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solar-panel-od
4keles/solar-panel-od
solar-panel-od is a object detection model from 4keles. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as mit.
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 contai…
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Repo size
171 MB
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Repo makeup
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.onnx114 MB · 66%
At a glance
- Task
- Object Detection
- Library
- ultralytics
- License
- mit
- Access
- Public
- Created
- Jun 21, 2026
- Updated
- Jun 21, 2026
- SHA
- 52ab71a2
Model card
From the Hugging Face model README
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
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_dropperformance 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)
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:
python scripts/download_model.py --version v1.2.1
Inference
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)
CLASSES = ["bird_drop", "bird_feather", "physical_damage", "dust_partical", "leaf", "snow"]
Hardware
Trained on NVIDIA GeForce RTX 3050 Laptop GPU (4 GB VRAM). ONNX export runs on CPU or any CUDA device without recompilation.
License
MIT
- Task
- Object Detection
- Library
- ultralytics
- License
- mit
- Languages
- en
- Created
- Jun 21, 2026
- Updated
- Jun 21, 2026