Fruits Detection using CNN.
Dataset used :
A dataset of images containing fruits and vegetables
- Total number of images: 82213.
- Training set size: 61488 images (one fruit or vegetable per image).
- Test set size: 20622 images (one fruit or vegetable per image).
- Multi-fruits set size: 103 images (more than one fruit (or fruit class) per image)
- Number of classes: 120 (fruits and vegetables).
- Image size: 100x100 pixels.
The Dataset Can be found over : https://www.kaggle.com/moltean/fruits and https://github.com/Horea94/Fruit-Images-Dataset
This is the work of Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018.
The paper introduces the dataset and an implementation of a Neural Network trained to recognized the fruits in the dataset.
The requirements.txt file, has all the packages that were in the environment at the time of training.
- Tensorflow 2.0 (Tensorflow-GPU was used)
- Keras 2.3.1
- Matplotlib
- Numpy
The Images to be predicted are put under the fruits/test_images folder.
This model is pretrained with and weights is a H5py file. Named 'Fruits_360.h5'.
The fruits.py file contains the Network Model and was used to train it.