Real-Time Object Detection Model Fine-Tuning Plan Generator

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.