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Zerodefect-1.0
prath0029/Zerodefect-1.0
Zerodefect-1.0 is a object detection model from prath0029. Use it when you need objects located in an image. The card lists the license as mit.
A YOLOv5 Nano object detection model trained to identify surface defects on aircraft skin. Optimized for edge deployment on the Luckfox Pico Max (RV1106 SoC, 0.5 TOPS NPU).
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Public
Updated Jul 17, 2026
Repo size
17.9 MB
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.onnx7.5 MB · 39%
At a glance
- Task
- Object Detection
- License
- mit
- Access
- Public
- Created
- Jul 17, 2026
- Updated
- Jul 17, 2026
- SHA
- 2a331f07
Model card
From the Hugging Face model README
ZeroDefect YOLOv5 Nano — v1.0
Aircraft Skin Defect Detection (Edge-Deployable)
A YOLOv5 Nano object detection model trained to identify surface defects on aircraft skin. Optimized for edge deployment on the Luckfox Pico Max (RV1106 SoC, 0.5 TOPS NPU).
Model Formats
| Format | File | Size | Use Case |
|---|---|---|---|
| PyTorch | best.pt | ~3.8 MB | Training, fine-tuning, desktop inference |
| ONNX | best.onnx | ~7.2 MB | Cross-platform CPU/GPU inference |
| RKNN | best.rknn | ~2.6 MB | RV1106 NPU hardware-accelerated inference |
Classes (7)
| ID | Class | Description |
|---|---|---|
| 0 | crack | Structural cracks, fatigue lines |
| 1 | dent | Impact dents, surface deformations |
| 2 | corrosion | Rust, chemical wear, oxidation |
| 3 | scratch | Superficial scrapes, paint scratches |
| 4 | paint-peel | Flaking paint, coating degradation |
| 5 | missing-head | Missing rivet heads or fasteners |
| 6 | defect | Generic surface defect anomalies |
Evaluation Results
Tested on 2,139 validation images (2,215 with detect.py)
| Class | Precision | Recall | [email protected] | [email protected]:0.95 |
|---|---|---|---|---|
| all | 0.435 | 0.355 | 0.323 | 0.238 |
| missing-head | 0.404 | 0.640 | 0.528 | 0.350 |
| dent | 0.576 | 0.448 | 0.447 | 0.372 |
| scratch | 0.509 | 0.397 | 0.363 | 0.356 |
| paint-peel | 0.395 | 0.312 | 0.312 | 0.250 |
| crack | 0.483 | 0.262 | 0.283 | 0.220 |
| corrosion | 0.308 | 0.227 | 0.172 | 0.072 |
| defect | 0.374 | 0.199 | 0.154 | 0.044 |
Inference Speed: 2.8 ms/image (RTX 3050, batch 16) | 7.8 ms/image (single image)
Confusion Matrix
Precision-Recall Curve
F1-Confidence Curve
Sample Detections
See the sample_detections/ folder for annotated detection results on validation images.
Training Details
- Architecture: YOLOv5 Nano (1.77M parameters, 4.2 GFLOPs)
- Epochs: 150
- Image Size: 640x640
- Batch Size: 16
- Dataset: 22k+ train / 2.2k+ val images merged from 36 COCO-format Roboflow datasets
- GPU: NVIDIA RTX 3050 6GB Laptop
- Optimizer: SGD with cosine LR schedule
- Augmentation: Standard YOLOv5 mosaic, mixup, HSV jitter
Quick Start: ONNX Inference
pip install onnxruntime opencv-python numpy
python infer_onnx.py --model best.onnx --image test.jpg --conf 0.25
Hardware Deployment (RV1106 NPU)
# Push to board
adb push best.rknn /userdata/
# Run on board (after cross-compiling rknn_model_zoo demo)
./rknn_yolov5_demo /userdata/best.rknn test.jpg
License
MIT License
- Task
- Object Detection
- License
- mit
- Created
- Jul 17, 2026
- Updated
- Jul 17, 2026