TrashNet

Waste Segregation Project to classify waste into different classes.

DIRECT LINK

Kaggle Kernel Dataset: Trashnet

Categories
Cardboard
Glass
Paper
Metal
Trash

Just a Beginner!

First attempt: Building Tensorflow keras CNN Model

Notebook

• Training on GrayScale images
• Validation Accuracy 42%
• Loss function : Sparse Categorical Loss function
• Overfitting High

Second attempt:

Notebook

• Image Augmentation
• Training on Colored images
• Validation Accuracy 80%
• Loss function : Categorical Loss function
• Added 1 more 32 filters Convolution block with default stride
• 2 Dense layers with dropouts

Understood

• Image, Fit, Predict Generators, Flow from directory.
• Difference between SpatialDropout2D and Dropout Regularization.
• Number of filters and dense perceptrons to build a model.
• Callbacks : Early Stopping and Model Checkpoints to save perfect model on Hierarchical Data Format HDF5 (.h5)
• Visualizations by Matplotlib

Completion of Course on :

Udacity Intro To Tensorflow

Next Steps

1. Further Regularization -
   • Batch Normalization
   • L1 & L2 error
   • intialization of weights
2. Transfer learning -
   • MobileNet
     • Saving & Deploying of TFLite
   • VGGNet
   • ResNet
3. Collect Preprocess my own Training Dataset.
4. Object Detection Localization
   • YOLO v2/v3
5. Trash, Instance Segmentation

Understood Git Large File Storage

Large H5 Files