ultralytics 8.4.130

v8.4.130 - Enable fraction to limit dataset by image counts (#25951)

on Python PyPI

🌟 Summary

Version v8.4.130 makes dataset subset selection far more flexible and efficient, while improving tuning, tracking guidance, and dataset metadata. 🚀

📊 Key Changes

• Count-based dataset limits 🎯

  • fraction now accepts a positive image count, such as fraction=1000, to train on exactly 1,000 images.
  • Use fraction=[1000, 100] to limit the training and validation splits independently.
  • Existing decimal ratio behavior remains unchanged, so fraction=0.1 still uses 10% of the dataset.
  • Integer 1 means one image, while float 1.0 means the complete split.
  • Supports YOLO, RTDETR, classification, validation, and INT8 calibration workflows.

• More efficient NDJSON and Platform dataset downloads ⚡

  • Count-based subsets are selected before images are downloaded.
  • NDJSON records are selected deterministically, helping repeated runs use the same images.
  • This avoids downloading an entire dataset when only a fixed-size subset is needed.

• Improved hyperparameter tuning 🧠

  • Model.tune() now defaults to AdamW unless another optimizer is explicitly selected.
  • This ensures tuning parameters such as learning rate and momentum actually affect training instead of being ignored by automatic optimizer selection.
  • MongoDB-based tuning now uses safer atomic coordination, preventing multiple workers from incorrectly claiming the default configuration.

• Clearer tuning fitness plots 📈

  • tune_fitness.png now shows overall fitness progression, the best result achieved so far, and initial-versus-best fitness for each dataset.
  • The new layout is easier to interpret, especially for multi-dataset tuning runs.

• Expanded and clarified tracking documentation 🎥

  • Documentation now lists six built-in trackers: TrackTrack, BoT-SORT, ByteTrack, OC-SORT, Deep OC-SORT, and FastTracker.
  • TrackTrack is documented as the default tracker, with optional ReID and camera-motion compensation.
  • Tracking guidance now more clearly explains confidence thresholds, low-confidence recovery, custom ReID models, and task-specific behavior for segmentation, pose, and OBB models.
  • Tracker-specific training is clarified: users train a detection, segmentation, pose, or OBB model, then apply tracking during inference.

• More complete dataset license metadata 📚

  • Added or corrected license information for MNIST, Global Wheat2020, PASCAL VOC, KITTI, and official depth datasets.
  • Depth8 and SUN RGB-D are now explicitly marked as having no specified source license where applicable.
  • Export documentation across ONNX, TensorRT, OpenVINO, LiteRT, Hailo, QNN, Rockchip, and other formats now reflects the expanded fraction behavior.

🎯 Purpose & Impact

• Faster experimentation: Quickly train or calibrate on a known number of images without creating duplicate dataset copies.
• Lower storage and bandwidth usage: Platform NDJSON datasets no longer need to download every image before applying a count-based limit.
• More reliable tuning: AdamW makes the default tuning search spaces effective, while MongoDB coordination avoids duplicate baseline trials in concurrent runs.
• Better reproducibility: Deterministic NDJSON subset selection makes repeated experiments more consistent.
• Improved deployment workflows: Fixed-size calibration subsets are now easier to use across supported export formats, helping reduce INT8 calibration time.
• Clearer tracking decisions: Users can more easily choose a tracker and understand the trade-offs between speed, ReID, camera-motion compensation, and occlusion handling.
• No major model architecture changes: This release primarily improves data handling, tuning reliability, tracking usability, and documentation rather than introducing a new model family.

What's Changed

• Fix concurrent MongoDB tuner default claims by @glenn-jocher in #25939
• Fix Tune optimizer default by @glenn-jocher in #25945
• Improve tuning fitness plot by @glenn-jocher in #25946
• Add depth dataset license metadata by @glenn-jocher in #25948
• Fix Depth8 licensing and tracker docs by @glenn-jocher in #25950
• Enable fraction to limit dataset by image counts by @fcakyon in #25951

Full Changelog: v8.4.129...v8.4.130