Ultralytics YOLO

🌟 Summary

Ultralytics 8.4.158 improves YOLO26 training reliability, model export compatibility, data handling, inference backends, and Ultralytics Platform workflows. 🚀

📊 Key Changes

• 🔄 Mosaic augmentation now stays closed after OOM recovery

  • Fixed a training issue where automatic batch-size reduction rebuilt the dataloader and unintentionally re-enabled Mosaic augmentation.
  • The trainer now re-arms the mosaic-closing logic after rebuilding the pipeline, ensuring the augmentation state matches the training log and configuration.

• ⚙️ More reliable AutoBatch sizing

  • AutoBatch no longer treats a failed batch-one probe caused by BatchNorm at very small image sizes as a GPU memory limit.
  • Added CUDA coverage for small image sizes to prevent regressions.

• 🧠 Improved data loading and caching

  • RAM image caches can now be shared more efficiently with spawn and forkserver DataLoader workers.
  • Linux DataLoader workers start lazily, reducing the risk of image-decoder deadlocks during initialization.
  • Dataset hyperparameters are copied before transforms modify them, preventing one dataset from changing settings used by later datasets.
  • Disk caching now uses a smaller safety margin and handles failed cache writes more safely.

• 📦 Stronger download and dataset handling

  • safe_download() can resume interrupted downloads with HTTP Range requests, handle encoded responses, and better tolerate temporary server errors.
  • Invalid files named as archives are now returned as files instead of being mistaken for extracted datasets.
  • NDJSON conversion keeps at least one image when a valid nonzero fraction would otherwise select none.

• 📤 More accurate model exports

  • Exported models with missing or invalid class names now infer the correct class count from the model head instead of defaulting to 999 names.
  • This improves metadata consistency for legacy detection and classification checkpoints.
  • TorchScript inference is stabilized on older PyTorch versions by avoiding a repeated-inference crash.
  • TensorRT now raises clear errors when dynamic shapes are rejected or inference execution fails.

• 🎭 Better SAM2 video masks

  • SAM2 video predictions now correctly enforce non-overlapping masks before thresholding.
  • The highest-scoring object owns each pixel, preventing tracked objects from incorrectly covering one another.
  • CPU semantic segmentation postprocessing is also faster through improved class-index selection.

• 🍎 Expanded Apple deployment support

  • Core AI assets now include model descriptions and are documented as an opt-in path in the Ultralytics iOS SDK and Flutter plugin.
  • Core ML remains the default and recommended Apple deployment format, especially for broader device compatibility.
  • Core AI remains limited to newer Apple operating systems and Apple silicon export environments.

• 🛠️ Ultralytics Platform improvements

  • Added class-prompted annotation using hosted open-source models and paid vision providers for detection datasets with one to 100 classes.
  • Agents workflows now support HTTPS webhook actions in addition to Slack notifications.
  • TIFF originals can be stored without re-encoding.
  • Platform documentation now covers provider API keys, annotation behavior, upload handling, and the corrected ARKitScenes raw-data workflow.

• 📚 Documentation and compatibility updates

  • ExecuTorch guidance now explains that Ultralytics installs a compatible version automatically.
  • ARKitScenes documentation now uses the raw subset and its approximately two-hertz frame sampling process.
  • System font lookup rescans installed fonts when Matplotlib's cached list is outdated.

🎯 Purpose & Impact

• More predictable training: Mosaic augmentation remains disabled when expected, even after automatic OOM recovery, improving reproducibility and avoiding misleading training behavior.
• Fewer training failures: AutoBatch, caching, DataLoader startup, and dataset conversion fixes make training more robust across hardware, operating systems, and small image sizes.
• Safer deployment: Exported metadata now matches the actual model, while TorchScript and TensorRT failures produce clearer and earlier errors.
• Cleaner segmentation results: SAM2 video users should see fewer overlapping tracked masks and more consistent object ownership.
• Broader platform workflows: Users can annotate datasets with natural-language or vision-language models, connect workflows to web services, and preserve TIFF source files.
• Better Apple integration: Core AI is usable as an opt-in for supported iOS 27 devices, while Core ML remains the safest default for most Apple deployments. 🍎

What's Changed

• Ignore AutoBatch probe failures below the first successful size by @fcakyon in https://github.com/ultralytics/ultralytics/pull/26224
• Cover AutoBatch at imgsz below 2x stride in the GPU tests by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26248
• Set the Core AI asset description and document Core AI as an SDK opt-in by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26230
• Remove Core AI from the Platform export format docs by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26249
• Resume safe_download retries with Range requests by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26250
• Avoid fork-inherited image decoder deadlocks in Linux data loaders by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26085
• Share RAM image caches across DataLoader workers by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26086
• Make safe_download robust to encoded responses, curl resumes, and 5xx bursts by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26251
• Add class names fallback for legacy checkpoints on export by @venu-banaras in https://github.com/ultralytics/ultralytics/pull/24715
• Reduce the disk cache safety margin and guard failed cache writes by @Swish78 in https://github.com/ultralytics/ultralytics/pull/26112
• Remove the ExecuTorch upgrade advice that installs a runtime the package rejects on torch < 2.13 by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26263
• Fix repeated TorchScript inference crash on torch<2.1 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26264
• Apply the SAM2 video non-overlapping mask constraint on the float masks and select the mask owner with max indices by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26262
• Select semantic class indices with max instead of argmax on CPU by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26261
• Document the ARKitScenes raw subset and its 2 Hz frame sampling by @Bovey0809 in https://github.com/ultralytics/ultralytics/pull/26260
• Align Platform docs with tiff passthrough, agents webhooks and class-prompted annotation models by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26258
• Rescan system fonts in check_font when the cached font list misses by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26256
• Raise on rejected TensorRT shapes and executions by @aswanth-07 in https://github.com/ultralytics/ultralytics/pull/26268
• Return the file itself when an archive-suffixed download is not a zip or tar by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26255
• Keep a nonzero ndjson conversion fraction from selecting zero images by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26254
• Stop dataset construction from mutating the caller's hyp namespace by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26266
• Preserve model class counts in export name fallbacks by @aswanth-07 in https://github.com/ultralytics/ultralytics/pull/26253
• ultralytics 8.4.158 Keep mosaic closed after an OOM batch auto-reduction by @rahultechenable in https://github.com/ultralytics/ultralytics/pull/26257

New Contributors

• @Swish78 made their first contribution in https://github.com/ultralytics/ultralytics/pull/26112
• @venu-banaras made their first contribution in https://github.com/ultralytics/ultralytics/pull/24715
• @rahultechenable made their first contribution in https://github.com/ultralytics/ultralytics/pull/26257

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.157...v8.4.158