Update ultralytics to 8.4.20 by pyup-bot · Pull Request #673 · DagsHub/client

pyup-bot · 2026-03-05T14:38:09Z

This PR updates ultralytics from 8.3.47 to 8.4.20.

Changelog

8.4.20

📊 Key Changes
- **(Most important) Ray Tune cleanup in current PR 23772** 🧹  
- Removed hardcoded `tuner_callbacks` and dropped built-in W&amp;B callback wiring from `RunConfig` in tuning.
- Version bumped from `8.4.19` → `8.4.20`.

- **RKNN export reliability improved (23802)** 📦  
- Added ONNX version guard (`onnx&lt;1.19.0`) and enforced ONNX opset cap (`&lt;=19`) for RKNN conversion.
- Explicitly supports smoother export paths for **YOLO26** models on Rockchip toolchains.

- **FastSAM prompt accuracy fix (23766)** 🎯  
- CLIP prompting now masks non-target neighboring segments before scoring.
- Reduces false positives in overlapping/contained regions (for example, object-inside-background cases).

- **ByteTracker consistency update (23771)** 🛠️  
- Optional score fusion now also applies in second-stage association (when `fuse_score=True`), aligning behavior across tracking stages.

- **Better YAML error messages (23767)** ✅  
- Failed YAML loads now return clearer syntax errors with file context and validation guidance, instead of ambiguous fallback failures.

- **Jetson/JetPack 6 stack refresh (23788 + 23801)** 🤖  
- Updated Jetson Docker/runtime stack to newer Torch/Torchvision/ONNX Runtime GPU versions.
- Compatibility checks now recognize `torch 2.10` + `torchvision 0.25`.

- **Docs and platform improvements** 📚  
- New SAM vs YOLO segmentation benchmark section includes **YOLO26n-seg** comparisons (23782).
- Platform docs expanded for **pose skeleton templates** and **dataset versioning** workflows (23787, 23796).
- YOLOE tutorial embed updated (23798).
- CI/workflow dependency/auth updates (23763, 23799).

🎯 Purpose &amp; Impact
- **More robust tuning workflows** ⚙️  
- By removing tightly coupled callback logic, Ray Tune runs are less fragile in mixed environments (especially distributed tuning setups).

- **Fewer deployment surprises** 🚀  
- RKNN export safeguards reduce version mismatch errors and manual troubleshooting for edge deployment users.

- **Higher output quality in segmentation/tracking** 👀  
- FastSAM text prompts become more trustworthy in crowded scenes.  
- ByteTracker behavior is more consistent when confidence fusion is enabled.

- **Smoother setup for modern environments** 🔧  
- Jetson and PyTorch compatibility updates reduce install/runtime friction for developers upgrading dependencies.

- **Better onboarding and reproducibility** 🧠  
- Clearer YAML errors help users fix configs faster.  
- Platform dataset versioning and skeleton-template docs improve team workflows and repeatable training.

What&#x27;s Changed
* Use github.Auth.Token in merge action by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23763
* Better YAML error introspection by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23767
* Fix for 23765 FastSAM CLIP prompting by orcinus in https://github.com/ultralytics/ultralytics/pull/23766
*  Apply  to ByteTracker second association by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23771
* Update `sam-3.md` speed comparison by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23782
* Platform Skeleton templates docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23787
* Update JetPack 6 Docker image with `torch 2.10` by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23788
* Platform dataset versioning docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23796
* Bump docker/login-action from 3 to 4 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23799
* Add https://youtu.be/JcZsqUc8PMM to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23798
* Update `torch` and `torchvision` compatibility table to latest by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23801
* Fix RKNN exports to support YOLO26 models by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23802
* `ultralytics 8.4.20` Remove redundant hardcoded `tuner_callbacks` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23772

New Contributors
* orcinus made their first contribution in https://github.com/ultralytics/ultralytics/pull/23766

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.19...v8.4.20

8.4.19

📊 Key Changes
- **🔴 Most important (current PR 23761 by glenn-jocher): Platform training `model_id` fix**
- Training callbacks now **consistently pass `model_id`** during model uploads and training events.
- Platform callback state was refactored into a single `trainer.platform` context (instead of scattered trainer fields), including:
 - `model_id`
 - last upload time
 - cancellation status
 - console/system loggers
- Upload and event flow was tightened for better session continuity, cancellation handling, and final result linking.
- Platform URL handling now better supports server-adjusted model slugs (for example when names auto-increment).

- **🧠 SAM prediction cleanup (23751 by Laughing-q)**
- Added NMS in `SAM3SemanticPredictor` postprocessing and feature-inference paths.
- Reduces duplicate overlapping detections and keeps boxes/masks aligned.

- **🛡️ PSA attention edge-case fix (23758 by Y-T-G)**
- Prevents zero attention heads in `PSABlock` for very small channel configs.
- Improves robustness for lightweight/custom model scales.

- **📘 Docs clarification for end-to-end models (23720 by raimbekovm)**
- Clarifies that in end-to-end mode (YOLO26, YOLOv10), `max_det` and `agnostic_nms` are supported directly.
- Notes that setting `end2end=False` is mainly for enabling traditional `iou`-based NMS behavior.

- **⚙️ CI maintenance**
- GitHub Actions artifact upload/download actions were bumped to newer major versions (23748, 23749).
- African Wildlife dataset tutorial video embed updated in docs (23757). 🎥

🎯 Purpose &amp; Impact
- **For Ultralytics Platform users:** ✅  
Training runs should now be **more reliable to track end-to-end**, with fewer mismatches between uploaded checkpoints and the correct model session. This is the biggest practical improvement in this release.

- **For production and team workflows:** 📡  
Better callback state management means cleaner event handling, better cancellation behavior, and improved confidence that results and artifacts land in the right place.

- **For segmentation users (SAM):** 🎯  
Cleaner predictions with fewer duplicate overlaps can improve downstream usability and visual quality.

- **For custom/small models:** 🧱  
Fewer architecture edge-case failures when using very small widths or low channel counts.

- **For all users reading docs:** 📚  
Clearer expectations around end-to-end inference/validation args in YOLO26 and YOLOv10 reduce confusion and setup mistakes.

What&#x27;s Changed
* Document `max_det` and `agnostic_nms` support in end2end mode by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23720
* Bump actions/download-artifact from 7 to 8 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23749
* Bump actions/upload-artifact from 6 to 7 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23748
* Add NMS to `SAM3SemanticPredictor` to eliminate overlapping boxes by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23751
* Add https://youtu.be/EXYB-dbgJjY to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23757
* Fix division by zero in PSABlock `num_heads` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23758
* `ultralytics 8.4.19` Platform training pass model ID fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23761


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.18...v8.4.19

8.4.18

🌟 Summary
Ultralytics `v8.4.18` is a reliability-focused release that improves downloads and dependency installs, while also adding stronger ExecuTorch export support (including Pose) for smoother edge/mobile deployment 🚀.

