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ℹ️ Note

This PR body was truncated due to platform limits.

This PR contains the following updates:

Package Change Age Confidence
@iconify-json/material-symbols 1.2.87 → 1.2.88 age confidence
@tanstack/solid-form (source) 1.33.2 → 1.33.3 age confidence
@tanstack/solid-virtual (source) 3.13.35 → 3.13.36 age confidence
@types/three (source) 0.185.1 → 0.185.3 age confidence
ultralytics (changelog) ==8.4.107 → ==8.4.115 age confidence
vite-plugin-solid 2.11.13 → 2.11.14 age confidence

Release Notes

TanStack/form (@​tanstack/solid-form)

v1.33.3

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Patch Changes
TanStack/virtual (@​tanstack/solid-virtual)

v3.13.36

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Patch Changes
ultralytics/ultralytics (ultralytics)

v8.4.115: - Deprecate HUB in favor of Ultralytics Platform (#​25608)

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🌟 Summary

v8.4.115 transitions Ultralytics from legacy HUB integrations to the streamlined Ultralytics Platform experience, with simpler authentication and a leaner training codebase. 🚀

📊 Key Changes

  • 🔐 Introduced validated Platform CLI authentication

    • Log in with yolo login API_KEY
    • Remove credentials with yolo logout
    • API keys are checked against the Platform before being saved.

  • 🔄 Added settings migration to schema 0.0.7

    • Existing compatible settings, such as custom dataset and run directories, are preserved.
    • Legacy HUB configuration and incompatible HUB API keys are removed automatically.
    • Users with old credentials are directed to create a Platform API key.

  • 🧹 Removed the legacy ultralytics.hub package

    • HUB authentication, remote training sessions, model loading, exports, dataset utilities, callbacks, and HUB-specific exceptions have been retired.
    • HUB-related API references, documentation pages, navigation entries, and the HUB example notebook were also removed.

  • 🧠 Simplified model and trainer workflows

    • Models no longer load directly from HUB URLs.
    • Training no longer manages HUB sessions, remote checkpoints, heartbeats, or HUB-specific training arguments.
    • Platform callbacks remain available for streaming training information.

  • 📚 Updated documentation and examples

    • Guides, notebooks, CLI help, and API documentation now reference the Ultralytics Platform instead of HUB.
    • Platform authentication and login commands are included in the quickstart and CLI documentation.

  • ✅ Expanded test coverage

    • Added tests for settings migration, API-key validation, login, and logout behavior.

🎯 Purpose & Impact

  • ✨ A clearer user experience: Platform is now the primary destination for dataset management, training, and deployment, avoiding confusion between HUB and Platform services.
  • ⚡ Simpler authentication: The new yolo login command validates credentials directly and provides a more intuitive alternative to manually editing settings.
  • 🛠️ Cleaner and easier-to-maintain code: Removing obsolete HUB components reduces dependencies, integration complexity, and potential maintenance issues.
  • 🔒 Safer upgrades: Existing user settings are migrated instead of being reset, while outdated or incompatible HUB keys are discarded.
  • ⚠️ Compatibility consideration: Applications that import ultralytics.hub, use HUB training sessions, load models from HUB URLs, or rely on HUB-specific utilities must migrate to Ultralytics Platform workflows.
  • 🚀 Recommended next step: Create a Platform API key and authenticate with:
yolo login YOUR_API_KEY

For no-code dataset annotation, training, and deployment, use the Ultralytics Platform.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.4.114...v8.4.115

v8.4.114: - Surface Platform error messages and stop the console-output retry loop (#​25581)

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🌟 Summary

v8.4.114 improves reliability across Platform workflows, exported models, validation, edge inference, and advanced vision tasks—while delivering clearer errors and faster, more robust execution. 🚀

📊 Key Changes

  • Clearer Ultralytics Platform errors and quieter retries — PR #​25581 by @​glenn-jocher:

    • Platform URI resolution now uses GET instead of HEAD, preserving the detailed error messages returned by the Platform.
    • API errors such as invalid credentials, inaccessible datasets, and malformed pose labels now include actionable details.
    • Console-output upload failures no longer create a feedback loop where logged retry warnings trigger additional failed uploads.
    • Platform requests now stop early when no API key is available, and invalid credentials disable further attempts.

  • Improved exported-model validation:

    • Static ONNX, TensorRT, OpenVINO, and similar models now automatically reuse the image size stored in export metadata.
    • Users no longer need to manually provide the exact export imgsz during validation. ✅

  • More reliable model export and deployment:

    • Fixed GPU device mismatches during TorchScript inference by ensuring generated anchors follow the runtime device.
    • Prompt-free YOLOE exports now work correctly with NCNN and Paddle formats.
    • Loading a TorchScript archive as if it were a PyTorch checkpoint now produces a clearer error message.
    • Paddle export compatibility was improved for newer Python and x2paddle environments.

