https://github.com/manohara-ai/tinyvgg_inspired_binary_classifier
This project implements a Convolutional Neural Network (CNN) binary classifier inspired by the TinyVGG architecture.
https://github.com/manohara-ai/tinyvgg_inspired_binary_classifier
binary-classifier cnn-classification machine-learning tinyvgg
Last synced: 10 days ago
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This project implements a Convolutional Neural Network (CNN) binary classifier inspired by the TinyVGG architecture.
- Host: GitHub
- URL: https://github.com/manohara-ai/tinyvgg_inspired_binary_classifier
- Owner: Manohara-Ai
- License: mit
- Created: 2024-09-11T18:33:43.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-08T10:40:44.000Z (5 months ago)
- Last Synced: 2025-03-31T02:41:19.269Z (about 2 months ago)
- Topics: binary-classifier, cnn-classification, machine-learning, tinyvgg
- Language: Python
- Homepage:
- Size: 23.4 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TinyVGG-inspired CNN Binary Classifier
This project implements a Convolutional Neural Network (CNN) binary classifier inspired by the TinyVGG architecture. The model is designed for binary classification tasks where the goal is to categorize input images into two classes.
## Table of Contents
- [Model Architecture](#model-architecture)
- [Requirements](#requirements)
- [Usage](#usage)
- [Training and Evaluation](#training-and-evaluation)
- [Contributor](#contributor)
## Model Architecture
The CNN model is based on the TinyVGG architecture with modifications for binary classification. The architecture consists of:
- Multiple convolutional layers followed by max-pooling.
- ReLU activation functions after each convolution.
- Fully connected layers at the end to output a binary classification decision.
**Key Features:**
- Lightweight model, suitable for small datasets.
- Two output nodes using softmax activation for binary classification.
### Architecture Overview:
```
Conv2D -> ReLU -> MaxPooling
Conv2D -> ReLU -> MaxPooling
Flatten -> Fully Connected -> ReLU
Fully Connected -> Output (Softmax for binary classification)
```
## Requirements
To run this project, the following packages are required:
- Python 3.x
- PyTorch
- OpenCV (for image preprocessing)
- NumPy
- Matplotlib (for visualizations)
- tqdm (for progress tracking)
You can install the required packages using `pip`:
```bash
pip install torch torchvision opencv-python numpy matplotlib tqdm
```
## Usage
1. **Data Preparation:**
- Place your training and validation images in respective folders: `datasets/train/` and `datasets/val/`, with subfolders for each class (e.g., `class0/`, `class1/`).
2. **Dataset:**
- For this project, you can use the dataset from Kaggle: [Binary Image Classification Dataset](https://www.kaggle.com/datasets/hasnainkhan0123/binary-image-classification)
## Training and Evaluation
To the train, evaluate and test the model, make necessary changes and run the main script.
- The script will output accuracy, loss, and other metrics for evaluation.
## Contributor
This project is developed by B M Manohara @Manohara-Ai
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