Industrial QC requires high accuracy + high robustness, and YOLOv12's customization capabilities can meet the demand:
1. Data preparation phase::
- Use LabelImg to label defective samples, noting the diversity of light conditions
- Data enhancement focuses on the use of ColorJitter and MotionBlur.
- Dividing the training/validation/testing set by 8:1:1
2. Migration learning configuration::
- Load pre-training weights: model = YOLO('yolov12m.pt')
- Freeze the first 20 layers of the backbone network: freeze=[0,19]
- Set initial lr = 0.001 and decay by cosine
3. Key training techniques::
- Enable -rect rectangle training to reduce padding
- Add Focal Loss to address sample imbalance
- Preventing overfitting by stopping early (PATIENCE=50)
4. Deployment options::
- Deployment to production line industrial controllers with ONNX-TensorRT
- Developing Django/Flask Visualization Interface
- Integrated PLC communication module triggers sorting actions
Typical implementations show that 3,000张样本训练后mAP@0.5可达92.3% takes ≤15ms to detect a single piece.
This answer comes from the articleYOLOv12: Open source tool for real-time image and video target detectionThe
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