1.10.1

RF-DETR 1.10.1 is a patch release: `compile=True` now actually reaches CUDA's `torch.compile` path under the default `multi_scale=True` recipe, and peak CUDA memory in segmentation loss drops by sampling ground-truth masks one image at a time. It also carries several device-sync and strategy-selection fixes for the still-experimental, undocumented TPU/XLA training path (tracked under issue #1058) — see Notable Changes below. No breaking changes in this release.

## ✨ Spotlights

### ⚡ `compile=True` now actually compiles on CUDA with the default `multi_scale=True`

The compile gate previously excluded multi-scale training, but the exclusion sat behind checks that already required a CUDA device *and* CUDA accelerator — so it only ever blocked the CUDA path, where `dynamic=True` already handles the varying input size. Setting `compile=True` on a default recipe previously logged a notice and trained eagerly instead. ([#1436]; [#1411], shipped in 1.10.0, made compilation reachable in the first place)

```python
from rfdetr import RFDETRSmall

model = RFDETRSmall()
model.train(dataset_dir="...", compile=True)  # multi_scale=True by default — now actually compiles on CUDA
```

### 💾 Reduced peak CUDA memory in segmentation loss

For multi-image CUDA batches with boolean ground-truth masks, sampling now happens one image at a time, releasing each image's float masks before the next, instead of concatenating a batch-wide float mask tensor. Narrower inputs (single-image batches, non-CUDA, non-bool masks) keep the prior concat path unchanged. ([#1437])

## 🔄 Migration guide

No breaking changes in this release. One thing to know: if you call `dice_loss_jit` / `sigmoid_ce_loss_jit` directly — not part of the public API, only reachable through `rfdetr.models.lwdetr`'s backward-compat re-exports — most NumPy scalar denominators now need casting to `float` first. See [MIGRATION.md](./MIGRATION.md) for details.

## 📝 Notable changes

### 🔧 Fixed

- `compile=True` now takes effect on CUDA with the default `multi_scale=True`, instead of previously logging a notice and training eagerly. Also fixes the now-stale `multi_scale=False` claim in the COCO2017 training cookbook. (#1436; #1411, shipped in 1.10.0, made compilation reachable in the first place)
- Reduced peak CUDA memory in segmentation loss by sampling matched ground-truth masks one image at a time, for multi-image CUDA batches with boolean masks. (#1437)
- Docs site: the version banner no longer flags the current release as outdated. (#1429)
- **TPU/XLA training (experimental, undocumented)**: `point_sample(mode="nearest")` no longer leaves the device on MPS/XLA (#1432, issue #1058); `SetCriterion.loss_masks` no longer syncs its normalizing denominator to the host, with a side effect that `dice_loss_jit`/`sigmoid_ce_loss_jit` — reachable only through `lwdetr.py`'s backward-compat re-exports, not the public API — now reject most NumPy scalar denominators (#1428, issue #1058); `build_trainer` now selects `XLAStrategy` for multi-device XLA/TPU training when `strategy="auto"`, previously a crash at `Trainer` construction, and changes the precision-plugin path for single-device `accelerator="auto"` XLA runs too (#1427, issue #1058); XLA-marked tests pass on real TPU silicon (#1426, issue #1058).

## 🏆 Contributors

- **Jesús Royeth** (@JESUSROYETH): all six code/test fixes in this release — the `compile=True`/CUDA multi-scale fix, reduced peak CUDA memory in segmentation loss, and the experimental TPU/XLA device-sync, strategy-selection, and test-suite fixes.
- **Jirka Borovec** (@Borda, [LinkedIn](https://linkedin.com/in/jirka-borovec)): fixed the docs-site version banner falsely flagging the current release as outdated, and this release's own prep.

______________________________________________________________________

**Full changelog**: https://github.com/roboflow/rf-detr/compare/1.10.0...1.10.1

[#1058]: https://github.com/roboflow/rf-detr/issues/1058
[#1411]: https://github.com/roboflow/rf-detr/pull/1411
[#1426]: https://github.com/roboflow/rf-detr/pull/1426
[#1427]: https://github.com/roboflow/rf-detr/pull/1427
[#1428]: https://github.com/roboflow/rf-detr/pull/1428
[#1429]: https://github.com/roboflow/rf-detr/pull/1429
[#1432]: https://github.com/roboflow/rf-detr/pull/1432
[#1436]: https://github.com/roboflow/rf-detr/pull/1436
[#1437]: https://github.com/roboflow/rf-detr/pull/1437