Classification of trash images using Convolutional Neural nets
1.Classification of trash for recyclability status
2.Smart trashnet : Waste localization
3.Classification of trashnet based on deeo learning models
4.A novel framework for trash classification using deep learning
Following models were mostly used for trash classification on Trashnet
- Densenet121
- ResNext-101
- Xception
- MobileNetV2
- Inception V4
- R-CNN
In this project i used a MobileNetV2 from tensorflow hub for classification
Data was augmented using computer vision python package to expand the dataset size , so that a better model could be trained Following augementation operations were done
- Rotation
- Vertical Flip
- Horizontal Flip
- Channel Shift
- Horizontal Shift
- Vertical Shift
In this project , using transfer learning i trained my augmented dataset, across all this networks, adding the a final fully connected layer for classification . The classification labels include
1.Trash
2.Plastic
3.Metal
4.Paper
5.Cardboard
Step 1. Clone the repository
Step 2: Ensure dependencies are installed as given in requirement.txt file or make a virtualenv
Step 3: To run the model for prediction , copy your image files to the prediction_image folder, and run
python predict.py
data_augment.py - Uses the images in dataset folder , augments them and saves them in the data_augmented folder
trash_classifier_MobileNetV2.ipynb - Builds the model , and saves it in the saved_models folder
predict.py - Runs the model, and prints the predictions of the model on the images stored in prediction_images folder
Complete Image Augmentation in OpenCV
Transfer learning with TensorFlow Hub