https://github.com/aryakoureshi/shallow-cnn-image-classification

This repository contains a Jupyter notebook implementing the shallow CNN architecture described in Shallow_CNN_for_Image_Classification.pdf. I demonstrate its performance on MNIST, Fashion‑MNIST, and CIFAR‑10 datasets.
https://github.com/aryakoureshi/shallow-cnn-image-classification

computer-vision deep-learning image-classification keras machine-learning scnn tensorflow

Last synced: 2 months ago
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This repository contains a Jupyter notebook implementing the shallow CNN architecture described in Shallow_CNN_for_Image_Classification.pdf. I demonstrate its performance on MNIST, Fashion‑MNIST, and CIFAR‑10 datasets.

• Host: GitHub
• URL: https://github.com/aryakoureshi/shallow-cnn-image-classification
• Owner: AryaKoureshi
• License: mit
• Created: 2025-07-04T12:10:38.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-07-04T12:25:42.000Z (about 1 year ago)
• Last Synced: 2025-07-04T13:59:55.936Z (about 1 year ago)
• Topics: computer-vision, deep-learning, image-classification, keras, machine-learning, scnn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.2 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# shallow-cnn-image-classification

This repository contains a Jupyter notebook implementing the shallow CNN architecture described in “[Shallow Convolutional Neural Network for Image Classification](paper/Shallow_convolutional_neural_network_for_image_classifcation.pdf)”. I demonstrate its performance on MNIST, Fashion‑MNIST, and CIFAR‑10 datasets.

---

## 💻 Environment & Installation

1. **Clone this repo**

   ```bash

   git clone https://github.com/AryaKoureshi/shallow-cnn-image-classification.git

   cd shallow-cnn-image-classification

   ```

2. **Create a virtual environment**

   ```bash

   python3 -m venv venv

   source venv/bin/activate     # On Windows: venv\Scripts\activate

   ```

3. **Install dependencies**

   ```bash

   pip install -r requirements.txt

   ```

---

## 📝 Notebook Overview

The notebook (`Shallow_CNN_for_Image_Classification.ipynb`) contains:

1. **Data Loading & Preprocessing**

   * MNIST, Fashion‑MNIST, CIFAR‑10

   * Normalization, one‑hot encoding, optional resizing

2. **Model Definition**

   * Shallow CNN: 2×Conv2D → MaxPooling → Flatten → Dense

   * BatchNormalization & Dropout for regularization

   * SGD optimizer

3. **Training & Evaluation**

   * Trained for 10–20 epochs

   * Plots of training vs. validation accuracy & loss

   * Final test accuracy on each dataset

4. **Results Summary**

   * **MNIST**: \~98% test accuracy

   * **Fashion‑MNIST**: \~91% test accuracy

   * **CIFAR‑10**: \~60% test accuracy

   (See the notebook’s final cells for precise numbers and graphs.)

---

## 📈 Results

The notebook produces:

* **Accuracy curves** for training vs. validation

* **Loss curves** for training vs. validation

* **Bar chart** comparing final test accuracies across datasets

---

## 🛠️ How to Run

1. Launch Jupyter:

   ```bash

   jupyter lab

   ```

2. Open `Shallow_CNN_for_Image_Classification.ipynb`.

3. Run all cells sequentially.

All figures and final metrics will appear in‑notebook.