---
license: apache-2.0
language:
  - en
  - my
tags:
  - image-classification
  - wildlife
  - biodiversity
  - myanmar
  - snake
  - yolo
  - ultralytics
  - computer-vision
  - conservation
metrics:
  - accuracy
model-index:
  - name: MM-DeepSnake-v2
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: MM Snakes Raw Images (Private)
          type: custom
        metrics:
          - type: accuracy
            value: 0.9314
            name: Top-1 Accuracy
          - type: accuracy
            value: 0.9974
            name: Top-5 Accuracy
---

# 🐍 MM-DeepSnake v2 — Myanmar Snake Species Classifier

[![Top-1 Accuracy](https://img.shields.io/badge/Top--1%20Accuracy-93.1%25-brightgreen)](https://mmdeepsnake.vercel.app)
[![Top-5 Accuracy](https://img.shields.io/badge/Top--5%20Accuracy-99.7%25-brightgreen)](https://mmdeepsnake.vercel.app)
[![Classes](https://img.shields.io/badge/Classes-10%20Species-blue)](https://mmdeepsnake.vercel.app)
[![Framework](https://img.shields.io/badge/Framework-Ultralytics%20YOLO-red)](https://ultralytics.com)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](LICENSE)
[![Website](https://img.shields.io/badge/Website-mmdeepsnake.vercel.app-informational)](https://mmdeepsnake.vercel.app)

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](https://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။

---

## 🔍 Model Details

| Property | Details |
|---|---|
| **Model Type** | Image Classification |
| **Architecture** | YOLOv2m-cls (`yolo26m-cls.pt`) |
| **Input Size** | 640 × 640 px |
| **Output Classes** | 10 Myanmar snake species |
| **Framework** | Ultralytics 8.4.15 (PyTorch) |
| **Parent Model** | `yolo26m-cls.pt` (ImageNet pretrained) |
| **Developed by** | [မင်းစည်သူ (Min Si Thu)](https://huggingface.co/jojo-ai-mst) |
| **Website** | [mmdeepsnake.vercel.app](https://mmdeepsnake.vercel.app) |
| **License** | Apache 2.0 |

---

## 📊 Performance

| Metric | Value |
|---|---|
| **Top-1 Accuracy** | **93.14%** |
| **Top-5 Accuracy** | **99.74%** |
| Training Loss (final) | 0.00994 |
| Epochs Trained | 100 |
| Training Time | 7m 23s |
| Compute  |  RTX PRO 6000 |

### Version Comparison

| Version | Architecture | Input Size | Top-1 Accuracy | Top-5 Accuracy |
|---|---|---|---|---|
| v1 | VGG19 (TensorFlow/Keras) | 300 × 300 | ~62% | — |
| **v2 (this model)** | **YOLOv2m-cls** | **640 × 640** | **93.14%** | **99.74%** |

---

## 🖼️ Sample Inference

![MM DeepSnake Inference Sample](https://huggingface.co/jojo-ai-mst/mm-deepsnake-v2/resolve/main/inference.png)

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

---

## 🐍 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](https://mmdeepsnake.vercel.app/pages/snakes.html)

---

## 🚀 How to Use

### Requirements

```bash
pip install ultralytics>=8.4.19
```

### Inference (Python)

```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)

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

### Batch Inference

```python
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])
```

---

## 🏋️ 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.

| Split | Images |
|---|---|
| Train | 1,300 |
| Validation | 201 |
| Test | 379 |
| **Total** | **~1,928** |

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

### Hyperparameters

| Parameter | Value |
|---|---|
| Epochs | 100 |
| Batch Size | 16 |
| Image Size | 640 |
| Optimizer | Auto (SGD) |
| Initial LR (`lr0`) | 0.01 |
| Final LR (`lrf`) | 0.01 |
| Momentum | 0.937 |
| AMP | ✅ Enabled |
| Dropout | 0.0 |
| Flip LR | 0.5 |
| Erasing | 0.4 |
| HSV-H / HSV-S / HSV-V | 0.015 / 0.7 / 0.4 |
| Close Mosaic | 10 |
| Pretrained | ✅ Yes (ImageNet) |

### For further Training Command

```bash
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

| Property | Details |
|---|---|
| GPU | NVIDIA RTX PRO 6000 |
| CPU | AMD EPYC 9655 (96-core) |
| Platform | Ultralytics Cloud (Docker) |
| OS | Linux 5.15.0 (Ubuntu, glibc 2.35) |
| Python | 3.11.14 |
| Ultralytics | 8.4.15 |

---

## ⚠️ 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.

---

## 🌏 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

---

## 📖 Citation

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

```bibtex
@misc{mm-deepsnake-v2-2026,
  author       = {မင်းစည်သူ (Min Si Thu)},
  title        = {MM-DeepSnake v2: Myanmar Snake Species Classification},
  year         = {2026},
  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\%}
}
```

---

## 🔗 Related Links

| Resource | Link |
|---|---|
| 🌐 Website | [mmdeepsnake.vercel.app](https://mmdeepsnake.vercel.app) |
| 🐍 Snake Species Info | [mmdeepsnake.vercel.app/pages/snakes.html](https://mmdeepsnake.vercel.app/pages/snakes.html) |
| ☠️ Venom Information | [mmdeepsnake.vercel.app/pages/venom.html](https://mmdeepsnake.vercel.app/pages/venom.html) |
| 📖 About the Project | [mmdeepsnake.vercel.app/about.html](https://mmdeepsnake.vercel.app/about.html) |
| 🤗 Author Profile | [huggingface.co/jojo-ai-mst](https://huggingface.co/jojo-ai-mst) |
| 🔧 Ultralytics Docs | [docs.ultralytics.com](https://docs.ultralytics.com) |

---

## 📄 License

This model is released under the **Apache 2.0 License**. See [LICENSE](LICENSE) for full terms.

The base model weights (`yolo26m-cls.pt`) are provided by [Ultralytics](https://ultralytics.com) under their respective license.

---

*Developed with ❤️ for Myanmar's wildlife and communities by [မင်းစည်သူ](https://huggingface.co/jojo-ai-mst)