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--- |
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language: en |
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tags: |
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- image-classification |
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- CNN |
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- Convolution Neural Entwork |
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- Nueral Network |
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- Trash |
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metrics: |
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- name: train-accuracy |
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value: 91% |
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- name: test-accuracy |
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value: 55% |
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pipeline: |
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- image-classification |
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libraries: |
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- name: torch |
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version: 1.9.0 |
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- name: torchvision |
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version: 0.10.0 |
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- name: numpy |
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version: 1.21.0 |
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--- |
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## Trash Classification CNN Model |
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### About |
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This project is a convolutional neural network (CNN) model developed for the purpose of classifying different types of trash items. |
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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: |
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- Cardboard |
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- Food Organics |
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- Glass |
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- Metal |
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- Miscellaneous Trash |
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- Paper |
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- Plastic |
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- Textile Trash |
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- Vegetation |
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In total, there are 9 categories into which the trash items are classified. |
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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. |
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### Info |
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Only 30% of the data from the Real Trash Dataset has been used and divided into an 80%-20% split of Train and Test. |
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The Huggingface Repository contains 7 files found in the `files and versions` tab: |
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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. |
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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. |
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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. |
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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. |
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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. |
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6. **utils.py**: This file contains functions to save the model in a specific folder with a changeable name. |
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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: |
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``` |
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python train.py --train_dir TRAIN_DIR --test_dir TEST_DIR --learning_rate LEARNING_RATE --batch_size BATCH_SIZE --num_epochs NUM_EPOCHS |
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``` |
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Additionally, it is device agnostic, meaning it automatically utilizes available resources regardless of the specific device used. |
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Additionally, the repository contains 2 folders: |
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- **data**: This stores the data and has subdirectories train and test. |
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- **models**: This stores the model saved by utils.py. |
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- **samples**: This has 10 pictures, you can use for testing the model using `predict.py`. |
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## Model Overview |
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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. |
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The architecture looks like : |
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```python |
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TrashClassificationCNNModel( |
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(block_1): Sequential( |
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(0): Conv2d(3, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(1): ReLU() |
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(2): Conv2d(15, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(3): ReLU() |
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(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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) |
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(block_2): Sequential( |
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(0): Conv2d(15, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(1): ReLU() |
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(2): Conv2d(15, 15, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(3): ReLU() |
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(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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) |
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(classifier): Sequential( |
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(0): Flatten(start_dim=1, end_dim=-1) |
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(1): Linear(in_features=11760, out_features=9, bias=True) |
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) |
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) |
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``` |
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## Dataset Overview |
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The dataset used containes images of multiple waste items with multiple classes named RealWaste. It has 4752 samples. |
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- Source: [Click here](https://archive.ics.uci.edu/dataset/908/realwaste) |
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- Citation: Single,Sam, Iranmanesh,Saeid, and Raad,Raad. (2023). RealWaste. UCI Machine Learning Repository. https://doi.org/10.24432/C5SS4G. |
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## Discliamer |
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The model mught give inaccurate or wrong results. |
