Zerodefect 1.0 Bucket
A YOLOv5 Nano object detection model trained on a class-balanced merged dataset of aircraft skin and automotive component defects. This is version 2.0 of the ZeroDefect model, trained with:
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 6,83–7,17 MB
- Versão
- vBest balanced
- Atualizado
- 10 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
ZeroDefect — YOLOv5 Nano (v2.0 Class-Balanced)
Aircraft Skin & Automotive Component Defect Detection (Edge-Deployable)
A YOLOv5 Nano object detection model trained on a class-balanced merged dataset of aircraft skin and automotive component defects. This is version 2.0 of the ZeroDefect model, trained with:
- AdamW optimizer + Cosine LR decay for stable convergence
- Class-balanced sampling across 7 defect categories
- Multi-scale training (random image size between 0.5x–1.5x)
- Label smoothing (0.05) for better generalization
- 150 epochs fine-tuning from a high-quality pretrained base
Detected Defect Categories (7 Classes)
| ID | Class | Description |
|---|---|---|
| 0 | crack | Structural cracks, fatigue lines, stress fractures |
| 1 | dent | Impact dents, surface deformations |
| 2 | corrosion | Rust, chemical wear, oxidation spots |
| 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 anomalies |
Training & Validation Metrics
| Run | Epochs | [email protected] | [email protected]:0.95 | Precision | Recall |
|---|---|---|---|---|---|
| Balanced v2.0 (this repo, best epoch=101) | 150 | 0.30994 | 0.22613 | 0.37777 | 0.34799 |
| Nano v1.0 (baseline, epoch 149) | 150 | 0.31962 | 0.23492 | 0.42944 | 0.35807 |
Note: The class-balanced v2.0 run achieves comparable mAP scores to the nano v1.0 baseline while training on a harder, class-balanced dataset. The lower absolute mAP reflects that the dataset had its class distribution enforced (harder to overfit to the dominant "defect" class), resulting in more balanced per-class detection capability.
Training Configuration
- Model Architecture: YOLOv5 Nano backbone (~1.76M parameters, 4.2 GFLOPs)
- Dataset: Merged aircraft skin defect + corrosion dataset (~30k images, 7 classes)
- Optimizer: AdamW | Cosine LR decay
- Label Smoothing: 0.05
- Early Stopping: patience=30 (best epoch: 101/150)
- Hardware: NVIDIA RTX 3050 Laptop GPU (6 GB VRAM)
Repository Contents
| File | Description | Size |
|---|---|---|
best.pt | PyTorch model weights (YOLOv5 format) | ~3.8 MB |
best_balanced.onnx | ONNX export (opset 18, simplified) | ~7.5 MB |
best.rknn | RKNN binary for RV1106 NPU (INT8, prior run) | ~2.6 MB |
data.yaml | Dataset configuration | <1 KB |
infer_onnx.py | Python ONNX inference helper | ~8 KB |
evaluation/ | Training curves, confusion matrix, PR/F1 plots | — |
Quick Start: ONNX Inference (Python)
pip install onnxruntime opencv-python numpy
python infer_onnx.py --model best_balanced.onnx --image test.jpg --conf 0.25
Hardware Deployment (Luckfox Pico Max / RV1106 NPU)
The best.rknn model is compiled for the RV1106 NPU (INT8 quantized):
# Push model to the board
adb push best.rknn /userdata/
# Build C++ inference runner from rknn_model_zoo
./build-linux.sh -t rv1106 -a armv7l -d yolov5
# Run inference on board
./rknn_yolov5_demo /userdata/best.rknn test.jpg
License
Released under the MIT License. See LICENSE for details.
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Trechos de fonte
2 trechos--- license: mit tags: - object-detection - pytorch - yolov5 - onnx - rknn - rv1106 - luckfox - aircraft-inspection - defect-detection - edge-ai - class-balanced datasets: - custom metrics: - precision - recall - map model-index: - name: Zerodefect-1.0-bucket results: - task: type: object-detection name: Object Detection dataset: type: custom name: Merged Aircraft Skin Defect & Corrosion Dataset (Class-Balanced) metrics: - type: precision value: 0.3778 name: Precision - type: recall value: 0.3480 name: Recall - type: map value: 0.30994 name: [email protected] - type: map value: 0.22613 name: [email protected]:0.95 --- # ZeroDefect — YOLOv5 Nano (v2.0 Class-Balanced) ### Aircraft Skin & Automotive Component Defect Detection (Edge-Deployable) [](https://python.org) [](https://pytorch.org) [](https://onnx.ai) [](LICENSE) A YOLOv5 Nano object detection model trained on a **class-balanced merged dataset** of aircraft skin and automotive component defects. This is version 2.0 of the ZeroDefect model, trained with: - **AdamW optimizer** + **Cosine LR decay** for stable convergence - **Class-balanced sampling** across 7 defect categories - **Multi-scale training** (random image size between 0.5x–1.5x) - **Label smoothing** (0.05) for better generalization - **150 epochs** fine-tuning from a high-quality pretrained base ## Detected Defect Categories (7 Classes) | ID | Class | Description | |----|-------|-------------| | 0 | `crack` | Structural cracks, fatigue lines, stress fractures | | 1 | `dent` | Impact dents, surface deformations | | 2 | `corrosion` | Rust, chemical wear, oxidation spots | | 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 anomalies | --- ## Training & Validation Metrics | Run | Epochs | [email protected] | [email protected]:0.95 | Precision | Recall | |-----|--------|---------|--------------|-----------|--------| | **Balanced v2.0** (this repo, best epoch=101) | 150 | **0.30994** | **0.22613** | 0.37777 | 0.347...
Source context: 0 downloads · 0 likes · Pipeline object-detection · Repo prath0029/Zerodefect-1.0-bucket
