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Custom Image Classifier

Custom Image Classifier

Python PyTorch Flask License

Train custom image classification models without writing code

Quick Start • Features • Documentation • Contributing

Overview

A complete framework for building custom image classification models through an intuitive web interface. Perfect for students, researchers, and developers who need fast ML prototyping.

Key Capabilities:

  • Upload datasets and train models via web UI or CLI
  • Support for any number of classes
  • Automatic data augmentation and validation
  • REST API for integration
  • Real-time training monitoring

Quick Start

# Clone and install
git clone https://github.com/iad1tya/custom-image-classifier.git
cd custom-image-classifier
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Run
python app.py

Open http://localhost:5000 in your browser.

Features

Core Features

  • Web Interface - Upload datasets, train models, make predictions
  • CLI Tool - Command-line interface for automation (`cli.py`)
  • REST API - Integration endpoints for training and prediction
  • GPU Support - CUDA acceleration for faster training
  • Multiple Projects - Manage multiple models simultaneously

Technical Features

  • Custom CNN architecture optimized for image classification
  • Automatic data preprocessing and augmentation
  • Real-time training progress monitoring
  • Model checkpointing and versioning
  • Confidence score visualization

Usage

Web Interface

  1. Create Project - Click "New Project" and enter a name
  2. Upload Dataset - Organize images by class folders, upload as ZIP ``` my_dataset/ ├── class1/ │ ├── image1.jpg │ └── image2.jpg └── class2/ └── image1.jpg ```
  3. Train - Click "Train", configure parameters (epochs, batch size)
  4. Predict - Upload test images or use webcam

CLI

# List projects
python cli.py list

# Create project
python cli.py create my_project

# Train model
python cli.py train my_project --epochs 20

API

# Train model
curl -X POST http://localhost:5000/api/train \
  -H "Content-Type: application/json" \
  -d '{"project_name": "my_project", "epochs": 10}'

# Make prediction
curl -X POST http://localhost:5000/api/predict \
  -F "project_name=my_project" \
  -F "image=@test.jpg"

Architecture

Stack: Flask (backend) + PyTorch (ML) + OpenCV (vision)

Model: Custom CNN with 3 conv layers, max pooling, batch normalization, dropout (50%)

Project Structure:

├── app.py              # Flask application
├── cli.py              # Command-line interface
├── config.py           # Configuration
├── models/model.py     # CNN architecture
├── scripts/train_model.py
├── templates/          # Web UI
└── utils/predictor.py

Configuration

Edit `config.py`:

DEFAULT_EPOCHS = 10
DEFAULT_BATCH_SIZE = 32
DEFAULT_LEARNING_RATE = 0.001
IMAGE_SIZE = (128, 128)
USE_CUDA = True  # GPU acceleration

Documentation

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

# Setup development environment
pip install -r requirements-dev.txt
pre-commit install

# Run tests
pytest

License

MIT License - see LICENSE for details.

Acknowledgments

Built with PyTorch, Flask, and OpenCV.