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

- Product: ultralytics (https://whatsnew.fyi/product/ultralytics)
- Vendor: ultralytics
- Date: 2026-08-26
- Version: v8.4.130
- Original notes: https://github.com/ultralytics/ultralytics/releases/tag/v8.4.130
- Permalink: https://whatsnew.fyi/product/ultralytics/releases/v8.4.130

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- **added** — fraction parameter now accepts positive integer image counts such as fraction=1000 to train on exactly 1000 images
- **added** — fraction parameter accepts list format [train_count, val_count] to limit training and validation splits independently
- **added** — Count-based dataset subset selection now supported for YOLO, RTDETR, classification, validation, and INT8 calibration workflows
- **changed** — fraction parameter now distinguishes between integer 1 meaning one image and float 1.0 meaning the complete split
- **changed** — Count-based subsets are selected before images are downloaded for NDJSON and Platform datasets
- **changed** — NDJSON records are now selected deterministically to ensure repeated runs use the same images
- **changed** — Model.tune() now defaults to AdamW optimizer unless another optimizer is explicitly selected
- **changed** — tune_fitness.png now displays overall fitness progression, best result achieved, and initial-versus-best fitness for each dataset
- **changed** — Tracking documentation now lists six built-in trackers and documents TrackTrack as the default tracker
- **changed** — Tracking documentation expanded to clarify confidence thresholds, low-confidence recovery, custom ReID models, and task-specific behavior for segmentation, pose, and OBB models
- **added** — Added or corrected license information for MNIST, Global Wheat2020, PASCAL VOC, KITTI, and official depth datasets
- **added** — Explicitly marked Depth8 and SUN RGB-D datasets as having no specified source license
- **changed** — Export documentation across ONNX, TensorRT, OpenVINO, LiteRT, Hailo, QNN, Rockchip and other formats updated to reflect expanded fraction behavior
- **fixed** — Fixed concurrent MongoDB tuner default claims in multi-worker tuning runs
- **fixed** — Fixed tuning optimizer default to apply hyperparameter changes effectively

##### 🌟 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 https://github.com/ultralytics/ultralytics/pull/25939
* Fix

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