📊 Key Changes
- **🔧 Priority fix (current PR): `safe_download()` now handles URLs with spaces** by encoding spaces as `%20` (PR 23736 by glenn-jocher).
- Prevents broken downloads from links like file paths or hosted assets containing spaces.
- **📱 Major export upgrade: ExecuTorch refactor + first-class utility module** (PR 23734 by lakshanthad).
- New `ultralytics/utils/export/executorch.py` with reusable `torch2executorch()` and `executorch_wrapper()`.
- Export pipeline now uses this shared utility.
- Added Pose-safe decoding patch for better XNNPACK compatibility.
- Benchmarks no longer block Pose + ExecuTorch inference.
- New reference docs page for ExecuTorch export.
- **📦 Dependency handling improvement for `git+` requirements with version constraints** (PR 23737 by fcakyon).
- Fixes invalid install behavior by cleaning git URL requirement strings before install.
- **📒 Notebook/API modernization** (PR 23745 by ahmet-f-gumustas).
- Object tracking notebook updated from deprecated `Annotator.seg_bbox()` to modern `Results.plot(color_mode=&quot;instance&quot;)`.
- **📚 Docs updates**
- OBB docs now point to a newer YOLO26 tutorial video (PR 23738 by RizwanMunawar).
- Vercel Web Analytics added to docs site template for usage insights (PR 23746 by glenn-jocher).

🎯 Purpose &amp; Impact
- **More reliable downloads for everyone** ✅  
The `safe_download()` fix is small but high-impact: fewer random failures when fetching models/assets from imperfect URLs.
- **Better edge deployment path** 📲  
ExecuTorch export is now cleaner, more maintainable, and better for Pose workflows—helpful for teams deploying YOLO26 on mobile/embedded targets.
- **Fewer setup headaches** 🧩  
Git-based dependency auto-update now works more as users expect, reducing installation friction in custom environments.
- **Examples stay working and easier to follow** 🎥  
Notebook/API updates reduce breakage from old methods and keep learning materials aligned with current Ultralytics usage.
- **Docs quality can improve faster over time** 📈  
Analytics and refreshed tutorials help the team prioritize what users need most.

What&#x27;s Changed
* Add version constraint support for git URL requirements by fcakyon in https://github.com/ultralytics/ultralytics/pull/23737
* Refactor ExecuTorch export to `executorch.py` by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23734
* Fix outdated `Annotator.seg_bbox` in object tracking notebook by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23745
* Add https://youtu.be/128JhhR2DlM to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23738
* Docs analytics by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23746
* `ultralytics 8.4.18` `safe_download()` URLs with spaces by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23736


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.17...v8.4.18

8.4.17

📊 Key Changes
- **NDJSON dataset re-split support (priority change)** ♻️🧹  
- Detects when the dataset output folder already exists and **reuses previously downloaded images** when you change `train/val/test` splits.  
- For non-classification tasks (detect/segment/pose), **removes the existing `labels/` directory** before reconversion to prevent stale annotations.  
- If an image is missing from the current split, it will **move (rename) it from another split** (train/val/test) *before* downloading from the URL.  
- Adds a **content hash** (ignoring signed/rotating URLs) stored in `data.yaml` to **skip reconversion when nothing meaningful changed** 🧾🔒  
- **Deletes orphaned images** that are no longer part of the dataset after a resplit, reducing “stale background files” that can silently affect training 🗑️
- **More reliable EdgeTPU exports** 🧩⚙️  
- Automatically **disables `end2end` mode for EdgeTPU** exports (and logs a warning), aligning EdgeTPU with other limited backends that don’t support required ops.
- **OpenVINO INT8 export dependency handling improved** 📦✅  
- Adds PyTorch **2.3 detection** and adjusts `nncf` requirements to reduce install/export conflicts across Torch versions.
- **Clearer disk space error messages** 💾🔎  
- Fixes incorrect “GB” reporting and now shows **MB for sizes under 1GB**, making download/asset errors easier to understand.

🎯 Purpose &amp; Impact
- **Faster dataset iteration for NDJSON workflows** ⚡  
Resplitting a dataset no longer forces full re-downloads—saving time, bandwidth, and friction when you’re refining splits during curation.
- **Fewer training gotchas from stale files** 🧼  
Clearing old labels and removing orphaned images helps prevent subtle mismatches (images/labels out of sync) that can degrade training quality.
- **More dependable deployment exports** 📤  
EdgeTPU exports should fail less often due to unsupported `end2end` behavior, and OpenVINO INT8 export setups are smoother across PyTorch versions.
- **Better user experience when storage is low** 🧰  
Disk space errors now communicate real numbers, reducing confusion during downloads and conversions.

What&#x27;s Changed
* Fix incorrect disk space in error message by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23727
* Pin `nncf&lt;3` for PyTorch 2.2 and below by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23726
* Disable `end2end` for EdgeTPU by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23724
* `ultralytics 8.4.17` NDJSON dataset re-split support by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23735


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.16...v8.4.17

8.4.16

📊 Key Changes
- 🪟🖥️ **Cross-platform `.pt` loading fix (PR 23725 by glenn-jocher):**
- Updated `torch_safe_load()` to **remap `pathlib.WindowsPath` ↔ `pathlib.PosixPath`** during unpickling, preventing common load failures when models are created on one OS and loaded on another.
- 🧵 **Checkpoint metadata made safer/more consistent:**
- `model.pt_path` is now stored as a **plain string** (`str(weight)`), not a `Path` object (applies in general checkpoint loading and YOLO NAS model wrapper code).
- 🔢 **Version bump:** `8.4.15` → `8.4.16`.
- 📸 **Docs + format clarity (PR 23717):**
- Docs now reflect that **HEIC/HEIF uses `pi-heif` (auto-installed on first use)** and **AVIF is supported natively by Pillow**.
- `.heif` is explicitly listed among supported image formats.

🎯 Purpose &amp; Impact
- ✅ **Fewer “can’t unpickle WindowsPath/PosixPath” errors** when sharing trained `.pt` weights between teammates or CI systems running different operating systems.
- 🔁 **More reliable export and tooling workflows** that depend on `model.pt_path` (strings behave consistently across platforms and serialization).
- 👥 **Better collaboration &amp; reproducibility** for teams training on Linux servers and deploying/testing on Windows machines (or vice versa).
- 📸 **Smoother HEIC/HEIF user experience** thanks to clearer docs and explicit `.heif` support visibility (less dependency confusion).

