis":{"anchor":"y","domain":[0,1],"title":{"text":"index"},"type":"linear","range":[-0.580958549222798,9.580958549222798],"autorange":true},"yaxis":{"anchor":"x","domain":[0,1],"title":{"text":"value"},"type":"linear","range":[0.6015276123004355,0.9822308610958658],"autorange":true},"legend":{"title":{"text":"variable"},"tracegroupgap":0},"title":{"text":"Training and Evaluation Accuracy every Epoch"},"autosize":true},"config":{"plotlyServerURL":"https://plot.ly"}},"text/html":"
","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"loss = pd.DataFrame({'train': history.history['loss'], 'val': history.history['val_loss']})\n\nfig = px.line(loss, x=loss.index, y=loss.columns[0::], title='Training and Evaluation Loss every Epoch', markers=True)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:06:56.634341Z","iopub.execute_input":"2024-10-02T17:06:56.634936Z","iopub.status.idle":"2024-10-02T17:06:56.716569Z","shell.execute_reply.started":"2024-10-02T17:06:56.634850Z","shell.execute_reply":"2024-10-02T17:06:56.714956Z"},"trusted":true},"execution_count":54,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"data":[{"hovertemplate":"variable=train
index=%{x}
value=%{y}","legendgroup":"train","line":{"color":"#000001","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines+markers","name":"train","orientation":"v","showlegend":true,"x":[0,1,2,3,4,5,6,7,8,9],"xaxis":"x","y":[1.0249313116073608,0.5304258465766907,0.4177747070789337,0.3509646952152252,0.2985239326953888,0.2641059458255768,0.23985496163368225,0.21680141985416412,0.1928921490907669,0.17516271770000458],"yaxis":"y","type":"scatter"},{"hovertemplate":"variable=val
index=%{x}
value=%{y}","legendgroup":"val","line":{"color":"#000002","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines+markers","name":"val","orientation":"v","showlegend":true,"x":[0,1,2,3,4,5,6,7,8,9],"xaxis":"x","y":[0.6265882849693298,0.484429270029068,0.4258926808834076,0.41310441493988037,0.3893868327140808,0.3884968161582947,0.428274542093277,0.3617123067378998,0.3601856529712677,0.362161248922348],"yaxis":"y","type":"scatter"}],"layout":{"template":{"data":{"candlestick":[{"decreasing":{"line":{"color":"#000033"}},"increasing":{"line":{"color":"#000032"}},"type":"candlestick"}],"contourcarpet":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"contourcarpet"}],"contour":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"contour"}],"heatmap":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"heatmap"}],"histogram2d":[{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"type":"histogram2d"}],"icicle":[{"textfont":{"color":"white"},"type":"icicle"}],"sankey":[{"textfont":{"color":"#000036"},"type":"sankey"}],"scatter":[{"marker":{"line":{"width":0}},"type":"scatter"}],"table":[{"cells":{"fill":{"color":"#000038"},"font":{"color":"#000037"},"line":{"color":"#000039"}},"header":{"fill":{"color":"#000040"},"font":{"color":"#000036"},"line":{"color":"#000039"}},"type":"table"}],"waterfall":[{"connector":{"line":{"color":"#000036","width":2}},"decreasing":{"marker":{"color":"#000033"}},"increasing":{"marker":{"color":"#000032"}},"totals":{"marker":{"color":"#000034"}},"type":"waterfall"}]},"layout":{"coloraxis":{"colorscale":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]]},"colorscale":{"diverging":[[0,"#000021"],[0.1,"#000022"],[0.2,"#000023"],[0.3,"#000024"],[0.4,"#000025"],[0.5,"#000026"],[0.6,"#000027"],[0.7,"#000028"],[0.8,"#000029"],[0.9,"#000030"],[1,"#000031"]],"sequential":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]],"sequentialminus":[[0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1,"#000020"]]},"colorway":["#000001","#000002","#000003","#000004","#000005","#000006","#000007","#000008","#000009","#000010"]}},"xaxis":{"anchor":"y","domain":[0,1],"title":{"text":"index"},"type":"linear","range":[-0.580958549222798,9.580958549222798],"autorange":true},"yaxis":{"anchor":"x","domain":[0,1],"title":{"text":"value"},"type":"linear","range":[0.1129185213309944,1.087175507976371],"autorange":true},"legend":{"title":{"text":"variable"},"tracegroupgap":0},"title":{"text":"Training and Evaluation Loss every Epoch"},"autosize":true},"config":{"plotlyServerURL":"https://plot.ly"}},"text/html":"
