| --- |
| 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. |
