{"id":29284903,"url":"https://github.com/aryakoureshi/shallow-cnn-image-classification","last_synced_at":"2026-05-07T01:08:37.154Z","repository":{"id":302863626,"uuid":"1013754023","full_name":"AryaKoureshi/shallow-cnn-image-classification","owner":"AryaKoureshi","description":"This repository contains a Jupyter notebook implementing the shallow CNN architecture described in Shallow_CNN_for_Image_Classification.pdf. 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I demonstrate its performance on MNIST, Fashion‑MNIST, and CIFAR‑10 datasets.\n\n---\n\n## 💻 Environment \u0026 Installation\n\n1. **Clone this repo**\n   ```bash\n   git clone https://github.com/AryaKoureshi/shallow-cnn-image-classification.git\n   cd shallow-cnn-image-classification\n   ```\n\n2. **Create a virtual environment**\n\n   ```bash\n   python3 -m venv venv\n   source venv/bin/activate     # On Windows: venv\\Scripts\\activate\n   ```\n\n3. **Install dependencies**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n---\n\n## 📝 Notebook Overview\n\nThe notebook (`Shallow_CNN_for_Image_Classification.ipynb`) contains:\n\n1. **Data Loading \u0026 Preprocessing**\n\n   * MNIST, Fashion‑MNIST, CIFAR‑10\n   * Normalization, one‑hot encoding, optional resizing\n\n2. **Model Definition**\n\n   * Shallow CNN: 2×Conv2D → MaxPooling → Flatten → Dense\n   * BatchNormalization \u0026 Dropout for regularization\n   * SGD optimizer\n\n3. **Training \u0026 Evaluation**\n\n   * Trained for 10–20 epochs\n   * Plots of training vs. validation accuracy \u0026 loss\n   * Final test accuracy on each dataset\n\n4. **Results Summary**\n\n   * **MNIST**: \\~98% test accuracy\n   * **Fashion‑MNIST**: \\~91% test accuracy\n   * **CIFAR‑10**: \\~60% test accuracy\n\n   (See the notebook’s final cells for precise numbers and graphs.)\n\n---\n\n## 📈 Results\n\nThe notebook produces:\n\n* **Accuracy curves** for training vs. validation\n* **Loss curves** for training vs. validation\n* **Bar chart** comparing final test accuracies across datasets\n\n---\n\n## 🛠️ How to Run\n\n1. Launch Jupyter:\n\n   ```bash\n   jupyter lab\n   ```\n2. Open `Shallow_CNN_for_Image_Classification.ipynb`.\n3. Run all cells sequentially.\n\nAll figures and final metrics will appear in‑notebook.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryakoureshi%2Fshallow-cnn-image-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryakoureshi%2Fshallow-cnn-image-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryakoureshi%2Fshallow-cnn-image-classification/lists"}