---
language: en
tags:
- image-classification
- CNN
- Convolution Neural Entwork
- Nueral Network
- Trash
metrics:
- name: train-accuracy
  value: 91%
- name: test-accuracy
  value: 55%
pipeline:
  - image-classification
libraries:
  - name: torch
    version: 1.9.0
  - name: torchvision
    version: 0.10.0
  - name: numpy
    version: 1.21.0
---


## Trash Classification CNN Model

### About

This project is a convolutional neural network (CNN) model developed for the purpose of classifying different types of trash items.

The CNN model in this project utilizes the TinyVGG architecture, a compact version of the popular VGG neural network architecture. The model is trained to classify trash items into the following subcategories:

- Cardboard
- Food Organics
- Glass
- Metal
- Miscellaneous Trash
- Paper
- Plastic
- Textile Trash
- Vegetation

In total, there are 9 categories into which the trash items are classified.

For more details about the CNN architecture used in this project, you can refer to the [CNN Explainer](https://poloclub.github.io/cnn-explainer/) website.

### Info

Only 30% of the data from the Real Trash Dataset has been used and divided into an 80%-20% split of Train and Test.

The Huggingface Repository contains 7 files found in the `files and versions` tab:

1. **data_setup.py**: This file contains functions for setting up the data into datasets using ImageFolder and then turning it into batches using DataLoader. It also returns the names of the classes.

2. **model_builder.py**: This file contains a class which subclasses nn.Module and replicates the TinyVGG CNN model architecture with a few modifications here and there.

3. **engine.py**: This file contains three functions: `train_step`, `test_step`, and `train`. The previous two are used to train and test the model, respectively, and the last one integrates both to train the model.

4. **plotting.py**: This file contains functions to plot metrics like loss and accuracy using `plot_metrics`, and it also has a function `plot_confusion_Matrix` to plot the confusion matrix.

5. **predict.py**: This file can be run with `--image` and `--model_path` arguments to get the prediction of the model on the specified image path.

6. **utils.py**: This file contains functions to save the model in a specific folder with a changeable name.

7. **train.py**: This script uses all the files except `predict.py` and can take argument flags to change hyperparameters. It can be run with the following arguments:

    ```
    python train.py --train_dir TRAIN_DIR --test_dir TEST_DIR --learning_rate LEARNING_RATE --batch_size BATCH_SIZE --num_epochs NUM_EPOCHS
    ```

    Additionally, it is device agnostic, meaning it automatically utilizes available resources regardless of the specific device used.

Additionally, the repository contains 2 folders:

- **data**: This stores the data and has subdirectories train and test.

- **models**: This stores the model saved by utils.py.

- **samples**: This has 10 pictures, you can use for testing the model using `predict.py`.


## Model Overview

This model is designed for image classification tasks. It requires input images of size 112x112 pixels. Containing 2 blocks with 2 convulutional layers and then a flattner with a classfier.

The architecture looks like :
```python
TrashClassificationCNNModel(
  (block_1): Sequential(
    (0): Conv2d(3, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(15, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (block_2): Sequential(
    (0): Conv2d(15, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(15, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=11760, out_features=9, bias=True)
  )
)
```

## Dataset Overview

The dataset used containes images of multiple waste items with multiple classes named RealWaste. It has 4752 samples.

  - Source: [Click here](https://archive.ics.uci.edu/dataset/908/realwaste)
  - Citation: Single,Sam, Iranmanesh,Saeid, and Raad,Raad. (2023). RealWaste. UCI Machine Learning Repository. https://doi.org/10.24432/C5SS4G.

## Discliamer
The model mught give inaccurate or wrong results.