","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 7)**Model Deployment**","metadata":{}},{"cell_type":"code","source":"model.save('waste_classification_model.h5')","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:26:28.175693Z","iopub.status.idle":"2024-10-02T17:26:28.176231Z","shell.execute_reply.started":"2024-10-02T17:26:28.175987Z","shell.execute_reply":"2024-10-02T17:26:28.176013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install streamlit tensorflow opencv-python","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:26:28.177910Z","iopub.status.idle":"2024-10-02T17:26:28.178475Z","shell.execute_reply.started":"2024-10-02T17:26:28.178214Z","shell.execute_reply":"2024-10-02T17:26:28.178238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Import necessary libraries\nimport numpy as np\nimport tensorflow as tf\nfrom PIL import Image\nimport io\nimport cv2\nimport streamlit as st\n\n# Load your trained model (Make sure to upload your model file to Kaggle)\nmodel = tf.keras.models.load_model('/kaggle/input/your-model-path/model.h5')\n\n# Waste categories\nwaste_categories = ['plastic', 'paper', 'glass', 'metal', 'organic', 'other', 'e-waste']\n\n# Function to preprocess image\ndef preprocess_image(image):\n image = image.resize((224, 224)) # Resize the image to the input size of the model\n image_array = np.array(image) / 255.0 # Normalize the image\n return np.expand_dims(image_array, axis=0) # Expand dimensions to match model input\n\n# Function to predict waste category for a single image\ndef predict_waste(image):\n processed_image = preprocess_image(image)\n prediction = model.predict(processed_image)\n return prediction\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:26:28.179945Z","iopub.status.idle":"2024-10-02T17:26:28.180444Z","shell.execute_reply.started":"2024-10-02T17:26:28.180220Z","shell.execute_reply":"2024-10-02T17:26:28.180245Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%html\n\n\n\n \n \n Waste Classification App\n \n\n\n

Waste Classification App

\n \n \n

\n\n \n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:26:28.182236Z","iopub.status.idle":"2024-10-02T17:26:28.182718Z","shell.execute_reply.started":"2024-10-02T17:26:28.182491Z","shell.execute_reply":"2024-10-02T17:26:28.182515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from flask import Flask, request, jsonify\n\n# Initialize Flask app\napp = Flask(__name__)\n\n@app.route('/classify', methods=['POST'])\ndef classify():\n if 'image' not in request.files:\n return jsonify({'error': 'No image provided'}), 400\n\n file = request.files['image']\n image = Image.open(file.stream)\n\n # Predict waste category\n prediction = predict_waste(image)\n category = waste_categories[np.argmax(prediction)]\n\n return jsonify({'category': category})\n\n# Run the Flask app\nif __name__ == '__main__':\n app.run(port=5000)\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:26:28.184821Z","iopub.status.idle":"2024-10-02T17:26:28.185350Z","shell.execute_reply.started":"2024-10-02T17:26:28.185089Z","shell.execute_reply":"2024-10-02T17:26:28.185113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install streamlit\n!streamlit run app.py & # Run your Streamlit application\n","metadata":{"execution":{"iopub.status.busy":"2024-10-02T17:26:28.187922Z","iopub.status.idle":"2024-10-02T17:26:28.188396Z","shell.execute_reply.started":"2024-10-02T17:26:28.188165Z","shell.execute_reply":"2024-10-02T17:26:28.188188Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/ML_Model/ewaste-analyzing-precious-metal-content-and-pro (1) (1).ipynb b/ML_Model/ewaste-analyzing-precious-metal-content-and-pro (1) (1).ipynb new file mode 100644 index 0000000..65015ee --- /dev/null +++ b/ML_Model/ewaste-analyzing-precious-metal-content-and-pro (1) (1).ipynb @@ -0,0 +1,728 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "472ec2ec", + "metadata": {}, + "source": [ + "E-waste is a treasure trove of precious metals. Did you know that your old electronics could be worth their weight in gold, literally? In this notebook, we'll dive into the precious metal content in e-waste and see