What&#x27;s Changed
* Update HEIC/HEIF dependency info and add .heif format to docs by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23717
* `ultralytics 8.4.16` Windows&lt;&gt;Linux model compatibility fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23725


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.15...v8.4.16

8.4.15

📊 Key Changes
- **HEIC/HEIF image support (priority change)** 🖼️📱  
- Added `ultralytics.utils.patches.image_open` and **monkey-patched `PIL.Image.open`** to lazily enable HEIC/HEIF decoding via **`pi-heif` only when needed** (i.e., on first decode failure) ⏱️  
- Expanded supported extensions to include **`.heif`** (in addition to `.heic`) ✅  
- Removed HEIC-specific loading code from dataset loaders (now unified through the standard image reading pathway) 🧹
- **Better plotting for non‑Latin text** 🈶🖋️  
- Improves matplotlib defaults by prioritizing **Arial Unicode MS** and auto-registering `.ttf` fonts from the Ultralytics user config directory for stronger multilingual rendering.
- **EXIF auto-rotation fix for list-of-path sources** 🔄🖼️  
- Applies `ImageOps.exif_transpose()` when loading images from **lists of paths/URLs**, reducing “sideways phone photo” issues.
- **Validation/metrics correctness fix (important for accuracy reporting)** 🎯📈  
- Fixes SciPy-based matching (`match_predictions`) to **maximize IoU** during assignment (previously could choose the worst matches), which can affect TP/FP classification and **mAP when `use_scipy=True`**.
- **IMX (Sony IMX500) export improvements** 📦🤖  
- **Enables IMX export on Linux ARM64** (removes an ARM64 block), adds dependency support, and improves test gating.  
- Adds **segmentation** as a supported IMX export/benchmark task ✅
- **Usability improvement: `Results.save()`** 💾📁  
- Adds `create_dir` parameter (default `True`) to control auto-creation of parent directories when saving annotated outputs.
- **Dev/CI reliability updates** 🧪🏗️  
- CI now tests newer PyTorch versions (2.8/2.9) and makes export tests less flaky by skipping TFLite export tests unless `torch&gt;=1.13` on Linux.

🎯 Purpose &amp; Impact
- **More “it just works” image input** 🖼️✅  
- HEIC/HEIF (common on iPhones) can be used directly for training/inference with fewer manual conversions, while lazy loading avoids paying the dependency cost unless you actually need it.
- **Fewer confusing orientation bugs** 📱➡️🖥️  
- EXIF transpose handling reduces rotated/sideways images that can break visualization, training data review, and inference outputs.
- **More trustworthy evaluation metrics** 📈🧠  
- The IoU matching fix ensures SciPy-based evaluation behaves as intended, preventing artificially worse results or incorrect TP/FP assignment when that path is enabled.
- **Better edge/export support on ARM** 🧩🚀  
- IMX export on ARM64 + segmentation support expands deployment options for embedded and edge workflows.
- **Smoother workflows &amp; sharing outputs** 💾✨  
- `Results.save(create_dir=True)` makes saving predictions into new folders simpler (fewer “directory not found” errors).
- **Improved global documentation and plots** 🈶📚  
- Better multilingual font defaults help teams with non‑Latin labels produce clearer charts/figures.

If you’re deploying/training with mixed photo sources (phones, HEIC libraries, multilingual labels), **8.4.15** is a practical upgrade 🛠️🚀

What&#x27;s Changed
* Add guide on customizing the YOLO trainer by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23579
* Add YOLO26 vs YOLO11 performance charts for Raspberry Pi 5 by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23625
* test: 🧪 Add TFLite export requirement for Torch version 1.13 by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23618
* Fix typos and grammar errors in documentation by him2him2 in https://github.com/ultralytics/ultralytics/pull/23617
* feat: improve `Results.save()` with pathlib and optional directory creation by ShuaiLYU in https://github.com/ultralytics/ultralytics/pull/23592
* fix: 🐞 add ExecuTorch version during model export into logging by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23620
* ci: 👷 move Conda Builds CI to monitor from conda-forge/ultralytics-feedstock to ultralytics  repo by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23600
* Add https://youtu.be/GAFlmuk0fZI to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23624
* Apply EXIF transpose when list of paths is used as source by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23631
* Fix scipy matching in `match_predictions` to maximize IoU by Mr-Neutr0n in https://github.com/ultralytics/ultralytics/pull/23634
* Enable IMX export for ARM64 by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23636
* Enable segmentation models in IMX benchmarks by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23635
* Fix: get the correct package name for `metadata.distribution` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23641
* docs: 📝 update hyperlink for hyperparameter tuning in custom trainer guide by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23644
* Fix broken YOLO26 documentation link in vision-eye guide by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23646
* Fix docstrings by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23648
* docs: 📝 fix conda badge link in CI documentation by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23661
* docs: 📝 remove unneeded tokenizer_ve documentation file by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23662
* Delete orphan markdown files in `build_reference.py` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23666
* docs: 📝 remove non-existed dataset generation section from COCO12-Formats documentation by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23667
* Simplify `CustomSaveTrainer` example in custom trainer guide by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23668
* Add custom trainer guide cross-reference in train.md by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23649
* Add PyTorch 2.8 and 2.9 tests to slow CI by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23683
* Update platform docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23688
* Update Platform docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23692
* Added max concurrency 16 to link checker workflow for the HTTP/2 error by banica-ultralytics in https://github.com/ultralytics/ultralytics/pull/23690
* Add https://youtu.be/pJLXmhyuHzA to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23658
* Platform Docs screenshots by sergiuwaxmann in https://github.com/ultralytics/ultralytics/pull/23697
* Add https://youtu.be/cxU5E2SkivU to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23701
* Update platform API docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23704
* Platform Rate Limit docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23706
* Fix uv conflicts between export and solutions extras by fcakyon in https://github.com/ultralytics/ultralytics/pull/23686
* Add YOLOE agnostic_nms default behavior note by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23707
* Document CUDA OOM auto-retry in batch-size settings by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23708
* Document Results.save() automatic directory creation by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23709
* Banica ultralytics http2 changes undone by banica-ultralytics in https://github.com/ultralytics/ultralytics/pull/23712
* `ultralytics 8.4.15` HEIC/HEIF image support by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23714

New Contributors
* him2him2 made their first contribution in https://github.com/ultralytics/ultralytics/pull/23617
* banica-ultralytics made their first contribution in https://github.com/ultralytics/ultralytics/pull/23690

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.14...v8.4.15

8.4.14

📊 Key Changes
- **Platform-driven training cancellation (priority change)** 🛑  
- Detects cancellation **even before training starts** (during session registration) and prevents wasted startup time.
- Checks for cancellation at **epoch end** via a send-and-check response flow, then sets `trainer.stop=True` when `cancelled=true`.
- Tracks cancellation state with `trainer._platform_cancelled` and logs clear messages.
- Continues to **upload partial artifacts/metadata** when a run is cancelled (so you don’t lose everything).
- **Faster responsiveness to stop requests between batches** ⚡  
- Training loop now breaks if `self.stop` is set, allowing external stops (like Platform cancellation) to take effect sooner.
- **YOLO26 Pose training stability fix** 🧍‍♂️📈  
- Clamps negative `rle_loss` to zero to prevent loss from going negative and destabilizing training (helps avoid mAP dropping across epochs).
- **Segmentation → bounding box conversion edge-case fix** 🖼️📦  
- `segment2box()` now excludes points exactly on the image border after clipping, avoiding boxes incorrectly snapping to edges (better box regression + mask quality).
- **Hyperparameter tuning crash fix** 🧪🔧  
- Prevents `TypeError` when `fitness` is present but `None` during tuning (safer/cleaner tuning runs).
- **Docs clarity: Params/FLOPs reporting after `model.fuse()`** 📚  
- Adds notes explaining why Params/FLOPs in docs may differ from what you see locally (fused inference vs full training architecture).

