Solar Panel Od

--- license: mit language: - en tags: - object-detection - solar-panel - yolo - computer-vision - onnx - defect-detection library_name: ultralytics pipeline_tag: object-detection --- # Solar Panel Defect Detection YOLOv11-based object detection model for solar panel surface anomaly detection. Identifies 6 defect categories in RGB images. Trained on a custom labeled dataset using both RGB and thermal modalities; this repo contains the RGB variant. **GitHub:** [4keles/Solar-Panel-AI-Analysis](https://github.com/4keles/Solar-Panel-AI-Analysis) --- ## Classes | Class | Description | |-------|-------------| | `bird_drop` | Bird dropping contamination | | `bird_feather` | Feather debris on panel surface | | `physical_damage` | Cracks, chips, physical panel damage | | `dust_partical` | Dust and particle contamination | | `leaf` | Leaf debris | | `snow` | Snow coverage | --- ## Performance — v1.2.1 (test split) | Metric | Value | |--------|-------| | mAP@50 | **0.546** | | mAP@50-95 | 0.241 | | Precision | 0.569 | | Recall | 0.582 | | F1 | 0.575 | ### Per-Class Breakdown | Class | mAP@50 | Precision | Recall | |-------|--------|-----------|--------| | bird_feather | **0.995** | 0.832 | 1.000 | | leaf | 0.752 | 0.668 | 0.813 | | physical_damage | 0.552 | 0.543 | 0.565 | | snow | 0.467 | 0.567 | 0.494 | | dust_partical | 0.408 | 0.590 | 0.373 | | bird_drop | 0.100 | 0.214 | 0.246 | > `bird_drop` performance is low due to limited labeled samples in the dataset — planned improvement in v1.3. --- ## Model Versions | Version | Format | Size | Notes | |---------|--------|------|-------| | `v1.2.1/best.onnx` | ONNX | 37.9 MB | **Recommended** — CPU/GPU portable | | `v1.2.1/best.pt` | PyTorch | 6 MB | Fine-tuning / training | | `thermal-v1.0.4/best.onnx` | ONNX | 37.9 MB | Thermal camera variant | --- ## Usage ### Download (Python) ```python from huggingface_hub import hf_hub_download model_path = hf_hub_download( repo_id="4keles/solar-panel-od", filename="v1.2.1/best.onnx" ) ``` Or use the project download script: ```bash python scripts/download_model.py --version v1.2.1 ``` ### Inference ```python from ultralytics import YOLO model = YOLO("best.onnx", task="detect") results = model.predict("solar_panel.jpg", conf=0.25) results[0].show() ``` ### Class names (ordered) ```python CLASSES = ["bird_drop", "bird_feather", "physical_damage", "dust_p...