v1.9.1: Faster Inference & Export, Correct Export Resize

RF-DETR v1.9.1 is a maintenance release that makes inference and export both faster and more correct — with no breaking changes and no code changes required. Segmentation post-processing is 2.6–3.0× faster, predict() is ~20% faster at 1080p by skipping masks it would discard, and on-device ExecuTorch/XNNPACK is 2.5× faster. Exported ONNX/TFLite/INT8 models now resize exactly like predict(), closing a silent confidence and calibration drift. A handful of install fixes round it out on the Python-version edges.

✨ Spotlights / highlights

Segmentation post-processing 2.6–3.0× faster

PostProcess no longer materialises a repeated int64 gather index (21–84 MiB per image for the mask head) — it selects rows with index_select/expand instead. Output is bit-for-bit identical.

mask head, K=300	before	after	speedup
96²	8.79 ms	3.40 ms	2.6×
192²	79.99 ms	27.09 ms	3.0×

predict() ~20% faster at 1080p

Segmentation masks below the caller's threshold are dropped before upsampling instead of after, so ~97% of the resize work on typical COCO images disappears. The saving scales with image area (bigger at 4K, neutral at 640 px); output is unchanged.

model.predict(image, threshold=0.5)  # only surviving masks are upsampled

ExecuTorch / XNNPACK 2.5× faster

Export recombines the addmm operations XNNPACK leaves undelegated back into aten.linear. RFDETRNano on Apple silicon: 119.9 → 48.3 ms median; outputs match to ~1e-4.

model.export(format="executorch", backend="xnnpack")

Exported models resize exactly like predict()

ONNX inference, TFLite inference, INT8 calibration, and the benchmark path now use predict()'s resize convention (bilinear, half-pixel centers, antialias=False) rather than PIL's antialiased filters, which diverged on downscale.

⚠️ If you ship INT8 TFLite models, re-export them to recalibrate against the corrected pixel distribution.

📝 Notable changes

🌱 Changed

• Segmentation post-process — index_select/expand replace a repeated int64 gather index; 2.6–3.0× faster at head resolution, output unchanged. (#1268)
• predict() mask upsampling — masks below threshold are discarded before upsampling; ~20% faster at 1080p, output unchanged. (#1265)
• ExecuTorch export — AddmmToLinearTransform recombines undelegated addmm into aten.linear; ~2.5× faster XNNPACK inference. (#1262)

🔧 Fixed

• Export resize parity — ONNX/TFLite inference, INT8 calibration, and benchmark paths now match predict()'s resize convention; re-export INT8 TFLite models to recalibrate. (#1269)
• [onnx] / [executorch] install — extras are gated to interpreters that ship wheels, fixing [onnx] on Python 3.10 and [executorch] on Python 3.14. (#1267)
• Kornia range params — GaussianBlur/GaussNoise builders accept a scalar or a (min, max) pair, matching the Albumentations path. (#1255)
• keypoint_flip_pairs — detection-only datasets with a custom aug_config keep horizontal-flip augmentations by passing None instead of []. (#1248)
• uv sync — an executorch/tflite extra conflict that blocked dev-environment creation is resolved. (#1253)

📚 Documentation

• Updated ONNX, TFLite, ExecuTorch, CoreML, and INT8 calibration examples to use the same tensor-first, antialias=False preprocessing as predict(). The TFLite example now selects dets and labels outputs by name. (#1269)
• Corrected keypoint model Params (126.4 → 40.7 M), added a Params column to the keypoint benchmarks, and clarified that the SAM 3 RF100-VL result is author-reported, not measured by RF-DETR/SAB. (#1258, #1261)
• Fixed the ONNX Runtime example to include the missing decode step. (#1251)
• Added keypoint coverage to llms.txt / llms-full.txt. (#1260)

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🏆 Contributors

• @JESUSROYETH — faster segmentation post-processing, threshold-aware mask upsampling, and export resize parity
• @chmjkb — 2.5× faster ExecuTorch / XNNPACK inference (React Native ExecuTorch team)
• @Vedanshu7 — gated the [onnx]/[executorch] extras to interpreters that ship wheels
• @adhavan18 (LinkedIn) — scalar-or-pair range parameters for the Kornia augmentation builders
• @unaxEtxeberriaBieleDigital — fixed uv sync dev-environment resolution
• @isaacrob (LinkedIn) — corrected keypoint benchmark Params and expanded llms.txt coverage
• @Borda (LinkedIn) — keypoint_flip_pairs fix, ONNX docs correction, and release maintenance
• @atikulmunna (LinkedIn) — stricter type-checking across variants and model_weights

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Full changelog: 1.9.0...1.9.1