🎯 Purpose &amp; Impact
- **Better control on Ultralytics Platform** 🎛️  
- If you hit **Cancel** in the Platform, training should stop reliably and promptly—without waiting for a long delay.
- **Lower wasted compute + cost** 💸  
- Cancelled jobs exit earlier and more predictably, especially helpful for long training runs.
- **Keeps useful outputs even when cancelling** 📦  
- Partial uploads mean you can still inspect progress, logs, and intermediate artifacts after stopping.
- **More stable YOLO26 Pose training** ✅  
- Reduces risk of training “going backwards” due to negative loss behavior; improves consistency of pose metrics over epochs.
- **Higher-quality results for segmentation-derived boxes** 🎯  
- Fewer edge-snapped boxes after augmentations can improve both training quality and final predictions.
- **Fewer interruptions in automated workflows** 🤖  
- Tuning and reporting are more robust, reducing flaky failures in pipelines.

If you train via the **[Ultralytics Platform](https://platform.ultralytics.com)**, this release is especially impactful due to the new cancellation behavior 🛑.

What&#x27;s Changed
* Fix TypeError when fitness is None during hyperparameter tuning by Mr-Neutr0n in https://github.com/ultralytics/ultralytics/pull/23603
* Clamp negative rle_loss to zero in Pose26 training by Mr-Neutr0n in https://github.com/ultralytics/ultralytics/pull/23604
* Fix `segment2box` boundary condition for edge-snapped segment points by Mr-Neutr0n in https://github.com/ultralytics/ultralytics/pull/23602
* Add footnote clarifying param/FLOP counts after `model.fuse()` by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23601
* `ultralytics 8.4.14` Platform training cancel feature by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23614


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.13...v8.4.14

8.4.13

📊 Key Changes
- **Auto-retry on CUDA OOM during training (major change)** 🔥🛠️  
- If a CUDA OOM happens in the **first epoch on single-GPU**, Ultralytics will retry up to **3 times**, **halving the batch size each time** (down to 1).  
- Training pipeline is rebuilt after batch reduction (dataloaders + optimizer + scheduler) to continue cleanly.
- **New internal training helper** 🧩  
- Adds a `_build_train_pipeline()` method to rebuild loaders/optimizer/scheduler when batch size changes (used by the new OOM recovery flow).
- **More reliable ONNX export for OBB + NMS** 📦✅  
- When exporting **OBB (oriented bounding boxes)** to ONNX **with NMS enabled**, `simplify=True` is now forced to avoid a known runtime issue (TopK-related error in some ONNX Runtime versions).
- **DGX system detection + TensorRT handling** 🖥️⚙️  
- Adds `is_dgx()` detection and uses it (along with Jetson JetPack 7) to trigger a TensorRT version check/reinstall path for better export reliability on those systems.
- **Packaging stability fix: pin setuptools** 🧰🔒  
- Pins build requirements to `setuptools&lt;=81.0.0` to avoid breakages introduced by newer setuptools versions (notably affecting `tensorflow.js` export tooling).
- **Docs &amp; examples refresh (YOLO26 messaging + tracking content)** 📚🎥  
- Tracking docs now embed a newer multi-object tracking video featuring **YOLO26** + BoT-SORT/ByteTrack.
- Exporter docs/examples updated to show **YOLO26** (`yolo26n.pt`) and mention ExecuTorch/Axelera export options (documentation signposting).
- **Example dependency update** 🔄  
- Updates `protobuf` in the RT-DETR ONNX Runtime Python example.

🎯 Purpose &amp; Impact
- **Fewer training crashes for everyday users** 🙌🔥  
- If you start training with a batch size that’s slightly too large for your GPU, Ultralytics can now **self-correct and continue** instead of failing immediately—especially helpful for beginners and for “first-epoch spikes” in memory use.
- **Less manual trial-and-error** 🎯  
- Reduces the common loop of “OOM → lower batch → restart training,” saving time and frustration.
- **More dependable deployment exports** 🚀  
- ONNX exports for OBB models with embedded NMS should work **more reliably out of the box**, with fewer runtime surprises.
- **More predictable builds/CI** 🧱  
- Pinning setuptools helps prevent sudden packaging/tooling failures across environments.
- **Clearer guidance aligned with YOLO26** 🧭  
- Docs and examples increasingly steer users toward **YOLO26** as the recommended model for training, tracking, and export workflows.

What&#x27;s Changed
* feat: 🚀 NVIDIA DGX device variants check by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23573
* Add https://youtu.be/qQkzKISt5GE to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23582
* Bump protobuf from 6.31.1 to 6.33.5 in /examples/RTDETR-ONNXRuntime-Python in the pip group across 1 directory by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23572
* docs: 📝 exporter documentation for new model formats and examples updated by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23585
* Force `simplify=True` for OBB export with NMS by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23580
* Pin `setuptools` version by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/23589
* `ultralytics 8.4.13` Retry smaller batch on training CUDA OOM by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23590


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.12...v8.4.13

8.4.12

📊 Key Changes (major updates)
- **YOLOE-26: Skip redundant `set_classes()` when prompts already match** 🧠⚡️  
- Adds a guard in `ultralytics/models/yolo/model.py` to **avoid re-running `set_classes()`** if `model.names` already matches the requested `classes`.  
- Only regenerates text embeddings (`get_text_pe(classes)`) **when a real class change is needed**.  
- Ensures the **predictor stays in sync** by setting `self.predictor.model.names = self.model.names`.
- **More robust GPU auto-selection under multi-process starts** 🎲🖥️  
- Adds a random tie-break when GPUs look equally idle, reducing the chance that multiple jobs pick the same GPU at launch (`ultralytics/utils/autodevice.py`).
- **Fix disk caching for grayscale/multispectral images** 💾🖼️  
- Disk cache now respects the dataset’s OpenCV read mode (`flags=self.cv2_flag`), preventing cached `.npy` files from silently changing input format (`ultralytics/data/base.py`).
- **Fix AutoBatch for non-RGB inputs (grayscale/multispectral)** 🧪⚙️  
- AutoBatch profiling now uses the model’s configured input channels (`model.yaml[&quot;channels&quot;]`) instead of assuming 3-channel RGB (`ultralytics/utils/autobatch.py`).
- **Tests: better grayscale coverage + cleanup** 🧪🧹  
- Adds CUDA training coverage for `coco8-grayscale.yaml`, uses `cache=&quot;disk&quot;` in grayscale tests, and cleans up cached `.npy` files to prevent flaky test interactions.
- **Docs/examples updated to YOLO26 naming** 📝🚀  
- Exporter and error-message examples now reference **YOLO26** (recommended) instead of YOLO11 (e.g., `yolo26n.onnx`, `yolo26n_openvino_model`, etc.).
- **Confusion matrix plot fixes for classification** 📉✅  
- Removes unintended “background” labeling behavior for classification confusion matrices and makes tick labeling more robust for large class counts (`ultralytics/utils/metrics.py`).
- **Minor tracker cleanup** 🧩  
- Simplifies `deque` initialization in BoT-SORT tracker (`ultralytics/trackers/bot_sort.py`).
- **Docker dependency tweak** 🐳  
- Swaps `wandb` for `nvidia-ml-py` in the Dockerfile to better support NVIDIA GPU querying/metrics in container builds.