what insights we can uncover. If you find this notebook useful, please upvote it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "181f31d3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.787831Z", + "iopub.status.busy": "2024-10-03T17:30:12.787000Z", + "iopub.status.idle": "2024-10-03T17:30:12.792943Z", + "shell.execute_reply": "2024-10-03T17:30:12.791792Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.787779Z" + } + }, + "outputs": [], + "source": [ + "# Suppress warnings\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba3bf273", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.799409Z", + "iopub.status.busy": "2024-10-03T17:30:12.798503Z", + "iopub.status.idle": "2024-10-03T17:30:12.808161Z", + "shell.execute_reply": "2024-10-03T17:30:12.806770Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.799360Z" + } + }, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "873380e6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.811586Z", + "iopub.status.busy": "2024-10-03T17:30:12.810570Z", + "iopub.status.idle": "2024-10-03T17:30:12.894460Z", + "shell.execute_reply": "2024-10-03T17:30:12.893176Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.811537Z" + } + }, + "outputs": [], + "source": [ + "# Load datasets\n", + "file_path1 = 'e_waste_dataset_with_profit.csv'\n", + "file_path2 = 'e_waste_dataset (1).csv'\n", + "file_path3 = 'updated_e_waste_dataset.csv'\n", + "\n", + "df1 = pd.read_csv(file_path1)\n", + "df2 = pd.read_csv(file_path2)\n", + "df3 = pd.read_csv(file_path3)" + ] + }, + { + "cell_type": "markdown", + "id": "7c49aaf1", + "metadata": {}, + "source": [ + "### Initial Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ccd317dc", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.896304Z", + "iopub.status.busy": "2024-10-03T17:30:12.895870Z", + "iopub.status.idle": "2024-10-03T17:30:12.927164Z", + "shell.execute_reply": "2024-10-03T17:30:12.925914Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.896261Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "( Item Category Gold Silver Platinum Rhodium Nickel Tin \\\n", + " 0 iPhone 11 Cat3 3.58 2.95 1.73 8.92 1.91 1.01 \n", + " 1 Toaster Cat2 7.21 4.31 6.21 5.63 9.59 7.65 \n", + " 2 Speaker Cat4 8.91 5.09 2.42 7.70 1.09 1.49 \n", + " 3 Microwave Oven Cat2 2.62 3.84 2.98 7.66 9.41 2.25 \n", + " 4 Air Conditioner Cat1 3.47 3.89 6.20 4.35 5.07 8.65 \n", + " \n", + " Lithium Aluminum Carbon Profit ($) \n", + " 0 1.82 1.27 9.51 270.34 \n", + " 1 0.51 3.03 4.22 689.75 \n", + " 2 7.42 3.63 8.83 570.43 \n", + " 3 7.84 6.18 6.36 290.78 \n", + " 4 8.62 0.82 5.53 505.16 ,\n", + " Item Gold (g) Aluminum (g) Silver (g) Carbon (g) \\\n", + " 0 OnePlus 9 Pro 0.43 1.01 0.84 0.61 \n", + " 1 Nintendo Switch 1.92 1.52 2.71 0.74 \n", + " 2 HP Spectre x360 1.42 1.29 2.29 0.97 \n", + " 3 Amazon Kindle 0.47 1.60 0.79 0.89 \n", + " 4 OnePlus 9 Pro 2.78 1.02 2.68 0.54 \n", + " \n", + " Platinum (g) Rhodium (g) Nickel (g) Tin (g) Lithium (g) \n", + " 0 0.07 0.08 0.49 1.19 0.38 \n", + " 1 0.08 0.09 0.40 0.55 2.91 \n", + " 2 0.19 0.10 0.65 0.68 0.85 \n", + " 3 0.16 0.06 0.49 0.91 0.94 \n", + " 4 0.11 0.06 1.37 0.74 0.31 ,\n", + " Item Name Category Brand Name Device Age Device Condition \\\n", + " 0 Toshiba Fire TV Cat2 Panasonic 3 Broken \n", + " 1 LG Sound Bar Cat4 Panasonic 12 Broken \n", + " 2 Nikon D850 Cat1 Sony 23 Good \n", + " 3 Amazon Echo Cat4 Lenovo 9 Broken \n", + " 4 JBL Charge 4 Cat3 Sony 17 Good \n", + " \n", + " Material Recovery Rate Device Type Year of Manufacture \\\n", + " 0 50.94 Consumer Electronics 2000 \n", + " 1 92.22 Consumer Electronics 1996 \n", + " 2 76.64 Appliance 2002 \n", + " 3 69.67 Appliance 2024 \n", + " 4 65.51 Consumer Electronics 2003 \n", + " \n", + " Market Value of Metals Cost of Recovery ... Aluminum (g) Silver (g) \\\n", + " 0 495.04 34.41 ... 22.73 0.35 \n", + " 1 494.41 10.02 ... 46.69 0.04 \n", + " 2 700.20 10.89 ... 44.67 1.59 \n", + " 3 533.94 7.37 ... 22.94 2.40 \n", + " 4 493.42 42.26 ... 