# ultralytics v8.4.134 — v8.4.134 - Reduce TaskAlignedAssigner OOM recovery cost (#25990)

- Product: ultralytics (https://whatsnew.fyi/product/ultralytics)
- Vendor: ultralytics
- Date: 2026-08-29
- Version: v8.4.134
- Original notes: https://github.com/ultralytics/ultralytics/releases/tag/v8.4.134
- Permalink: https://whatsnew.fyi/product/ultralytics/releases/v8.4.134

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- **changed** — TaskAlignedAssigner now retries target assignment one image at a time on GPU instead of moving the entire operation to CPU when GPU memory runs out
- **changed** — Remove unused trailing ground-truth padding before retrying target assignment to lower memory usage
- **changed** — Preallocate retry outputs and reuse dense metric buffers during target assignment
- **changed** — Keep candidate masks in compact integer format to reduce memory usage
- **changed** — Compute box metrics only for valid anchor/ground-truth pairs instead of all pairs
- **changed** — Use more memory-efficient in-place operations during target assignment where possible
- **changed** — Streamline point-in-box checks and overlapping ground-truth resolution
- **changed** — Hyperparameter tuning default increased from 10 trials to 300 trials for built-in tuner, direct Tuner usage, and Ray Tune

##### 🌟 Summary

**v8.4.134 makes crowded-object training more resilient to GPU memory limits and increases the default hyperparameter search budget from 10 to 300 trials.** 🚀

##### 📊 Key Changes

- **Faster TaskAlignedAssigner OOM recovery** 🧠
  - When GPU memory runs out during target assignment, Ultralytics now retries the work **one image at a time on the GPU** instead of moving the entire operation to the CPU.
  - The model’s forward-pass batch size remains unchanged.
  - A warning is shown only once per training run and reports the assignment dimensions.

- **Lower memory usage during target assignment** 💾
  - Removes unused trailing ground-truth padding before retrying.
  - Preallocates retry outputs and reuses dense metric buffers.
  - Keeps candidate masks in a compact integer format.
  - Computes box metrics only for valid anchor/ground-truth pairs.
  - Uses more memory-efficient in-place operations where possible.

- **Improved geometric and assignment processing** ⚙️
  - Streamlines point-in-box checks and overlapping-ground-truth resolution.
  - Preserves compatible output behavior while reducing temporary tensor allocations.

- **Hyperparameter tuning now defaults to 300 trials** 🔍
  - The built-in tuner, direct `Tuner` usage, and Ray Tune all now use **300 trials by default**, up from 10.
  - Documentation has been updated to reflect the shared default.

- **Version update** 📦
  - Package version updated to `8.4.134`.

##### 🎯 Purpose & Impact

- **Much better recovery from GPU out-of-memory errors** ✅  
  Large or highly crowded batches can continue training without falling back to a very slow full-CPU assignment. In the reported xView benchmark, optimized single-image GPU assignment used about **1.56 GB** of peak memory and completed in **0.376 seconds**, compared with **194.86 seconds** and **85.62 GB** for the previous GPU-to-CPU fallback.

- **More practical training on dense datasets** 🏙️  
  The changes are particularly valuable for aerial imagery, crowd analysis, and other datasets containing many objects per image.

- **No need to reduce the model’s forward batch size after recovery** 📈  
  The fallback is isolated to target assignment, helping retain the intended training configuration while handling temporary memory pressure.

- **More effective automatic hyperparameter tuning** 🎯  
  A 300-trial default gives the tuner substantially more opportunities to explore configurations and find stronger settings, especially for the broad YOLO26 search space.

- **Higher tuning cost unless overridden** ⏱️  
  Users who rely on defaults should expect tuning jobs to run considerably longer and consume more compute. Smaller runs can still be requested explicitly when time or budget is limited.

##### What's Changed
* Default hyperparameter tuning to 300 trials by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25987
* Reduce TaskAlignedAssigner OOM recovery cost by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25990


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.133...v8.4.134