🎯 Purpose &amp; Impact (why it matters)
- **Faster, more stable YOLOE-26 inference loops** 🚀  
If you repeatedly run predictions with the same text prompts/classes, v8.4.12 avoids unnecessary reconfiguration and embedding regeneration—improving performance and reducing potential side effects.
- **More reliable multi-job GPU usage** 🖥️  
When launching multiple trainings/inference processes at once, GPU selection is less likely to “pile onto” the same device due to tie conditions.
- **Better support for grayscale/multispectral workflows** 🖤🤍  
Training with `cache=&quot;disk&quot;` and using AutoBatch on non-3-channel inputs is now more correct and consistent—reducing mismatches, mis-profiling, and possible crashes.
- **Clearer docs aligned with the recommended model** 📘  
Updated examples reduce copy/paste confusion by pointing users to **YOLO26**, the latest stable and recommended Ultralytics model family.

What&#x27;s Changed
* Round-robin GPU autoselect by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23544
* Fix disk caching with multispectral images by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23545
* Fix AutoBatch with multispectral images by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23546
* Remove cached `*.npy` files in grayscale tests by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23554
* docs: 📝 update exporter file yolo11 terms to newest yolo26 by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23556
* Remove &quot;background&quot; class from classification confusion matrix by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23547
* `ultralytics 8.4.12` YOLOE-26 skip `set_classes` if text prompts already set by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23552


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.11...v8.4.12

8.4.11

📊 Key Changes
- **Robust Platform upload + metrics retries (PR 23538 by glenn-jocher)** 🔁
- Added a new utility: `ultralytics/utils/uploads.py` with `safe_upload()` that supports **retries, timeouts, and optional progress bars** ⏳📦
- Platform callback networking hardened:
 - `_send()` now **retries transient failures**, uses a **longer timeout (30s)**, and **does not retry most 4xx client errors** (except common transient ones like timeout/rate-limit) 🛡️
 - `_upload_model()` now **retries signed-URL retrieval** and uses `safe_upload()` for the actual upload to cloud storage ☁️
- Platform now receives **only the remote cloud path (`gcsPath`)** as `modelPath` (instead of accidentally sending a local filesystem path) ✅
- Training end now uploads the best model **with a progress bar** enabled by default 📊
- Added a new reference doc page for uploads utilities and linked it into the docs nav 📚
- **Windows OpenVINO export test stability fix (PR 23528 by lakshanthad)** 🪟✅
- Ensures OpenVINO export tests use **unique output filenames** on Windows to prevent file locking/collision issues in CI 🧪

🎯 Purpose &amp; Impact
- **Fewer failed Platform syncs** during training due to flaky networks or transient API/upload hiccups 📶➡️✅  
*Impact:* your experiment tracking, logs, metrics, and model artifacts are more likely to show up correctly on the **[Ultralytics Platform](https://platform.ultralytics.com)**.
- **More reliable large checkpoint uploads** (with retries + progress feedback) 📦⏫  
*Impact:* less manual re-running/re-uploading when training finishes, especially on slower or unstable connections.
- **Cleaner, safer metadata sent to Platform** 🧹  
*Impact:* Platform gets a **downloadable cloud path**, not a meaningless local path like `C:\...` or `/home/...`.
- **Improved CI stability on Windows for OpenVINO exports** 🪟  
*Impact:* no change for most end users, but faster/cleaner releases and fewer flaky test failures.

What&#x27;s Changed
* Fix OpenVINO failures on Windows fast CI tests by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23528
* `ultralytics 8.4.11` Platform model uploads retries by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23538


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.10...v8.4.11

8.4.10

📊 Key Changes
- 🧠 **YOLOE-26 inference default change (most important):** YOLOE predictions now default to `agnostic_nms=True`, reducing overlapping duplicate boxes across different classes (set in `ultralytics/models/yolo/model.py`).
- 📦 **Version bump:** `8.4.9` → `8.4.10`.
- 🍏 **CoreML export feature:** CoreML export now correctly honors `agnostic_nms` (previously the argument existed but wasn’t applied in the CoreML NMS pipeline).
- ⚙️ **TensorRT setup reliability:** added `check_tensorrt()` helper and used it in export + tests to reduce missing-dependency failures; also adds special handling to pin compatible TensorRT on **Jetson JetPack 7 / CUDA 13 ARM** for RT-DETR exports.
- 🏎️ **OpenVINO bugfix:** fixes an `UnboundLocalError` risk by making the inference-mode check safe when `dynamic` is false.
- ☁️ **Ultralytics HUB robustness:** HUB session now raises clearer `HUBModelError` (instead of generic `ValueError` or silently returning `None`) when loading/creating HUB models fails.
- 🧩 **SAM results metadata fix:** improves how class `names` are generated (especially for single-object outputs and “visual” naming mode).
- 🛡️ **Instances safety + debugging:** avoids crashes when `segments=None` during zero-area filtering, and adds a helpful `Instances.__repr__()` for easier debugging.
- 📚 **Docs improvements:** YOLOE examples simplified to `model.set_classes([&quot;person&quot;, &quot;bus&quot;])` (no manual text embedding handling), ExecuTorch docs updated with **YOLO26** benchmarks, and Benchmark docs video link refreshed.

🎯 Purpose &amp; Impact
- 🎯 **More consistent YOLOE predictions by default:** class-agnostic NMS helps suppress duplicate overlapping detections across classes, improving “it just works” behavior for new users.
- ⚠️ **Behavior change to expect:** YOLOE outputs may differ vs. 8.4.9 (some boxes previously kept may now be suppressed). If you relied on per-class duplicates, explicitly set `agnostic_nms=False`.
- 🍏 **More predictable exports across formats:** CoreML now matches other exporters when `agnostic_nms` is enabled—fewer surprises when deploying to Apple devices.
- 🤖 **Smoother Jetson/TensorRT experience:** fewer export failures and less manual dependency wrangling, especially on newer Jetson stacks (JetPack 7).
- 🧯 **Fewer runtime errors:** OpenVINO, Instances filtering, HUB model handling, and SAM naming fixes reduce “random crash” scenarios and make failures easier to diagnose.
- 🧼 **Cleaner onboarding + examples:** YOLOE prompting is easier to copy/paste, and YOLO26 guidance in docs stays aligned with the recommended models.

You can update with: `pip install -U ultralytics` ⬆️✅

What&#x27;s Changed
* fix: 🐞 Add requirement check for TensorRT in `test_export_engine_matrix` by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23496
* Add https://youtu.be/UF7pYdLSMng to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23497
* Fix `autobackend.py` when using OpenVINO by zboszor in https://github.com/ultralytics/ultralytics/pull/23505
* Force update `tensorrt` on CUDA 13 ARM to `10.15.x` to fix a bug with RT-DETR exports by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23499
* Update `executorch` doc with YOLO26 benchmarks on Raspberry Pi 5 by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23500
* 🐞 fix `remove_zero_area_boxes` instance when segment is `None` by Bovey0809 in https://github.com/ultralytics/ultralytics/pull/23495
* Add repr for Instance Class for devlopers who needs better debugging … by Bovey0809 in https://github.com/ultralytics/ultralytics/pull/23473
* Update YOLOE docs example by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23526
* Remove debug asserts from TVP loss classes by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23523
* Improve HUB session error handling with HUBModelError by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23512
* Fix names generation for single object detection in SAM3SemanticPredictor by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23513
* CoreML Export: Add Support for Class-Agnostic NMS by K-saif in https://github.com/ultralytics/ultralytics/pull/23493
* `ultralytics 8.4.10` YOLOE-26 default `agnostic_nms=True` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23525

