# New Release: Ultralytics v8.4.150

**URL:** <https://community.ultralytics.com/t/new-release-ultralytics-v8-4-150/2219>\
**Category:** Discussion\
**Tags:** releases, announcements, ultralytics-official\
**Created:** [September 13, 2026, 1:08pm UTC](https://community.ultralytics.com/t/new-release-ultralytics-v8-4-150/2219 "2026-09-13T13:08:22Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![glenn-jocher](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/glenn-jocher/32/83_2.png) [@glenn-jocher](https://community.ultralytics.com/u/glenn-jocher)\
**Post date:** [September 13, 2026, 1:08pm UTC](https://community.ultralytics.com/t/new-release-ultralytics-v8-4-150/2219/1 "2026-09-13T13:08:22Z")

</div>

# 🚀 Ultralytics v8.4.150 Released

Ultralytics v8.4.150 delivers **faster and more secure checkpoint loading** , restores **fused YOLOE checkpoint compatibility** , improves **RT-DETR training efficiency on dense datasets** , and expands model and deployment documentation.

## 🌟 Highlights

- ⚡ Faster restricted checkpoint loading and reduced cold-start overhead
- ✅ Restored loading and warmup support for fused YOLOE checkpoints
- 📈 More efficient RT-DETR matching for images containing many objects
- 🛡 Improved privacy for Sentry diagnostics
- ☁ Expanded Ultralytics Platform deployment guidance
- 📚 A clearly labeled YOLO27 preview, while **YOLO26 remains the recommended released model**
- 🏆 Clearer benchmark tables across supported model families

## ✨ New Features and Documentation

### YOLO27 Preview Documentation

[PR #26154](https://github.com/ultralytics/ultralytics/pull/26154) by [@glenn-jocher](https://github.com/glenn-jocher) introduces a **YOLO27 Coming Soon** page covering planned architecture details, supported tasks, preliminary benchmarks, and future usage examples.

YOLO27 weights, package support, and a launch date are **not currently available**. For new projects, [YOLO26 remains the recommended stable Ultralytics model](https://docs.ultralytics.com/models/yolo26).

### Expanded Ultralytics Platform Deployment Guidance

[PR #26149](https://github.com/ultralytics/ultralytics/pull/26149) by [@sergiuwaxmann](https://github.com/sergiuwaxmann) documents:

- Configurable CPU and memory resources
- Uptime billing and scale-to-zero behavior
- Lightweight monitoring for eligible paid endpoints
- Temporary prediction examples and statistics
- Saving useful prediction examples to datasets for persistent storage

The [Ultralytics Platform](https://platform.ultralytics.com) provides an integrated workflow for annotating datasets, training models, and deploying Ultralytics YOLO.

## ⚡ Performance Improvements

### Faster Restricted Checkpoint Loading

[PR #26155](https://github.com/ultralytics/ultralytics/pull/26155) by [@glenn-jocher](https://github.com/glenn-jocher) resolves only the model classes referenced by a checkpoint instead of scanning every supported model module.

This update:

- Reduces unnecessary module discovery and startup overhead
- Preserves trusted namespaces, aliases, registration locks, and allow-list behavior
- Restores safe `forward` and `forward_fuse` bindings for fused YOLOE checkpoints
- Improves rejected-object error messages without calling `repr()` on partially reconstructed modules
- Expands tests for legacy checkpoints, fused models, and inference consistency

### Faster RT-DETR Matching on Dense Datasets

[PR #26150](https://github.com/ultralytics/ultralytics/pull/26150) by [@fcakyon](https://github.com/fcakyon) avoids computing matching costs between predictions and targets belonging to different images.

This reduces unnecessary storage and padding overhead, with the largest benefits expected on dense-object datasets such as SKU-110K. The assignment solver, losses, precision, and training configuration remain unchanged.

### Clearer Model Benchmarks

[PR #26156](https://github.com/ultralytics/ultralytics/pull/26156) by [@glenn-jocher](https://github.com/glenn-jocher) standardizes bold highlighting for leading accuracy, speed, latency, size, parameter-count, and computational-cost values.

Tables were updated across YOLO26, YOLO11, YOLOv8, YOLOv9, YOLO12, YOLOE, SAM, YOLO-NAS, and other supported families without changing the underlying benchmark results.

## 🛠 Compatibility and Bug Fixes

### Legacy NumPy Metadata and CLIP Support

[PR #26153](https://github.com/ultralytics/ultralytics/pull/26153) by [@glenn-jocher](https://github.com/glenn-jocher) adds restricted-loading support for legacy NumPy metadata and embedded Ultralytics CLIP objects.

Together with the fused-checkpoint updates, this improves compatibility with older Platform checkpoints while maintaining explicit loading restrictions.

### Safer Diagnostics and Updated Ray Tune Support

[PR #26151](https://github.com/ultralytics/ultralytics/pull/26151) by [@glenn-jocher](https://github.com/glenn-jocher) prevents Sentry from capturing command-line arguments and local variables that may contain credentials.

The update also requires Ray `2.41.0` or newer and removes obsolete compatibility logic for older Ray releases.

## 📦 Upgrade and Try It

Upgrade to v8.4.150 with:

```bash
pip install -U ultralytics

```

Explore the complete release details on the [Ultralytics v8.4.150 release page](https://github.com/ultralytics/ultralytics/releases/tag/v8.4.150), or review every change in the [v8.4.149 to v8.4.150 comparison](https://github.com/ultralytics/ultralytics/compare/v8.4.149...v8.4.150).

Please try the release and share your feedback, benchmark results, or any issues you encounter. Thanks to every contributor and the wider YOLO community for helping improve Ultralytics YOLO! 🙌