pypi ultralytics 8.4.176
v8.4.176 - `ultralytics 8.4.176` Keep CLIP out of YOLO-World checkpoints after set_classes (328 MB → 26 MB) (#26647)

5 hours ago

🌟 Summary

YOLO 8.4.176 makes YOLO-World checkpoints much smaller after setting custom classes, while improving reliability across validation, fine-tuning, segmentation, tracking, and model deployment.

📊 Key Changes

  • YOLO-World checkpoints are dramatically smaller: Setting classes no longer caches the CLIP text encoder by default. For yolov8s-worldv2.pt, a saved checkpoint shrank from 328.5 MB to 25.8 MB, with identical predictions after reload. Caching remains available when explicitly requested.
  • More reliable validation and fine-tuning: Detection validation now matches predictions to eligible targets in confidence order, reducing missed matches. YOLOE fine-tuning and resume also preserve trained classification heads instead of reinitializing them.
  • Fixes for segmentation and SAM: MobileSAM FP16 inference no longer produces empty masks. Reused SAM predictors now reset state for new images and videos. A mask-thresholding change was reverted after it reduced YOLO26 segmentation mAP.
  • Improved deployment and input handling: ONNX GPU inference now supports custom input names and strided tensors; OpenVINO NPU inference handles batches and depth outputs more reliably. Floating-point NumPy images also work with grayscale models.
  • Smoother video, tracking, and benchmarking: Saved videos preserve fractional frame rates, high-FPS streams are handled correctly, and trackers can be copied or pickled on newer Python versions. Several benchmark workflow issues were fixed.
  • Documentation and Platform updates: Docs clarify fine-tuning, inference, and precision behavior; Platform documentation covers relevance-based dataset sorting, email changes, and AI-assisted model comparison.

🎯 Purpose & Impact

  • Save storage and simplify sharing: YOLO-World users can save and move custom-vocabulary checkpoints without carrying an unnecessary CLIP encoder.
  • Trust results and continue training safely: Validation matching and YOLOE resume fixes reduce misleading evaluation results and prevent trained heads from being lost.
  • Get more dependable inference across devices: SAM, ONNX, OpenVINO, image inputs, and video handling work more consistently across common workflows.
  • Keep segmentation metrics stable: Reverting the mask threshold avoids the measured mAP regression introduced by the earlier change.

What's Changed

New Contributors

Full Changelog: v8.4.175...v8.4.176

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