New Contributors
* K-saif made their first contribution in https://github.com/ultralytics/ultralytics/pull/23493
* zboszor made their first contribution in https://github.com/ultralytics/ultralytics/pull/23505

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.9...v8.4.10

8.4.9

📊 Key Changes
- 🧪 **(Priority) Segmentation CopyPaste mask fix** in `ultralytics/data/augment.py`:
- Builds the contour mask as a **2D (H×W) array** instead of matching the full image tensor shape.
- Draws contours onto a **single-channel mask** using value `1` (instead of `(1,1,1)`), avoiding channel mismatches.
- 🧾 **Version bump:** `8.4.8 → 8.4.9` 🔢
- 📦 **NDJSON datasets always re-download** when using `ul://...` URIs (instead of silently reusing a cached `.ndjson`) 🧹⬇️
- ⚙️ **ExecuTorch support improvements**:
- Removes the `&lt;2.10` PyTorch upper bound in dependencies (Windows still excludes `torch==2.4.0`) 🔓
- Centralizes ExecuTorch install checks via `check_executorch_requirements()` to reduce platform-specific install failures 🧩
- ⚡ **Training + compile coverage:** CUDA test now trains with `compile=True`, and stride calculation is simplified for more predictable behavior 🧠🔥
- 📚 **Docs refresh:** Export docs video updated to a more relevant **YOLO26 export/deployment** tutorial 🎥

🎯 Purpose &amp; Impact
- 🧩 **More robust segmentation augmentation**: prevents mask shape/channel bugs that can break or silently degrade CopyPaste-based training—especially when images are **single-channel** (common in grayscale and some hyperspectral pipelines) 📷✅
- 📈 **More reliable segmentation training**: fewer augmentation edge cases means smoother training runs and more consistent results 🏋️‍♂️📊
- 🧹 **Less “stale dataset” confusion**: always refreshing `.ndjson` metadata helps ensure you train/evaluate on the **latest Ultralytics-hosted dataset state** when using `ul://...` 🔄🧪
- 🚀 **Better export/inference readiness on modern stacks**: improved ExecuTorch requirement handling + Torch 2.10+ compatibility reduces install friction and improves deployment workflows 📦⚙️
- ⚡ **Safer `compile=True` usage**: better test coverage helps catch regressions for users relying on PyTorch compile for potential speedups 🧠🛠️

What&#x27;s Changed
* Always redownload NDJSON datasets by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23458
* feat(executorch): Add support for Torch 2.10 in ExecuTorch requirements and imports and remove torch&lt;2.10  pin by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23344
* Add missing type hints to `checks.py` and `dist.py` by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23485
* Add https://youtu.be/KGHYU-MKYeE to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23463
* Fix training with `compile` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23479
* refactor: ♻️  Simplify requirement checks for executorch by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23475
* `ultralytics 8.4.9` Hyperspectral CopyPaste augmentation by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23471


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.8...v8.4.9

8.4.8

📊 Key Changes
- **End2end now supports `max_det` + `agnostic_nms` (PR 23396 by Y-T-G)** 🎛️  
- Adds a safe `set_head_attr(**kwargs)` helper to set head/last-layer attributes like `end2end`, `max_det`, `agnostic_nms` 🧩  
- When `end2end=True`, **`predict` and `val` now push `max_det` + `agnostic_nms` into the model head**, so these flags *actually take effect* ⚙️  
- Adds **`agnostic_nms` support inside the `Detect` head** (affects top-k selection logic used in end-to-end mode) 🎯  
- Export pipeline also carries `agnostic_nms` into the exported head for better parity between Python and exported models 📦
- **Clearer `end2end` controls across CLI/docs** 📚  
- Adds `end2end` to config/defaults and docs for predict/val/export args 🧾  
- Expands tests to run export/predict/val with `end2end=True/False` ✅🧪
- Notable additional fixes included in this tag (smaller but impactful) 🛠️  
- **Export reliability:** exporter auto-disables end2end for formats that don’t support required ops (RKNN/NCNN/ExecuTorch/Paddle/IMX) and stores `end2end` in metadata 🧰  
- **YOLOE/YOLO-World training usability:** allows passing a YAML file (or `Path`) for multi-source data configs 🗂️  
- **Docs improvements:** new **Compare** section + cleaner navigation; sitemap auto-fills missing pages 🧭  
- **Stability fixes:** rotated-OBB small-box robustness, DDP unwrap fixes for pose validation, PyTorch 1.9 compatibility adjustment, OpenVINO/ONNX example bugfixes 🔧

🎯 Purpose &amp; Impact
- **More predictable end-to-end inference results** 🧠  
- `max_det` now reliably limits outputs in end2end mode (no more “why didn’t it cap?” confusion).
- **Better class-agnostic behavior when you need it** 🧹  
- `agnostic_nms` becomes meaningful in end2end workflows, helpful when classes overlap heavily or you want “best boxes regardless of class”.
- **Closer parity between Python and exported deployments** 🚚  
- Exported models are less likely to behave differently from local inference due to missing head settings.
- **Fewer surprises across formats &amp; environments** ✅  
- Safer exporting to constrained runtimes, more robust training/validation in edge cases, and improved docs discoverability for new users.

If you’re using **YOLO26** (the recommended model family) in **end-to-end mode**, this release is a practical quality-of-life upgrade. 🚀🙂

What&#x27;s Changed
* Update optimizer selection strategy by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23350
* Fix broken links in NVIDIA Jetson documentation by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23362
* Add ability to override `end2end` mode by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23333
* Update small obb candidates selection by lmycross in https://github.com/ultralytics/ultralytics/pull/23378
* Update `coco12-formats` predict tests by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23374
* fix openvino sample for coordinates by jules-ai in https://github.com/ultralytics/ultralytics/pull/23366
* Fix index issue of strides in `select_candidates_in_gts` by PT0X0E in https://github.com/ultralytics/ultralytics/pull/23369
* Add note about SAM3 tokenizer error due to incorrect `clip` package by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23368
* Fix `stride_val` type issue in STAL by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23390
* Export non-e2e version by default for `rknn/executorch/paddle/imx` formats by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23312
* fix: add unwrap_model to fix DDP AttributeError by lmycross in https://github.com/ultralytics/ultralytics/pull/23392
* Fix: use `torch.no_grad` for `torch==1.9.0` to resolve training issue by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23395
* Simplify Docs nav by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23398
* Fix swapped input_height and input_width in ONNX example - Fixes 23126 by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23402
* Add models compare pages to Docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23406
* Fix visual prompt training for YOLOE26 by ShuaiLYU in https://github.com/ultralytics/ultralytics/pull/23401
* Add https://youtu.be/7lZa3Yi2kbo to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23388
* Classify train fixes by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23420
* Fix/yoloe text model attribute in yoloe trainer from scratch by ShuaiLYU in https://github.com/ultralytics/ultralytics/pull/23428
* Fix names generation in SAM3VideoSemanticPredictor by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23434
* Update NVIDIA Jetson Thor benchmarks with YOLO26 by lakshanthad in https://github.com/ultralytics/ultralytics/pull/23443
* Save `close_mosaic` as integer in `best_hyperparameters.yaml` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23435
* Feat: support multiple data config via yaml for YOLOE training by ShuaiLYU in https://github.com/ultralytics/ultralytics/pull/23427
* Fix YOLOE training test by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23448
* Fix SMTP port parameter syntax in SecurityAlarm by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23453
* Disable shuffle only if batch shapes mismatch with `rect=True` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23419
* `ultralytics 8.4.8` Support `max_det` and `agnostic_nms` for end2end by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23396

