RF-DETR Files

🚀 Added

• RFDETR.predict(shape=...) — pass an explicit (height, width) tuple to run inference at a non-square resolution, matching the resolution used when exporting the model. Both dimensions must be positive integers divisible by 14. (#866)

  python detections = model.predict("image.jpg", shape=(480, 640))

🌱 Changed

• ModelConfig.device and RFDETR.train(device=...) now accept torch.device objects and indexed device strings ("cuda:0", "cuda:1"). Existing string values ("cpu", "cuda") are unchanged. RFDETR.train() warns when a valid but unmapped device type is passed to PyTorch Lightning auto-detection. (#872)

  ```python from rfdetr import RFDETRSmall from torch import device

  model = RFDETRSmall(...)

  model.train(..., device=device("cuda:1")) model.train(..., device="cuda:0") ```

🔧 Fixed

• Fixed ONNX export ignoring an explicit patch_size argument: export() and predict() now resolve patch_size from model_config by default, validate it strictly (must be a positive integer, not bool), and enforce that (H, W) dimensions are divisible by patch_size × num_windows. (#876)
• Fixed ONNX export for models traced with dynamic batch dimensions — torch.full is now used for Python-int spatial dims to avoid H_.expand(N_) tracer failures. (#871)

🏆 Contributors

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

• zhaoshuo (@zhaoshuo1223) — ONNX export shape validation and patch_size fixes
• Sven Goluza (@svengoluza) — ONNX export dynamic batch fix
• Jirka Borovec (@Borda) (LinkedIn) — shape inference, torch.device support, release coordination

Full changelog: https://github.com/roboflow/rf-detr/compare/1.6.1...1.6.2