1.7.1

## Summary

RF-DETR `1.7.1` is a focused patch release that fixes three bugs affecting common workflows. If you train segmentation models with BF16 mixed-precision, load official starter checkpoints with `from_checkpoint`, or run RF-DETR alongside NumPy 2.x, this release resolves crashes you may have hit.

## What's fixed

- **BF16 mixed-precision segmentation training crash.** Training a segmentation model with BF16 mixed-precision (e.g. `amp_dtype="bfloat16"`) could fail with a dtype mismatch error in fused AdamW. The root cause was a low-level convolution backward pass converting a gradient to reduced precision before it reached the optimizer. Gradients now stay in the correct dtype throughout, matching standard PyTorch behaviour. (#1076)

- **`RFDETR.from_checkpoint` failing on official starter checkpoints.** Loading official Roboflow starter weights with `RFDETR.from_checkpoint(...)` raised `ValueError: Could not infer model class`. The checkpoint metadata stores `pretrain_weights="none"` as a sentinel value, which the loader didn't handle. It now falls back to inferring the model variant from the checkpoint filename and logs which variant it detected. (#1065)

- **NumPy 2.x import crash.** `import rfdetr` raised `AttributeError: module 'numpy' has no attribute 'complex_'` when NumPy 2.0 or later was installed alongside an older transitive dependency (such as chumpy or older TensorFlow builds) that still references the removed `np.complex_` alias. A compatibility shim at import time resolves this without requiring any changes to your environment. (#1064)

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## Contributors

New contributor to this release — welcome!

- **Omkar Kabde** (@omkar-334) — TFLite test reliability improvements
- **Jirka Borovec** (@Borda) — BF16 segmentation fix, release

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**Full changelog**: https://github.com/roboflow/rf-detr/compare/1.7.0...1.7.1