New Contributors
* PT0X0E made their first contribution in https://github.com/ultralytics/ultralytics/pull/23369

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.7...v8.4.8

8.4.7

📊 Key Changes
- **AVIF is now a supported training image format** ✅📷  
- `IMG_FORMATS` expanded to include **`avif`**
- **More robust image decoding for modern formats** 🛡️  
- Adds a **Pillow-based fallback image reader** (`_imread_pil`) for cases where **OpenCV can’t decode AVIF/HEIC**
- **New “COCO12-Formats” dataset + generator script** 🧰  
- A tiny 12-image dataset covering **all supported extensions** (AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WebP)
- Includes a **dataset YAML** and a **generator** (`generate_coco12_formats.py`) to build the set for testing/validation
- Adds a dedicated docs page and links it in the dataset index 📚
- **Training and tooling reliability improvements** 🔧  
- Fixes loss image-size calculation for **OBB and Pose26** multi-scale training 📐
- Fixes **semantic segmentation masking/indexing** for overlapping instances 🧩
- More accurate **optimizer parameter-group logging** 🧾
- Adds **retry + validation** for dataset image downloads (NDSON) 🔁📥
- **Install/export stability &amp; experiment tracking** 📦📈  
- Pins **torch &lt; 2.10** to prevent ExecuTorch export incompatibilities 🧷
- Saves runs under `runs/` by default for relative projects + improves **W&amp;B traceability** 🗂️
- **Docs updates (large)** 📝⚡  
- Docs images migrated to a CDN and improved alt text for accessibility/SEO

🎯 Purpose &amp; Impact
- **Train directly on AVIF images** without manual conversion 🎉  
- Useful for modern web/phone pipelines where AVIF is common
- **Fewer “it works on my machine” image-loading failures** 🧯  
- The new COCO12-Formats dataset is designed to catch format-specific decoding issues early, especially in CI/CD
- **Better resilience across environments** 🌍  
- Pillow fallback reduces dependence on how OpenCV was built (many prebuilt OpenCV wheels lack AVIF support)
- **More reliable training &amp; clearer debugging** 🔍  
- Fixes reduce subtle scaling bugs (Pose/OBB), segmentation masking issues, and misleading optimizer logs
- **Smoother installs/exports and cleaner run management** 🚀  
- Dependency pin helps prevent breakage; run folder changes improve organization and experiment tracking

What&#x27;s Changed
* Fix docs images content disposition by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23322
* Docs images cleanup by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23323
* Fix the length of optimizer params group print by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23314
* Platform docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23338
* Route outputs to `runs/` by default and improve W&amp;B run traceability by artest08 in https://github.com/ultralytics/ultralytics/pull/23026
* fix image size calculation in OBB and Pose26 loss for multi-scale training by lmycross in https://github.com/ultralytics/ultralytics/pull/23342
* fix: 🐞 pin torch version to &lt;2.10 for executorch export by onuralpszr in https://github.com/ultralytics/ultralytics/pull/23353
* fix semantic segmentation loss indexing error by lmycross in https://github.com/ultralytics/ultralytics/pull/23330
* Retry NDSON download failures by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23354
* Fix `multi_scale` range sampling and clarify docs by artest08 in https://github.com/ultralytics/ultralytics/pull/23284
* `ultralytics 8.4.7` AVIF training and new COCO12-Formats dataset by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23358

New Contributors
* artest08 made their first contribution in https://github.com/ultralytics/ultralytics/pull/23026

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.6...v8.4.7

8.4.6

📊 Key Changes
- **✅ Fixed DDP multi-GPU training crash (PR 23301 by pfabreu)**  
- Added missing `PosixPath` import to the generated temporary DDP training script (`ultralytics/utils/dist.py`).  
- Prevents `NameError: name &#x27;PosixPath&#x27; is not defined` when model paths are passed as `PosixPath` objects (common on Linux) during Distributed Data Parallel training 🧩🧯
- **⏱️ More robust dataset URI resolution for large Ultralytics HUB/Platform datasets (PR 23305 by glenn-jocher)**  
- `ul://...` dataset URLs now allow a much longer server-side preparation time (NDJSON generation) to avoid premature timeouts 🐢📦
- **🧹 Developer quality: BYTETracker typing modernized (PR 23304)**  
- Switched legacy ` type:` comments to inline type annotations in `BYTETracker` (no behavior change) 🧠🔧
- **📚 Docs + Platform guidance improvements (PR 23294, 23298)**  
- Added new docs for **Activity Feed** and **Trash/Restore**, refreshed **billing/GPU pricing**, added “Try on Ultralytics Platform” callouts for **YOLO11 / YOLO26 / YOLOv5 / YOLOv8**, plus small typo fixes ✍️🚀

🎯 Purpose &amp; Impact
- **DDP training is stable again on multi-GPU setups** 🎛️✅  
- If you upgraded to 8.4.5 and saw failures when launching DDP training (especially with models like `yolo26n.pt` passed as a `Path/PosixPath`), this release removes that blocker—less downtime, fewer confusing crashes.
- **Fewer flaky failures when using `ul://...` datasets from Ultralytics HUB/Platform** 🌐✅  
- Large datasets can take longer to prepare; the smarter timeout reduces false “timeout” errors during dataset resolution.
- **Better usability and discoverability in docs** 🧭📘  
- Clearer Platform workflows (Activity Feed, Trash/Restore) and more direct “try it now” paths for major YOLO models.

For more context, see the **[v8.4.6 release tag on GitHub](https://github.com/ultralytics/ultralytics/releases/tag/v8.4.6)**.

What&#x27;s Changed
* Improve Platform docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23294
* Fix typos in YOLO26 and YOLOE docs by raimbekovm in https://github.com/ultralytics/ultralytics/pull/23298
* Longer NDJSON generation timeout by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23305
* Modernize type comments to inline annotations in `byte_tracker.py` by ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23304
* `ultralytics 8.4.6` Add missing PosixPath import in DDP train file gen by pfabreu in https://github.com/ultralytics/ultralytics/pull/23301

New Contributors
* ahmet-f-gumustas made their first contribution in https://github.com/ultralytics/ultralytics/pull/23304

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.5...v8.4.6

8.4.5

📊 Key Changes
- 🧩 **Pose Results summary now supports keypoints missing visibility**: `Results.summary()` checks `kpt.has_visible` and only outputs `&quot;visible&quot;` when it exists (otherwise returns just `&quot;x&quot;` and `&quot;y&quot;`), preventing crashes in mixed keypoint formats.
- 🔖 **Version bump**: `8.4.4` → `8.4.5`.
- 📚 **Docs &amp; examples shift to YOLO26**: Kaggle links, Ultralytics Platform docs, and multiple notebooks now point to **YOLO26** as the recommended current model family 🚀.
- 🗂️ **NDJSON dataset docs improved**: clearer per-task examples/tabs for Detect/Segment/Pose/OBB/Classify, including pose visibility explanations 🧾👀.
- 🧹 **Dependency cleanup**: removed optional `hub-sdk` extra from `pyproject.toml` (simplifies installs for users who don’t need it) 📦✂️.