40.85 1.70 \n", + " \n", + " Carbon (g) Platinum (g) Rhodium (g) Nickel (g) Tin (g) Lithium (g) \\\n", + " 0 2.54 1.56 0.05 14.94 0.64 6.13 \n", + " 1 2.42 0.45 0.78 1.98 4.96 4.48 \n", + " 2 2.58 0.62 0.79 11.73 1.05 6.74 \n", + " 3 6.13 0.58 0.77 0.25 0.28 5.03 \n", + " 4 7.07 0.82 0.82 10.61 1.81 4.82 \n", + " \n", + " Current Metal Value ($) Recycling Score \n", + " 0 253.866639 4 \n", + " 1 658.490621 4 \n", + " 2 723.642095 3 \n", + " 3 693.395058 4 \n", + " 4 578.926631 5 \n", + " \n", + " [5 rows x 22 columns])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the first few rows of each dataset\n", + "df1.head(), df2.head(), df3.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b40bb11e", + "metadata": {}, + "source": [ + "### Data Cleaning and Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "991a33e1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.929819Z", + "iopub.status.busy": "2024-10-03T17:30:12.929450Z", + "iopub.status.idle": "2024-10-03T17:30:12.946456Z", + "shell.execute_reply": "2024-10-03T17:30:12.945403Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.929771Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(Item 0\n", + " Category 0\n", + " Gold 0\n", + " Silver 0\n", + " Platinum 0\n", + " Rhodium 0\n", + " Nickel 0\n", + " Tin 0\n", + " Lithium 0\n", + " Aluminum 0\n", + " Carbon 0\n", + " Profit ($) 0\n", + " dtype: int64,\n", + " Item 0\n", + " Gold (g) 0\n", + " Aluminum (g) 0\n", + " Silver (g) 0\n", + " Carbon (g) 0\n", + " Platinum (g) 0\n", + " Rhodium (g) 0\n", + " Nickel (g) 0\n", + " Tin (g) 0\n", + " Lithium (g) 0\n", + " dtype: int64,\n", + " Item Name 0\n", + " Category 0\n", + " Brand Name 0\n", + " Device Age 0\n", + " Device Condition 0\n", + " Material Recovery Rate 0\n", + " Device Type 0\n", + " Year of Manufacture 0\n", + " Market Value of Metals 0\n", + " Cost of Recovery 0\n", + " Profit 0\n", + " Gold (g) 0\n", + " Aluminum (g) 0\n", + " Silver (g) 0\n", + " Carbon (g) 0\n", + " Platinum (g) 0\n", + " Rhodium (g) 0\n", + " Nickel (g) 0\n", + " Tin (g) 0\n", + " Lithium (g) 0\n", + " Current Metal Value ($) 0\n", + " Recycling Score 0\n", + " dtype: int64)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check for missing values\n", + "df1.isnull().sum(), df2.isnull().sum(), df3.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a87e092", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.948162Z", + "iopub.status.busy": "2024-10-03T17:30:12.947768Z", + "iopub.status.idle": "2024-10-03T17:30:12.961721Z", + "shell.execute_reply": "2024-10-03T17:30:12.960814Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.948096Z" + } + }, + "outputs": [], + "source": [ + "# Drop rows with missing values\n", + "df1.dropna(inplace=True)\n", + "df2.dropna(inplace=True)\n", + "df3.dropna(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "185c6ba8", + "metadata": {}, + "source": [ + "### Correlation Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b785b454", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.963957Z", + "iopub.status.busy": "2024-10-03T17:30:12.963532Z", + "iopub.status.idle": "2024-10-03T17:30:12.972207Z", + "shell.execute_reply": "2024-10-03T17:30:12.971048Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.963905Z" + } + }, + "outputs": [], + "source": [ + "# Select only numeric columns for correlation heatmap\n", + "numeric_df1 = df1.select_dtypes(include=[np.number])\n", + "numeric_df3 = df3.select_dtypes(include=[np.number])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4792242d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:12.974687Z", + "iopub.status.busy": "2024-10-03T17:30:12.974066Z", + "iopub.status.idle": "2024-10-03T17:30:13.607260Z", + "shell.execute_reply": "2024-10-03T17:30:13.605859Z", + "shell.execute_reply.started": "2024-10-03T17:30:12.974632Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot correlation heatmap for df1\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(numeric_df1.corr(), annot=True, cmap='coolwarm')\n", + "plt.title('Correlation Heatmap for e_waste_dataset_with_profit')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c984b43a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:13.609075Z", + "iopub.status.busy": "2024-10-03T17:30:13.608690Z", + "iopub.status.idle": "2024-10-03T17:30:14.741113Z", + "shell.execute_reply": "2024-10-03T17:30:14.739674Z", + "shell.execute_reply.started": "2024-10-03T17:30:13.609034Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot