ultralytics 8.4.85

v8.4.85 - Fix TAL OOM recovery, AutoBatch loss estimation, LiteRT INT8 calibration, and nc validation (#25016)

on Python PyPI

🌟 Summary

Ultralytics v8.4.85 focuses on reliability and deployment polish, with important fixes for training out-of-memory recovery, AutoBatch memory estimation, LiteRT INT8 calibration, dataset validation, YOLO-World validation, RT-DETR/CoreML behavior, and edge-device documentation 🚀

📊 Key Changes

• Fixed TaskAlignedAssigner OOM recovery during training 🧠
  The CPU fallback for TaskAlignedAssigner now runs after the failed CUDA traceback is released, preventing the recovery path itself from running out of GPU memory.

• Improved AutoBatch memory estimation 📦
  AutoBatch now better estimates detection loss memory usage, including the large temporary tensors used during assignment and classification loss calculation. This should help choose safer batch sizes for heavy training jobs.

• Improved LiteRT static INT8 calibration 📱
  LiteRT calibration now handles smaller or partial calibration batches by repeating samples to match the traced batch size instead of failing when the dataset does not provide a perfectly sized batch.

• Stricter dataset nc validation ✅
  Dataset YAML files now validate that nc is an integer or integer-like value. Invalid values such as placeholders or decimals fail early with a clearer error.

• YOLO-World standalone validation fix 🌍
  Added a dedicated WorldValidator so model.val() correctly loads dataset class names and regenerates text embeddings when validating YOLO-World models on custom datasets.

• Rectangular training shape fix 📐
  Augmentations now respect rect=True batch shapes instead of incorrectly falling back to square sizing, improving training consistency for datasets with varied aspect ratios.

• RT-DETR validation and docs cleanup ⚡
  RT-DETR validation transforms were simplified by removing a special stretch path, and docs now clarify that max_det cannot make pretrained RT-DETR models return more detections than their decoder query limit.

• CoreML RT-DETR inference improvement 🍎
  CoreML export metadata now records the model head type, allowing RT-DETR models to use ComputeUnit.ALL. This improves RT-DETR CoreML speed and helps preserve FP16 accuracy compared with Neural Engine-only execution.

• CUDA device selection fix 🖥️
  select_device() now preserves the requested CUDA index after CUDA has already been initialized, avoiding confusing device remapping behavior.

• Tracking index bug fix 🎯
  ByteTrack now preserves original detection indices for low-confidence detections matched during second association, reducing callback/index mismatches in tracking workflows.

• Better user-facing prediction windows 🪟
  show=True display windows now auto-scale oversized images on Linux and Windows to avoid cropped previews.

• YOLOE visual prompting verbosity fix 🔇
  YOLOE visual prompting now respects verbose=False, preventing unwanted setup logs when users request quiet prediction.

• Dataset name convenience 🗂️
  Detection-style validation now supports bare dataset names like coco8 by resolving them to coco8.yaml when available.

• Training fails fast when labels are missing 🚨
  Training now raises an error when no labels are found, helping users catch dataset setup issues immediately. Unlabeled validation splits can still warn instead of failing.

• Updated edge deployment documentation 📚
  Hailo docs now include a cleaner YOLO11-to-HEF workflow, metadata.yaml handling, and expanded inference examples for images, videos, webcams, and Raspberry Pi cameras. LiteRT, TFLite, and QNN docs were refreshed with current YOLO26n Android benchmark context.

• Security warning for HTTP console log streaming 🔐
  ConsoleLogger now warns when logs are sent to plaintext http:// destinations, helping users avoid transmitting sensitive console output unencrypted.

🎯 Purpose & Impact

• More stable training on large or dense datasets 💪
  The OOM recovery and AutoBatch fixes directly target real training failures, making Ultralytics more robust for workloads with many objects, large batches, or high memory pressure.

• Fewer confusing export and deployment failures 📲
  LiteRT calibration is more forgiving, CoreML RT-DETR inference is more accurate and faster, and Hailo deployment docs are clearer about required files and metadata.

• Better validation across model families 🧪
  YOLO-World, RT-DETR, and detection-style datasets now behave more consistently, especially when users validate custom datasets or use simplified dataset names.

• Clearer errors for dataset problems 🛠️
  Missing labels and invalid nc values are caught earlier, reducing wasted training time and making configuration issues easier to fix.

• Improved day-to-day usability ✨
  Rectangular training, CUDA device selection, tracking callbacks, prediction display windows, and YOLOE quiet mode all receive practical fixes that make common workflows smoother.

• Stronger mobile and edge guidance 🚀
  Refreshed YOLO26n LiteRT/TFLite/QNN benchmark documentation and expanded Hailo examples help users make better deployment decisions across Android, Raspberry Pi, and Hailo hardware.

What's Changed

• Improve Hailo documentation and add more inference options by @lakshanthad in #24645
• Fix rect=True training producing square instead of rectangular tensors by @Y-T-G in #25009
• Clarify RT-DETR max_det and query limit by @oaslananka in #24994
• Simplify RTDETR dataset transforms and remove stretch argument from v8_transforms by @Laughing-q in #25011
• Clarify square image size requirement in benchmark docs by @oaslananka in #24993
• Add semantic mask boundary quality guidance by @oaslananka in #24976
• Scale window to prevent cropped preview on WIndows by @Y-T-G in #24973
• Add WorldValidator for YOLO-World by @Y-T-G in #24974
• Use CoreML ComputeUnit.ALL for RT-DETR inference by @artest08 in #24999
• Fix verbose bug YOLOE visual prompting by @YoannSME in #24814
• Raise exception when labels are missing by @alhatemmohanad-ui in #24760
• Skip unstable RTDETR CPU train on JetPack 5 by @glenn-jocher in #24945
• Support dataset names without .yaml extension in train and val by @lakshanthad in #24765
• Fix initialized CUDA device selection by @oaslananka in #24987
• Warn on plaintext HTTP console log streaming destinations by @amanharshx in #25001
• Optimize the index for tracking target feedback by @zhixuwei in #24971
• Update LiteRT and TFLite benchmark docs by @glenn-jocher in #25015
• Fix TAL OOM recovery, AutoBatch loss estimation, LiteRT INT8 calibration, and nc validation by @glenn-jocher in #25016

New Contributors

• @YoannSME made their first contribution in #24814
• @alhatemmohanad-ui made their first contribution in #24760

Full Changelog: v8.4.84...v8.4.85