Paper Implementations

Clean implementations of 20 landmark CV papers: ResNet through DiT, each in its own module

paper-implementations deep-learning computer-vision pytorch research

Overview

This repository implements a complete pipeline for paper implementations, covering data preprocessing, model training, evaluation, and deployment.

Features

• Clean, modular PyTorch implementation
• Reproducible experiments with MLflow tracking
• Comprehensive evaluation with standard benchmarks
• ONNX export for production deployment
• Detailed documentation and usage examples

Installation

git clone https://github.com/YOUR_USERNAME/paper-implementations.git
cd paper-implementations
pip install -r requirements.txt

Quick Start

from src.model import Model
from src.trainer import Trainer
from src.config import Config

config = Config.from_yaml("configs/default.yaml")
model = Model(config)
trainer = Trainer(model, config)
trainer.train()

Project Structure

paper-implementations/
├── src/
│   ├── model.py        # Model architecture
│   ├── dataset.py      # Data loading and preprocessing
│   ├── trainer.py      # Training loop
│   ├── evaluate.py     # Evaluation metrics
│   └── utils.py        # Helper utilities
├── configs/
│   └── default.yaml    # Default configuration
├── notebooks/
│   └── exploration.ipynb
├── tests/
│   └── test_model.py
├── requirements.txt
└── README.md

Results

Model	Dataset	Metric	Score
Baseline	Standard	Primary	-
Ours	Standard	Primary	-

Usage

# Train
python train.py --config configs/default.yaml

# Evaluate
python evaluate.py --checkpoint checkpoints/best.pth

# Export to ONNX
python export.py --checkpoint checkpoints/best.pth

References

• Relevant papers and resources for paper implementations

License

MIT

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