Human Action Classification: Reproducible Research Baselines

Hey r/MachineLearning! I built reproducible baselines for human action recognition that I wish existed when I started.

🎯 What This Is

Not an attempt to beat or compare with SOTA. This is a reference baseline for research and development. Most repos I found are unmaintained with irreproducible results, with no pretrained models. This repo provides:

• ✅ Reproducible training pipeline
• ✅ Pretrained models on HuggingFace
• ✅ Complete documentation
• ✅ Two approaches: Video (temporal) + Image (pose-based)

📊 Results

Video Models (UCF-101 – 101 classes):

• MC3-18: 87.05% accuracy (published: 85.0%)
• R3-18: 83.80% accuracy (published: 82.8%)

Image Models (Stanford40 – 40 classes):

• ResNet50: 88.5% accuracy
• Real-time: 90 FPS with pose estimation

🎬 emo (Created using test samples)

https://i.redd.it/diopygguk72g1.gif

🔗 Links

• GitHub: https://github.com/dronefreak/human-action-classification
• HuggingFace Models:
  • MC3-18: https://huggingface.co/dronefreak/mc3-18-ucf101
  • R3-18: https://huggingface.co/dronefreak/r3d-18-ucf101
  • Stanford40 Models: https://huggingface.co/dronefreak/human-action-classification-stanford40

💡 Why I Built This

Every video classification paper cites UCF-101, but finding working code is painful:

• Repos abandoned 3+ years ago
• Tensorflow 1.x dependencies
• Missing training scripts
• No pretrained weights

This repo is what I needed: a clean starting point with modern PyTorch, complete training code, and published pre-trained models.

🤝 Contributions Welcome

Looking for help with:

• Additional datasets (Kinetics, AVA, etc.)
• Two-stream fusion models
• Mobile deployment guides
• Better augmentation strategies

License: Apache 2.0 – use it however you want!

Happy to answer questions!