correlation heatmap for df3\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(numeric_df3.corr(), annot=True, cmap='coolwarm')\n", + "plt.title('Correlation Heatmap for updated_e_waste_dataset')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "31b8796c", + "metadata": {}, + "source": [ + "### Predicting Profit from Metal Content" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "04690c3c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:14.745323Z", + "iopub.status.busy": "2024-10-03T17:30:14.744818Z", + "iopub.status.idle": "2024-10-03T17:30:14.752620Z", + "shell.execute_reply": "2024-10-03T17:30:14.751306Z", + "shell.execute_reply.started": "2024-10-03T17:30:14.745271Z" + } + }, + "outputs": [], + "source": [ + "# Define features and target variable\n", + "X = numeric_df1.drop(columns=['Profit ($)'])\n", + "y = numeric_df1['Profit ($)']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "773d28cb", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:14.754689Z", + "iopub.status.busy": "2024-10-03T17:30:14.754211Z", + "iopub.status.idle": "2024-10-03T17:30:14.766957Z", + "shell.execute_reply": "2024-10-03T17:30:14.765486Z", + "shell.execute_reply.started": "2024-10-03T17:30:14.754636Z" + } + }, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1cbae531", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:14.768992Z", + "iopub.status.busy": "2024-10-03T17:30:14.768468Z", + "iopub.status.idle": "2024-10-03T17:30:14.785376Z", + "shell.execute_reply": "2024-10-03T17:30:14.783957Z", + "shell.execute_reply.started": "2024-10-03T17:30:14.768946Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train a linear regression model\n", + "model = LinearRegression()\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "815cffc5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:14.787249Z", + "iopub.status.busy": "2024-10-03T17:30:14.786769Z", + "iopub.status.idle": "2024-10-03T17:30:14.799518Z", + "shell.execute_reply": "2024-10-03T17:30:14.798022Z", + "shell.execute_reply.started": "2024-10-03T17:30:14.787208Z" + } + }, + "outputs": [], + "source": [ + "# Make predictions\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3a42586d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:30:14.803015Z", + "iopub.status.busy": "2024-10-03T17:30:14.802040Z", + "iopub.status.idle": "2024-10-03T17:30:14.813821Z", + "shell.execute_reply": "2024-10-03T17:30:14.812570Z", + "shell.execute_reply.started": "2024-10-03T17:30:14.802956Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(8.388869767444517e-06, 0.9999999998134541)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "mse, r2" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "922adab5", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import os\n", + "\n", + "coco_names_url = \"https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names\"\n", + "\n", + "if not os.path.exists(\"coco.names\"):\n", + " response = requests.get(coco_names_url)\n", + " with open(\"coco.names\", \"wb\") as f:\n", + " f.write(response.content)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1b3afd3c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T17:33:09.163809Z", + "iopub.status.busy": "2024-10-03T17:33:09.163281Z", + "iopub.status.idle": "2024-10-03T17:34:12.677960Z", + "shell.execute_reply": "2024-10-03T17:34:12.676386Z", + "shell.execute_reply.started": "2024-10-03T17:33:09.163762Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Item Category Gold Silver Platinum Rhodium Nickel Tin \\\n", + "0 iPhone 11 Cat3 3.58 2.95 1.73 8.92 1.91 1.01 \n", + "1 Toaster Cat2 7.21 4.31 6.21 5.63 9.59 7.65 \n", + "2 Speaker Cat4 8.91 5.09 2.42 7.70 1.09 1.49 \n", + "3 Microwave Oven Cat2 2.62 3.84 2.98 7.66 9.41 2.25 \n", + "4 Air Conditioner Cat1 3.47 3.89 6.20 4.35 5.07 8.65 \n", + "\n", + " Lithium Aluminum Carbon Profit ($) \n", + "0 1.82 1.27 9.51 270.34 \n", + "1 0.51 3.03 4.22 689.75 \n", + "2 7.42 3.63 8.83 570.43 \n", + "3 7.84 6.18 6.36 290.78 \n", + "4 8.62 0.82 5.53 505.16 \n", + "Item 0\n", + "Category 0\n", + "Gold 0\n", + "Silver 0\n", + "Platinum 0\n", + "Rhodium 