🎯 Purpose &amp; Impact
- 🛡️ **Fewer runtime errors in pose pipelines**: exporting/logging/serving pose outputs as JSON via `Results.summary()` won’t fail when datasets/models omit visibility/confidence per keypoint (common in non-COCO formats).
- 🔄 **More robust downstream integrations**: safer summaries improve reliability for APIs, analytics, logging, and dataset export workflows that depend on consistent serialization.
- 📈 **Clearer “recommended model” guidance**: shifting docs and notebooks toward **YOLO26** reduces confusion for new users and aligns examples with the current default direction.
- 🧰 **Better dataset onboarding**: expanded NDJSON docs help users format annotations correctly across tasks, reducing trial-and-error when building datasets.

What&#x27;s Changed
* Add YOLO26 Kaggle models by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23289
* YOLO26 Google Colab notebook by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23290
* Update YOLO26 Colab Notebook by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23291
* Update NDJSON docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23292
* `ultralytics 8.4.5` 2D Pose `Result.summary()` support by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23293


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.4...v8.4.5

8.4.4

📊 Key Changes
- 🧠 **MuSGD optimizer scaling update (PR 23279 by Laughing-q)**  
MuSGD scale factors are now chosen more appropriately based on total training iterations:
- **&gt; 10,000 iterations:** `(muon=0.1, sgd=1.0)`
- **≤ 10,000 iterations:** `(muon=0.5, sgd=0.5)`  
This is a *behavior change* in `ultralytics/engine/trainer.py`.

- 📷 **Sony IMX500 export compatibility improved (PR 23266 by Laughing-q)**  
- IMX exporter now accepts **multiple valid layer counts** (a set of allowed values) instead of requiring exactly one, reducing false “unsupported model” failures ✅  
- IMX500 docs/examples are refreshed to focus on **YOLO11** (model names and paths updated).

- 🧩 **Segmentation proto/output handling fixed across backends (PR 23241 by Laughing-q)**  
- Segmentation post-processing now pulls the **proto masks from the correct output index** for both PyTorch and exported models.
- TensorFlow segmentation handling in AutoBackend now triggers based on **task == &quot;segment&quot;** (more reliable than guessing from output shape/length).

- 📁 **No more empty run folders when `save=False` (PR 23268 by Y-T-G)**  
`get_save_dir()` no longer auto-creates directories while computing a unique run path—reduces unwanted “predict” folders appearing on disk 🧹

- 📈 **TensorBoard OBB graph logging made safer (PR 23276 by Y-T-G)**  
TensorBoard graph logging now uses `smart_inference_mode()` to avoid gradient tracking—often **less memory/overhead** and fewer callback edge cases ⚙️

- 🔗 **Docs branding/link refresh (PR 23283 by glenn-jocher)**  
Multiple repo/docs links now point to the **Ultralytics Platform** entry point (and wording updated accordingly) 🧭

🎯 Purpose &amp; Impact
- 🚀 **More consistent training dynamics with MuSGD**  
If you use MuSGD (muon/sgd), you may see **improved stability and convergence** depending on whether your run is short or long—especially around the 10k-iteration threshold 🧠📉

- ✅ **Fewer export surprises (IMX500 + segmentation exports)**  
IMX500 exports should fail less often due to minor layer-count differences, and segmentation outputs should be **more consistent across PyTorch/exported/TF backends** 📦🧩

- 🧹 **Cleaner local runs and tooling behavior**  
Computing a save directory no longer creates folders prematurely, which is especially helpful for **dry runs**, scripts, and `save=False` predictions 📁✨

- ⚡ **More robust logging during training**  
TensorBoard graph logging becomes less intrusive and more reliable (particularly for OBB setups) 📊🛡️

What&#x27;s Changed
* Fix `YOLOv8/YOLO11` IMX export by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23266
* Disable automatic directory creation with `mkdir` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23268
* Use `YOLO26n` for benchmark tests by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23241
* Fix TensorBoard error with OBB by Y-T-G in https://github.com/ultralytics/ultralytics/pull/23276
* Replace HUB -&gt; Platform links by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23283
* `ultralytics 8.4.4` MuSGD optimizer scale factor update by Laughing-q in https://github.com/ultralytics/ultralytics/pull/23279


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.4.3...v8.4.4

8.4.3

🌟 Summary (single-line synopsis)
Ultralytics `v8.4.3` boosts **Ultralytics Platform NDJSON dataset downloads/conversion speed** 🚀, improves **training metric correctness** 🧠, and refreshes defaults/docs around **YOLO26** 📚.

📊 Key Changes
- 🚀 **Faster NDJSON → YOLO dataset conversion (Ultralytics Platform data) — PR 23257 by glenn-jocher**
- Lazy-loads `aiohttp` only when NDJSON conversion is used (faster startup, fewer unnecessary deps) 📦
- Simplifies async image download code and improves concurrency scaling to match dataset size ⚡
- Small robustness improvement for Platform URL handling 🌐
- Version bump: `8.4.2 → 8.4.3` 🔖
- 🌐 **Configurable Platform base URL — PR 23256 by glenn-jocher**
- Adds `ULTRALYTICS_PLATFORM_URL` to point callbacks/links to staging or local environments 🧪
- 🏷️ **Defaults and examples move to YOLO26 — PR 23242 by Laughing-q**
- Default `YOLO()`/CLI fallback model becomes `yolo26n.pt` and many docs/examples follow suit ✅
- ⚙️ **Training optimizer and warmup logic made more reliable — PR 23234 by Laughing-q**
- Parameter groups are explicitly labeled; warmup LR now targets the **bias** group by name (not by position) 🔥
- “Auto” optimizer strategy simplified around MuSGD with improved defaults 🎛️
- 🧍 **Pose training logs are more accurate — PR 23230 by lmycross**
- Only reports `rle_loss` when the model actually supports it (avoids confusing metrics) 🧾
- 🧩 **IMX inference/export consistency improvements — PR 23235 by Laughing-q**
- Anchor/stride refresh is more robust for changing input shapes; IMX decode path simplified 🔧
- ✅ **Fix duplicated `Results.summary()` entries — PR 23218 by xusuyong**
- Prevents duplicated rows in summaries (cleaner analytics/logging) 🧹
- 📈 **Benchmark tables clarified with end-to-end (e2e) metrics — PR 23238**
- README tables now clearly distinguish e2e evaluation metrics for YOLO26 tasks 📊
- 🍓 **Raspberry Pi 5 guide updated with YOLO26 benchmarks — PR 23227**
- Refreshes benchmark formats and adds ExecuTorch results 📌

🎯 Purpose &amp; Impact
- 🚀 **If you use Ultralytics Platform datasets (NDJSO

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