# ultralytics v8.4.131 — v8.4.131 - Add Apple Core AI export (#25926)

- Product: ultralytics (https://whatsnew.fyi/product/ultralytics)
- Vendor: ultralytics
- Date: 2026-08-27
- Version: v8.4.131
- Original notes: https://github.com/ultralytics/ultralytics/releases/tag/v8.4.131
- Permalink: https://whatsnew.fyi/product/ultralytics/releases/v8.4.131

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- **added** — Export models to Apple Core AI format with model.export(format="coreai") creating .aimodel assets that can be loaded with YOLO("yolo26n.aimodel")
- **added** — Support FP32 and optional FP16 export for Apple Core AI through new export-coreai dependency group
- **added** — Add dedicated Core AI backend, metadata handling, API references, and export-table support
- **added** — Include model metadata such as class names, stride, and task information inside .aimodel assets
- **added** — Support exporting YOLO26 with end2end=False to produce raw predictions and reduce inference latency
- **changed** — YOLO26's end-to-end head is exported by default, returning finished detections directly
- **changed** — Validation now consistently uses unaugmented validation pipeline instead of accidentally applying training augmentations like Mosaic, MixUp, and Random Perspective
- **changed** — Dataset fractions are now selected according to the requested split during validation
- **changed** — Model-scale overrides in parse_model now match exact scale letters, preventing unscaled or dictionary-based configurations from taking wrong architecture branch
- **changed** — Documentation and logging now distinguish YOLO26's l1_loss from dfl_loss used by models with distribution-based box regression
- **changed** — TQDM output no longer disappears in zero-width pseudo-terminals and notebook output is allowed to scroll naturally
- **changed** — Truncated terminal lines now show ellipsis instead of being silently cut off
- **fixed** — C3k2 configurations without explicitly provided optional argument no longer fail for medium, large, or extra-large variants
- **fixed** — YOLOE.set_classes() now recognizes class-order changes and regenerates prompt embeddings when necessary
- **fixed** — Class weights are now preserved on underlying model during DDP training and continue to target student model correctly during knowledge distillation
- **fixed** — Fix crashes and unreliable metrics for detection, segmentation, OBB, RT-DETR, and YOLOE validation workflows caused by training augmentations
- **added** — Add OBB task header image to documentation
- **added** — Add continuous macOS CI coverage for Core AI export
- **changed** — Correct documented run paths to match actual increment_path behavior for each product
- **changed** — Architecture guide now explains scale-dependent behaviors more accurately

##### 🌟 Summary

Ultralytics `v8.4.131` adds Apple Core AI export and inference support for YOLO26, alongside important validation, training, model-configuration, and documentation improvements. 🚀

##### 📊 Key Changes

- **🍎 Apple Core AI export and inference**
  - Export models with `model.export(format="coreai")` or the equivalent CLI command.
  - Creates Apple’s `.aimodel` asset format, which can be loaded again with `YOLO("yolo26n.aimodel")`.
  - Supports FP32 and optional FP16 export through the new `export-coreai` dependency group.
  - Adds a dedicated Core AI backend, metadata handling, API references, export-table support, and continuous macOS CI coverage.
  - Supports YOLO26 models on **Apple silicon with macOS 26 or later**; exported assets target iOS 27 and macOS 27.
  - Core AI export currently has important limitations: fixed input size, no dynamic shapes or NMS export, and no support in the Ultralytics iOS or Flutter SDKs yet.

- **⚡ Core AI deployment options**
  - YOLO26’s end-to-end head is exported by default, returning finished detections directly.
  - Exporting with `end2end=False` produces raw predictions and can significantly reduce inference latency when post-processing is handled on the host.
  - Core AI export includes model metadata such as class names, stride, and task information inside the `.aimodel` asset.

- **✅ More reliable validation with `split=train`**
  - Validation now consistently uses the unaugmented validation pipeline instead of accidentally applying training augmentations such as Mosaic, MixUp, and Random Perspective.
  - Dataset fractions are now selected according to the requested split.
  - This fixes crashes and unreliable metrics for detection, segmentation, OBB, RT-DETR, and YOLOE validation workflows.

- **🧮 Correct YOLO26 loss terminology**
  - Documentation and logging now distinguish YOLO26’s `l1_loss` from `dfl_loss` used by models with distribution-based box regression.
  - Training guides, default configuration comments, tuning tables, experiment trackers, and tutorial output have been updated accordingly.

- **🎯 Improved model configuration handling**
  - Model-scale overrides in `parse_model` now match exact scale letters, preventing unscaled or dictionary-based configurations from taking the wrong architecture branch.
  - `C3k2` configurations without an explicitly provided optional argument no longer fail for medium, large, or extra-large variants.
  - The architecture guide now explains these scale-dependent behaviors more accurately.

- **🔤 YOLOE class reordering fixes**
  - `YOLOE.set_classes()` now recognizes class-order changes and regenerates prompt embeddings when necessary.
  - Reordering classes therefore updates class IDs and names correctly instead of being treated as a no-op.

- **⚖️ Training robustness improvements**
  - Class weights are now preserved on the underlying model during DDP training and continue to target the student model correctly during knowledge distillation.
  - Fine-tuning guidance now recommends non-zero warmup while clarifying that the full three-epoch default is not always necessary.
  - Documentation now accurately describes automatic optimizer selection and module-name-based layer freezing.

- **📟 Better progress bars in notebooks and narrow terminals**
  - TQDM output no longer disappears in zero-width pseudo-terminals such as those used by Colab.
  - Notebook output is allowed to scroll naturally, while truncated terminal lines now show an ellipsis instead of being silently cut off.

- **📚 Documentation and presentation updates**
  - Corrects documented YOLOE and YOLOv5 run paths to match actual `increment_path` behavior.
  - Adds the missing OBB task header image.
  - Expands and updates Apple Core AI integration guidance, including deployment limitations and Core ML recommendations.

##### 🎯 Purpose & Impact

- **Apple developers gain a new native deployment path** for YOLO26 models on the latest Apple silicon platfor

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