RF-DETR Files

🌱 Changed

• predict() returns source image and shape on detections. Returned sv.Detections objects now include detections.data["source_image"] (the original image as a NumPy array) and detections.data["source_shape"] (a (height, width) tuple), so you can annotate results without loading the image separately. (#892)

  python detections = model.predict("https://media.roboflow.com/dog.jpg", threshold=0.5) annotated = sv.BoxAnnotator().annotate(detections.data["source_image"], detections)

• RFDETR.train() auto-detects num_classes from the dataset. When num_classes is not explicitly set, RF-DETR reads the class count from the dataset directory and reinitializes the detection head automatically. A warning is emitted when your configured value differs from the dataset count. (#893)

  python model = RFDETRSmall() model.train(dataset_dir="./dataset") # num_classes inferred from dataset

• optimize_for_inference() accepts dtype as a string. Pass "float16" or "bfloat16" in addition to torch.float16; invalid inputs now raise TypeError uniformly. (#899)

🔧 Fixed

• Fixed fine-tuned models exporting wrong class counts to ONNX: reinitialize_detection_head now replaces nn.Linear modules instead of mutating tensor data in-place, keeping out_features consistent with the actual weight shape after fine-tuning. (#904)
• Fixed optimize_for_inference() leaking a CUDA context on multi-GPU setups — deep-copy, export, and JIT-trace now run inside the correct device context. Also fixed: state is rolled back cleanly if optimization fails mid-way, and temp download files now use unique per-process paths to prevent parallel worker collisions. (#899)
• Fixed deploy_to_roboflow raising FileNotFoundError after PyTorch Lightning migration — class_names.txt is now written to the upload directory and args.class_names is populated before saving the checkpoint, restoring uploads for all model types including segmentation. (#890)

🏆 Contributors

Welcome to our new contributors, and thank you to everyone who helped with this release:

• Md Faruk Alam (@farukalamai) (LinkedIn) — predict source image and shape
• Jirka Borovec (@Borda) (LinkedIn) — release coordination, reviews

Automated contributions: @copilot-swe-agent[bot], @pre-commit-ci[bot]

Full changelog: https://github.com/roboflow/rf-detr/compare/1.6.2...1.6.3