0\n", + "Nickel 0\n", + "Tin 0\n", + "Lithium 0\n", + "Aluminum 0\n", + "Carbon 0\n", + "Profit ($) 0\n", + "dtype: int64\n", + "Mean Squared Error: 8.388869767430789e-06\n", + "R-squared Score: 0.9999999998134541\n", + "Mean Absolute Error: 0.0025068590802123543\n", + "Root Mean Squared Error: 0.002896354565213104\n", + "Pipeline saved to e_waste_pipeline.pkl\n", + "First prediction: 624.5198446977203\n" + ] + } + ], + "source": [ + "# Import necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.metrics import accuracy_score\n", + "import cv2\n", + "import pickle\n", + "\n", + "# Download YOLOv3 weights and configuration file\n", + "#!wget https://pjreddie.com/media/files/yolov3.weights\n", + "#!wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg\n", + "#!wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names\n", + "\n", + "# Load the dataset\n", + "file_path1 = 'e_waste_dataset_with_profit.csv'\n", + "df1 = pd.read_csv(file_path1)\n", + "\n", + "# Display the first few rows of the dataset\n", + "print(df1.head())\n", + "\n", + "# Check for missing values\n", + "print(df1.isnull().sum())\n", + "\n", + "# Select only numeric columns for correlation heatmap\n", + "numeric_df1 = df1.select_dtypes(include=[np.number])\n", + "\n", + "# Define features and target variable\n", + "X = numeric_df1.drop(columns=['Profit ($)'])\n", + "y = numeric_df1['Profit ($)']\n", + "\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Create a column transformer for preprocessing\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('num', StandardScaler(), X.select_dtypes(include=['int64', 'float64']).columns),\n", + " ('cat', OneHotEncoder(handle_unknown='ignore'), X.select_dtypes(include=['object']).columns)\n", + " ]\n", + ")\n", + "\n", + "# Create a pipeline with preprocessing and model\n", + "pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', LinearRegression())\n", + "])\n", + "\n", + "# Train the model using the pipeline\n", + "pipeline.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = pipeline.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "mae = np.mean(np.abs(y_test - y_pred))\n", + "rmse = np.sqrt(mse)\n", + "print(f'Mean Squared Error: {mse}')\n", + "print(f'R-squared Score: {r2}')\n", + "print(f'Mean Absolute Error: {mae}')\n", + "print(f'Root Mean Squared Error: {rmse}')\n", + "\n", + "# Save the pipeline\n", + "filename = 'e_waste_pipeline.pkl'\n", + "with open(filename, 'wb') as file:\n", + " pickle.dump(pipeline, file)\n", + "\n", + "print(f\"Pipeline saved to {filename}\")\n", + "\n", + "# Load and use the pipeline\n", + "loaded_pipeline = pickle.load(open('e_waste_pipeline.pkl', 'rb'))\n", + "prediction = loaded_pipeline.predict(X_test)\n", + "print(f\"First prediction: {prediction[0]}\")\n", + "\n", + "# Load the YOLOv3 model\n", + "# Load the YOLOv3 model\n", + "net = cv2.dnn.readNet(\"yolov3.weights\", \"yolov3.cfg\")\n", + "\n", + "# Load the COCO dataset classes\n", + "classes = []\n", + "with open(\"coco.names\", \"r\") as f:\n", + " classes = [line.strip() for line in f.readlines()]\n", + "\n", + "# Function to get output layers\n", + "def get_output_layers(net):\n", + " layer_names = net.getLayerNames()\n", + " output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]\n", + " return output_layers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c97f803", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "datasetId": 5649116, + "sourceId": 9333976, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30761, + "isGpuEnabled": false, + "isInternetEnabled": false, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ML_Model/readme b/ML_Model/readme new file mode 100644 index 0000000..5377d90 --- /dev/null +++ b/ML_Model/readme @@ -0,0 +1,5 @@ +Kaggle link for dataset - +1)https://www.kaggle.com/datasets/gufranamu/waste-dataset/code?datasetId=5065184&sortBy=dateRun&tab=profile&excludeNonAccessedDatasources=false +2)https://www.kaggle.com/datasets/abhaynb/precious-metal-content-in-e-waste/code +b-n ml model is for biodegradable and non biodegradable waste distinguisher . +e-waste model is for understand price of electronic goods thrown away along with elements composition.