rf-detr

Explore RF-DETR model variants for object detection, segmentation, and keypoint detection. Discover seventeen powerful architectures to enhance your computer vision projects.

Learn about RF-DETR's safe loading mechanism to prevent code execution from malicious checkpoints. Discover when to use trust_checkpoint=True for secure model loading.

Discover how RF-DETR infers the model class from a checkpoint by examining hyper_parameters, model_cfg, and model_name for automatic reconstruction.

Learn to load a trained RF-DETR model from checkpoint using `load_from_checkpoint`. Effortlessly handle .ckpt and .pth files and interpolate embeddings for any resolution.

Easily export RF-DETR models to OpenVINO IR format with a single command no ONNX needed. Streamline your object detection workflow for edge devices.

Export your RF-DETR model to CoreML format with ease. Follow our guide to convert your model for Apple devices using simple API calls or CLI commands.

Easily export RF-DETR models to TFLite format using the export() method. Optimize edge deployment with INT8 or FP16 quantization. Get the complete guide now.

Easily export your RF-DETR model to TensorRT format. Learn how to convert trained detection models into optimized TensorRT engines using Roboflow's built-in pipeline.

Export RF-DETR models to ONNX format easily. Use the dedicated export pipeline with the export_onnx function for portable ONNX models. Get started now.

Master RF-DETR training hyperparameters with our guide to TrainConfig. Optimize learning rates, batch sizing, and EMA for peak model performance. Learn how to fine-tune your object detection models.

Explore RF-DETR model architecture parameters with this comprehensive guide. Understand and control hyperparameters for vision transformer backbones, decoders, attention, and heads.

Unlock efficient RF-DETR training with auto-batch. Effortlessly set batch_size='auto' to optimize micro-batch size and gradient accumulation for your GPU, maximizing performance.