A deep learning model capable of identifying various solid waste items such as glass, paper, cardboard, plastic, metal, and trash.
Trashnet: 2527 Images - 2274 Training (90%) & 253 Validation (10%)
- 501 Glass
- 594 Paper
- 403 Cardboard
- 482 Plastic
- 137 Trash
- 410 Metal
- Reorganize Dataset to have a training and validation directories with subdirectories containing each category
- Initalize the pretrained model
- Reshape the final layer(s) to have the same number of outputs as the number of classes in the new dataset (6)
- Define for the optimization algorithm which parameters we want to update during training
- Run the training step
*Can be reduced using more powerful GPUs, reducing its importance for now.
- Try with various architectures
- Save and export model for post-training evaluation to finetune hyperperameters