jojo-ai-mst / mm-deepsnake-v2

🐍 MM-DeepSnake v2 — Myanmar Snake Species Classifier

MM-DeepSnake is an AI-powered image classification model for identifying 10 snake species native to Myanmar . Built to support wildlife conservation, public safety, and biodiversity research, this model achieves 93.1% Top-1 accuracy and 99.7% Top-5 accuracy using a YOLOv2 medium classification backbone fine-tuned on a curated Myanmar snake image dataset.

🌐 Live Demo: mmdeepsnake.vercel.app

This is Version 2 — a major upgrade over the original VGG19-based v1 model (~62% val accuracy), delivering a +31% accuracy improvement .

မြန်မာနိုင်ငံတွင်တွေ့ရှိရသော မြွေမျိုးစိတ်များကို အမျိုးအစား ခွဲခြားရန်အတွက် ရေးဆွဲထားသော AI Project တစ်ခုဖြစ်သည်။

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🔍 Model Details

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📊 Performance

Version Comparison

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🖼️ Sample Inference

Sample predictions from MM-DeepSnake v2 on unseen test images.

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🐍 Supported Species (Classes)

The model classifies images into 10 snake species found across Myanmar, covering both venomous and non-venomous species common to the region. Visit the project website for the full species list with descriptions and venom information:

👉 mmdeepsnake.vercel.app/pages/snakes.html

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🚀 How to Use

Requirements

pip install ultralytics>=8.4.19

Inference (Python)

from ultralytics import YOLO

# Load model
model = YOLO("jojo-ai-mst/mm-deepsnake-v2")  # or local path to model.pt

# Run inference
results = model("path/to/snake_image.jpg")

# Print top prediction
for result in results:
    top1_class = result.names[result.probs.top1]
    top1_conf = result.probs.top1conf.item()
    print(f"Predicted species: {top1_class} ({top1_conf*100:.1f}% confidence)")

    # Top-5 predictions
    for idx in result.probs.top5:
        print(f"  {result.names[idx]}: {result.probs.data[idx]*100:.1f}%")

Inference (CLI)

yolo classify predict model=jojo-ai-mst/mm-deepsnake-v2 source=snake_image.jpg

Batch Inference

from ultralytics import YOLO

model = YOLO("jojo-ai-mst/mm-deepsnake-v2")

results = model("path/to/images/", stream=True)

for result in results:
    print(result.path, "→", result.names[result.probs.top1])

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🏋️ Training Details

Dataset

The model was trained on a private dataset of Myanmar snake images, collected from across the country and carefully labeled by species.

The training dataset is not publicly available to protect data integrity and prevent misuse.

Hyperparameters

For further Training Command

yolo train device=2 model=yolo26m-cls.pt \
  data=your-dataset-path \
  project=min-si-thu/mm-deepsnake \
  name=experiment-1 \
  epochs=100 batch=16 imgsz=640

⚠️ Requires ultralytics>=8.4.19

Hardware & Environment

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⚠️ Limitations & Biases

• Dataset size: ~1,928 images across 10 classes is relatively small. Performance may degrade on unusual lighting, rare poses, or juvenile specimens.
• Geographic scope: Trained exclusively on Myanmar snake species. This model is not suitable for identifying snake species from other regions.
• No rejection mechanism: The model has no out-of-distribution detection. Passing non-snake images may yield confident but incorrect predictions.
• Class imbalance: Some species may have fewer training samples, potentially resulting in lower per-class accuracy for underrepresented species.
• Not a medical tool: This system is intended to assist — not replace — professional identification, especially in snakebite emergency contexts.

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🌏 Intended Use

Recommended uses:

• Wildlife research and biodiversity surveys in Myanmar
• Public snake safety education and awareness
• Field identification assistance for researchers and conservationists
• Citizen science platforms and mobile applications

Out-of-scope uses:

• Medical diagnosis or snakebite treatment decisions
• Identification of non-Myanmar snake species
• Real-time safety-critical systems without human expert oversight

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📖 Citation

If you use this model in your research or applications, please cite:

@misc{mm-deepsnake-v2-2026,
  author       = {မင်းစည်သူ (Min Si Thu)},
  title        = {MM-DeepSnake v2: Myanmar Snake Species Classification},
  year         = {2026},
  organization = {AhinsaAI},
  url          = {https://huggingface.co/jojo-ai-mst/mm-deepsnake-v2},
  note         = {YOLOv2m-cls fine-tuned on private MM Snakes dataset, Top-1 Acc: 93.1\%}
}

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🔗 Related Links

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📄 License

This model is released under the Apache 2.0 License . See LICENSE for full terms.

The base model weights ( yolo26m-cls.pt ) are provided by Ultralytics under their respective license.

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Developed with ❤️ for Myanmar's wildlife and communities by မင်းစည်သူ @ AhinsaAI