1.6.4

## 🌱 Changed

- **Class names on predictions.** `predict()` now includes `class_name` in the returned `detections.data` dict, mapping each detection's 0-indexed class ID to its human-readable name. No more manual lookups. (#914)

    ```python
    model = RFDETRSmall(pretrain_weights="path/to/fine_tuned.pth")
    detections = model.predict("image.jpg", threshold=0.5)
    print(detections.data["class_name"])  # ["cat", "dog", "cat"]
    ```

## 🔧 Fixed

- Fixed segmentation training crashing on multi-GPU DDP setups. The segmentation head leaves some parameters unused on certain forward steps, which triggered `RuntimeError: parameters that were not used in producing the loss`. `build_trainer()` now automatically enables `find_unused_parameters=True` when `segmentation_head=True`. (#947)

- Fixed fused AdamW optimizer crash during FP32 multi-GPU training. On Ampere+ GPUs, fused AdamW was enabled whenever the hardware supported BF16 — even when the trainer was explicitly configured for `precision="32-true"`. This caused a dtype mismatch in DDP gradient buckets. The optimizer now checks the trainer's actual precision setting, not just GPU capability. (#947)

- Fixed multi-GPU DDP training failing in Jupyter notebooks and Kaggle. Fork-based DDP corrupted PyTorch's OpenMP thread pool, causing `SIGABRT` on the second process. RF-DETR now uses a spawn-based DDP strategy in interactive environments, avoiding the thread pool issue entirely. (#928)

- Fixed `RFDETR.train(resolution=...)` being silently ignored. The `resolution` kwarg is a model-level setting, not a training config field, so it was quietly dropped. It is now applied to the model config before training begins, with validation that the value is divisible by `patch_size * num_windows`. (#933)

    ```python
    model = RFDETRSmall()
    model.train(dataset_dir="./dataset", resolution=768)  # now works
    ```

- Fixed `save_dataset_grids` being silently a no-op. The grid saver was never wired into the training loop. Dataset sample grids are now saved to `{output_dir}/dataset_grids/` when enabled. Grid save failures are caught and logged without interrupting training. (#946)

- Fixed partial gradient-accumulation windows at the end of training epochs. When the dataset length was not evenly divisible by `effective_batch_size * world_size`, PyTorch Lightning would fire the optimizer on an incomplete accumulation window. The training dataset is now padded to an exact multiple, ensuring every optimizer step uses a full gradient window. (#937)

- Fixed `torch.export.export` failing on the transformer decoder. The `spatial_shapes_hw` parameter was not threaded through the decoder layers, breaking export for models using multi-scale deformable attention. (#936)

- Fixed `download_pretrain_weights()` silently overwriting fine-tuned checkpoints. When a fine-tuned checkpoint shared a filename with a registry model (e.g. `rf-detr-nano.pth`), an MD5 mismatch would trigger a re-download that replaced the user's weights. The function now returns early when the file exists and `redownload=False`, emitting a warning instead. (#935)

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## 🏆 Contributors

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

- **M. Fazri Nizar** (@mfazrinizar) ([LinkedIn](https://linkedin.com/in/mfazrinizar)) — *multi-GPU DDP training in notebooks*
- **Jiahao Sun** (@sjhddh) ([LinkedIn](https://www.linkedin.com/in/jiahao7sun/)) — *config type hint fix*
- **Jirka Borovec** (@Borda) ([LinkedIn](https://www.linkedin.com/in/jirka-borovec)) — *release coordination, reviews*

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

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**Full changelog**: https://github.com/roboflow/rf-detr/compare/1.6.3...1.6.4