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places365

Places365

Input

input_image

from https://github.com/CSAILVision/places365

Ailia input shape: (1, 3, 224, 224)

Output

If model type is resnet18, resnet50 or alexnet, result is as below.

--SCENE CATEGORIES:
	0.616 -> patio
	0.314 -> restaurant_patio
	0.020 -> beer_garden
	0.011 -> courtyard
	0.011 -> porch

If model type is wideresnet18, result is as below and class activation map is generated.

--TYPE OF ENVIRONMENT:
	outdoor
--SCENE CATEGORIES:
	0.651 -> patio
	0.068 -> restaurant_patio
	0.043 -> porch
	0.026 -> courtyard
	0.022 -> picnic_area
--SCENE ATTRIBUTES:
	 man-made, no horizon, natural light, open area, foliage, wood, vegetation, leaves, trees

Class activation map output_image

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 places365.py

If you want to specify the input image, put the image path after the --input option.

$ python3 places365.py --input IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 places365.py --video VIDEO_PATH

You can select a model from resnet18 | resnet50 | alexnet | wideresnet18 by adding --model option.

In the case of wideresnet18 model, you can use --savepath option to save the class activation map file.

(ex1)$ python3 places365.py --model wideresnet18 --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

(ex2)$ python3 places365.py --model wideresnet18 --video VIDEO_PATH --savepath SAVE_VIDEO_PATH

Assets

categories_places365.txt

IO_places365.txt

labels_sunattribute.txt

W_sceneattribute_wideresnet18.npy

Reference

Places: A 10 million Image Database for Scene Recognition

Release of Places365-CNNs

Framework

PyTorch 1.2.0

Model Format

ONNX opset = 11

Netron

resnet18_places365.onnx.prototxt

resnet50_places365.onnx.prototxt

alexnet_places365.onnx.prototxt

wideresnet18_places365.onnx.prototxt