ultralytics 8.4.149

v8.4.149 - Fix resume, ONNX CUDA fallback, Ray Tune and W&B training crashes (#26148)

on Python PyPI

🌟 Summary

Ultralytics 8.4.149 improves training reliability, export compatibility, and Platform documentation—especially for resumed runs and third-party integrations. 🚀

📊 Key Changes

• More reliable training resume behavior 🔄

  • Resuming from a checkpoint now preserves the dataset stored in that checkpoint instead of unexpectedly switching to the default COCO8 dataset.
  • An explicitly provided data= argument still takes priority.
  • Missing datasets now produce the expected dataset-not-found error rather than causing optimizer or model-shape failures.
  • Resuming checkpoints created with AMP disabled no longer attempts to load an empty AMP scaler state.

• Safer ONNX CUDA fallback ⚙️

  • CUDA availability is now checked after ONNX Runtime creates its inference session.
  • If the CUDA execution provider is unavailable, inference automatically falls back to CPU with a clear warning instead of crashing.

• Ray Tune compatibility improvements 📈

  • Ray Tune integration now verifies that a compatible Ray version is installed.
  • Training metrics are reported through the current Ray Tune API, reducing failures with newer Ray releases.

• More robust Weights & Biases runs 📊

  • Automatically generated W&B run names are limited to 64 characters, preventing crashes caused by overly long names.

• Improved invalid-argument errors 🛠️

  • CLI help is logged separately from the syntax error message, helping the core error remain visible even when logs are truncated.

• RT-DETR LiteRT export and inference fixes 📱

  • RT-DETR outputs are no longer incorrectly coordinate-normalized during LiteRT export or inference.
  • This brings LiteRT detection metrics much closer to ONNX results, improving accuracy across Python and JavaScript/npm workflows.

• SAM 3.1 becomes the default Platform smart annotation model ✨

  • Ultralytics Platform now lists SAM 3.1 as the default smart annotation model, while SAM 3 and SAM 2.1 remain available.
  • SAM 3.1 image prediction support and current limitations, including unsupported Object Multiplex video tracking, are documented.

• Platform AutoTrain documentation reorganized 📚

  • AutoTrain has moved to Platform → Train → AutoTrain.
  • The documentation now emphasizes baseline creation, controlled comparisons, experiment review, billing, and team workspace usage.

• Package version updated 📦

  • Version bumped to 8.4.149.

🎯 Purpose & Impact

• Fewer training crashes: Users can resume interrupted jobs more confidently, even when datasets have moved or AMP settings differ.
• Better deployment resilience: ONNX models requested on CUDA systems can continue running on CPU when CUDA support is unavailable.
• More dependable integrations: Ray Tune and W&B workflows are less likely to fail because of API differences or naming limits.
• Improved edge deployment accuracy: RT-DETR models exported to LiteRT should produce results more consistent with ONNX, benefiting mobile and browser applications.
• Simpler annotation workflows: New users on Ultralytics Platform get SAM 3.1 by default for high-quality image annotation, with alternative SAM models available for speed or compatibility.
• Clearer experimentation guidance: The reorganized AutoTrain documentation makes it easier to run repeatable training experiments and choose the next improvement step.

What's Changed

• List SAM 3.1 as the default Platform smart annotation model by @glenn-jocher in #26145
• Move AutoTrain documentation under Platform Train by @JaviChulvi in #26146
• Fix normalization conditions in DETR models for LiteRT export and inference by @onuralpszr in #26142
• Fix resume, ONNX CUDA fallback, Ray Tune and W&B training crashes by @glenn-jocher in #26148

Full Changelog: v8.4.148...v8.4.149