# ultralytics v8.4.135 — v8.4.135 - Respect dataset object counts when selecting max_det (#25993)

- Product: ultralytics (https://whatsnew.fyi/product/ultralytics)
- Vendor: ultralytics
- Date: 2026-08-29
- Version: v8.4.135
- Original notes: https://github.com/ultralytics/ultralytics/releases/tag/v8.4.135
- Permalink: https://whatsnew.fyi/product/ultralytics/releases/v8.4.135

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

---

- **changed** — Training and validation now automatically increase max_det if the default value is lower than the largest number of labeled objects found in a single image
- **changed** — User-specified max_det values are preserved but a warning is shown when they may limit validation recall
- **changed** — The resolved max_det value is propagated to native end-to-end model heads before validation for improved consistency with NMS-free models
- **added** — Warnings notify users when images contain more objects than max_det allows, explaining that a low limit can cap recall and produce misleading validation metrics
- **changed** — Fraction boundary behavior is now consistent, with fraction=1 and fraction=1.0 both meaning use the full dataset, integers greater than 1 representing image count, 0 and 0.0 for skipping optional test split
- **changed** — Boolean values such as fraction=True are now rejected instead of being interpreted ambiguously
- **added** — Additional tests cover configuration validation, dataset conversion, concatenated datasets, training pipelines, and end-to-end detection behavior

##### 🌟 Summary

**v8.4.135 improves detection reliability by adapting `max_det` to dataset object counts and standardizing dataset fraction handling.**

##### 📊 Key Changes

- 🚀 **Smarter `max_det` selection for detection, segmentation, pose, and OBB tasks**
  - Training and validation now inspect the largest number of labeled objects found in a single image.
  - If the default `max_det` is too low, it is automatically increased to match the observed dataset maximum.
  - User-specified `max_det` values are preserved, but a warning is shown when they may limit validation recall.
  - The resolved value is propagated to native end-to-end model heads before validation, improving consistency for NMS-free models.

- ⚠️ **Clearer warnings for object-count mismatches**
  - Users are notified when images contain more objects than `max_det` allows.
  - Warnings explain that a low limit can cap recall and produce misleading validation metrics.
  - Increasing `max_det` may increase validation cost, and cannot exceed the model or export format’s own capacity.

- 📏 **Consistent `fraction` boundary behavior**
  - `fraction=1` and `fraction=1.0` now both mean “use the full dataset.”
  - Integers greater than `1` continue to represent an image count.
  - `0` and `0.0` remain available for skipping an optional test split.
  - Training and validation splits must still contain at least one image.
  - Boolean values such as `fraction=True` are now rejected instead of being interpreted ambiguously.

- 📚 **Documentation and validation updates**
  - Training, export, and cloud-training documentation now describe the normalized fraction semantics.
  - Additional tests cover configuration validation, dataset conversion, concatenated datasets, training pipelines, and end-to-end detection behavior.

##### 🎯 Purpose & Impact

- ✅ **More trustworthy validation:** Large-object-count images are less likely to be truncated by an unnoticed default limit.
- 📈 **Better recall measurement:** Automatically matching `max_det` to observed data helps prevent artificially low validation recall.
- 🧩 **More predictable configuration:** Dataset behavior no longer depends on whether a serializer writes `1` as an integer or `1.0` as a float.
- 🛠️ **Safer user overrides:** Custom `max_det` settings continue to work, with warnings when they may restrict results.
- ⚡ **Potential performance trade-off:** A higher detection limit can increase validation and inference post-processing cost, while model or deployment-format limits may still cap the maximum number of predictions.

##### What's Changed
* Normalize fraction boundary values by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25994
* Respect dataset object counts when selecting max_det by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25993


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.134...v8.4.135