RF-DETR: Real-Time Object Detection Model
RF-DETR is a state-of-the-art (SOTA) real-time object detection model architecture developed by Roboflow. It is designed for fine-tuning and achieves competitive performance on the Microsoft COCO benchmark, exceeding 60 AP. This model is small enough to run on edge devices, making it ideal for applications requiring both strong accuracy and real-time performance.
Key Features:
- Real-Time Performance: Achieves competitive speed comparable to current real-time object detection models.
- Fine-Tuning Capability: Designed for easy fine-tuning on custom datasets, enhancing adaptability to specific tasks.
- Multiple Variants: Available in two variants, RF-DETR-B (29M parameters) and RF-DETR-L (128M parameters), catering to different performance needs.
- Batch Inference: Supports batch processing for efficient inference on multiple images.
- Logging Support: Integrates with TensorBoard and Weights & Biases for tracking training metrics and performance.
- ONNX Export: Allows exporting models to ONNX format for improved deployment efficiency.
Benefits:
- High Accuracy: Validated performance on COCO and RF100-VL benchmarks ensures reliability in real-world applications.
- Edge Compatibility: Small model size enables deployment on edge devices, suitable for various applications.
- Community Support: Open-source with contributions welcomed, fostering a collaborative development environment.
Highlights:
- First real-time model to exceed 60 AP on COCO.
- Comprehensive documentation and examples for easy implementation.
- Active development with regular updates and community engagement.