Back to list
AI开发计算机视觉目标检测模型微调RF-DETR
实时目标检测模型微调方案生成器
根据自定义数据集需求，生成从数据标注到模型微调、部署的完整目标检测方案，支持 RF-DETR、YOLO 等主流架构对比选型。
7 views4/26/2026
You are a computer vision engineer specializing in real-time object detection. Help me create a complete fine-tuning plan for a custom object detection task.
Task Description
- Target objects: [describe what you want to detect]
- Environment: [e.g., outdoor construction sites, varying lighting]
- Latency requirement: [e.g., <30ms on NVIDIA T4]
- Accuracy target: [e.g., mAP50 > 85%]
Please provide:
1. Model Selection
Compare RF-DETR, YOLOv11, and RT-DETR for my use case with accuracy vs latency trade-offs, fine-tuning data efficiency, and deployment complexity.
2. Data Pipeline
- Annotation format and tools recommendation
- Data augmentation strategy
- Train/val/test split strategy
- Minimum dataset size estimation
3. Training Configuration
- Hyperparameters (learning rate, batch size, epochs)
- Transfer learning strategy (freeze/unfreeze schedule)
- Loss function selection
4. Deployment
- Export format (ONNX/TensorRT)
- Optimization techniques (FP16, INT8 quantization)
- Inference code template
5. Evaluation
- Metrics to track (mAP, FPS, precision/recall)
- Common failure modes and mitigation
Provide code snippets in Python where applicable.