v8.4.146 - Fix RT-DETR inference for small and dynamic inputs (#26120)

🌟 Summary

Ultralytics v8.4.146 improves RT-DETR reliability across small inputs, dynamic exports, and tracking, while updating export portability, Windows training behavior, data downloads, and GPU container environments. 🚀

📊 Key Changes

• 🛠️ RT-DETR inference fixes

  • Prevents failures when small images produce fewer anchors than the configured query count.
  • Keeps query counts, denoising masks, and learned query embeddings consistent.
  • Preserves the correct detection limit in dynamic ONNX and OpenVINO exports.
  • Makes RT-DETR tracking compatible with TrackTrack by avoiding an unsupported NMS recovery argument.
  • Validated across image sizes from 32 to 640 pixels, TorchScript, CoreML, ONNX, OpenVINO, training, validation, and tracking.

• 📦 More device-agnostic TorchScript exports

  • Removes hard-coded device assumptions from export-time tensor creation.
  • TorchScript models exported on CUDA or Apple MPS can now be loaded and used on other devices, such as CPU.
  • Fixes dynamic RT-DETR export behavior so bounding boxes remain correct at sizes different from the export size.

• 🎯 Improved FP16 embedded-NMS inference

  • Converts end-to-end NMS output rows to float32.
  • Prevents segmentation, pose, and OBB FP16 exports from failing during mask or box processing.

• 🪟 Better Windows CUDA training defaults

  • Stops automatically enabling channels_last memory format on Windows, where it could significantly reduce performance.
  • Users can still explicitly enable it with channels_last=True.

• 🌐 More reliable NDJSON image downloads

  • Replaces the previous 30-second total timeout with connection and socket-inactivity timeouts.
  • Large images on slow or shared connections are less likely to fail while downloading.

• 🐳 Updated GPU and export environments

  • AMD64 GPU images now use PyTorch 2.14 with CUDA 13.2.
  • NVIDIA ARM64 images move to NVIDIA PyTorch 26.08 with CUDA 13.4 and updated ONNX Runtime GPU support.
  • GPU runner images now include g++ for compiled training workflows and TensorRT for CUDA 13.
  • LiteRT remains in the Python export image because its current dependencies are not compatible with PyTorch 2.14.
  • Conda Docker builds now use conda-forge PyTorch 2.13, torchvision 0.28, and CUDA 13.0.

• 🧪 CI and maintenance updates

  • Restores the GPU CI workflow while keeping it temporarily disabled through an explicit condition.
  • Adds disk cleanup before isolated export setup.
  • Updates documentation for CUDA 13, JetPack, TensorRT, DLA, Conda images, and export compatibility.
  • Bumps the package version from 8.4.145 to 8.4.146.

🎯 Purpose & Impact

• Greater RT-DETR stability: Small images and dynamically sized inputs should no longer trigger query or anchor-related inference failures. ✅
• More dependable deployment: Models exported on one accelerator can be used on another device more reliably, reducing device-specific TorchScript issues.
• Safer FP16 workflows: Embedded-NMS exports for segmentation, pose, and OBB models now handle post-processing without dtype errors.
• Faster Windows training in common cases: Windows CUDA users avoid an automatic memory layout optimization that was measured to slow training substantially.
• Improved dataset conversion reliability: Large NDJSON image downloads can complete on constrained networks instead of repeatedly stopping at a total timeout.
• Clearer GPU compatibility requirements: CUDA 13 containers may not support older Maxwell, Pascal, or Volta GPUs, and Jetson users must match TensorRT versions to their JetPack release.
• Deployment caution: The updated GPU images require compatible NVIDIA host drivers, and TensorRT engines should be rebuilt or validated when the GPU, CUDA, TensorRT, or JetPack environment changes.

What's Changed

• Update GPU Docker images to current PyTorch and CUDA releases by @glenn-jocher in #26115
• Restore GPU CI with a temporary disable condition by @glenn-jocher in #26119
• Time NDJSON image downloads by socket inactivity instead of a 30 s total deadline by @raimbekovm in #26113
• Keep TorchScript exports device-agnostic by @raimbekovm in #26118
• Cast end-to-end NMS rows to float32 so FP16 exports with embedded NMS decode segment masks by @raimbekovm in #26114
• Skip channels_last auto-enable on Windows CUDA training by @Y-T-G in #26105
• Fix RT-DETR inference for small and dynamic inputs by @glenn-jocher in #26120

Full Changelog: v8.4.145...v8.4.146