  • Faster LiteRT CPU inference:

    • LiteRT now uses the configured number of CPU threads, enabling multi-core inference.
    • Raspberry Pi 5 LiteRT benchmarks were corrected for YOLO26n and YOLO26s, showing substantially lower latency than previously reported. ⚡

  • Fixes for SAM3, visualization, and pose rendering:

    • SAM3 semantic prediction now defines the required mask threshold and avoids an AttributeError.
    • Class activation maps safely handle class IDs outside a model’s output range.
    • Pose keypoints and limbs located exactly on image borders are now rendered correctly instead of being silently dropped.

  • Improved training and data pipelines:

    • BGR augmentation now applies correctly to semantic segmentation and depth training.
    • Distributed validation no longer crashes when the total batch size exceeds the number of validation images.
    • Dataset YAML validation handles empty names fields more safely.
    • Analytics line charts now accumulate counts across the configured update window instead of resetting every frame.

  • Documentation and maintenance updates:

    • Added missing validation documentation for channels_last.
    • Documented class remapping and depth-loss training parameters.
    • Updated augmentation support tables for semantic and depth tasks.
    • Fixed Intel DL Streamer installation links and similarity-search examples.
    • Removed several redundant regression tests to reduce test-suite maintenance overhead. 📚

🎯 Purpose & Impact

  • 🛠️ Faster troubleshooting: Platform failures now explain what went wrong, helping users fix dataset, authorization, and configuration issues without repeated trial and error.
  • 🔁 More stable automation: Console logging will no longer feed failed messages back into Platform upload retries, reducing noisy logs and unnecessary network traffic.
  • ✅ Simpler validation: Exported models can generally be validated without manually matching their original image size and batch settings.
  • 🚀 Better edge performance: Multi-core LiteRT support can significantly improve CPU inference speed on devices such as Raspberry Pi 5.
  • 📦 Broader deployment compatibility: TorchScript, NCNN, Paddle, and other export paths are more dependable across devices and Python environments.
  • 🎯 More robust vision workflows: SAM3, pose visualization, CAM generation, depth, semantic segmentation, and distributed validation now handle common edge cases more gracefully.
  • 📖 Clearer documentation: Users can more easily discover supported arguments and follow current Intel and similarity-search setup instructions.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.113...v8.4.114

v8.4.113: - Add Dragonwing IQ-8275 QNN export target (#​25558)

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🌟 Summary

🚀 Ultralytics 8.4.113 expands Qualcomm QNN deployment to Dragonwing IQ-8275 devices, improves model visualization and export reliability, and standardizes task support documentation across the project.

📊 Key Changes

  • Qualcomm Dragonwing IQ-8275 QNN export

    • Adds iq-8275 and qcs8275 as QNN export targets using Qualcomm SoC model 82.
    • Centralizes the mapping between supported QNN targets and their HTP or SoC provider options.
    • Adds documentation for supported Snapdragon and Dragonwing targets, including the unsupported IQ-615.
    • Enables QNN export testing on Linux x86-64 with onnxruntime-qnn==2.4.0.
    • Example usage: model.export(format="qnn", name="iq-8275").
    • Requires a compatible Qualcomm/Yocto BSP and target-side QNN, FastRPC, DSP firmware, and driver components.

  • New class activation heatmaps for prediction 🔥

    • Replaces the previous visualize=True feature-map dump with a more useful LayerCAM-style heatmap.
    • Saves one heatmap image per input, showing which image regions influenced the predicted class scores.
    • Respects confidence and class filters and is available for PyTorch models.
    • Reduces output clutter compared with the former multi-file feature-map visualization.

  • More accurate attention FLOPs reporting 📊

    • THOP-based profiling now counts functional attention matrix multiplications in YOLO12 attention blocks.
    • Models containing area attention are measured at the requested image size rather than extrapolated from a small stride-sized input.
    • Reported GFLOPs should better reflect the real computational cost of attention-heavy models.

  • Improved RKNN and quantized export support

    • Normalizes detection and pose coordinates during INT8 RKNN export to preserve class-score precision.
    • Restores coordinates at runtime for RKNN inference.
    • Adds compatibility handling for current rknn-toolkit2 dependencies, including setuptools<82.
    • Improves ONNX compatibility and cleans up temporary normalized graphs after export.

  • Export and runtime reliability fixes 🛠️

    • Resets detection shape caches at TorchScript, ONNX, and OpenVINO export boundaries.
    • Avoids redundant TorchScript retracing.
    • Replaces ONNX advanced indexing with gather where needed.
    • Fixes YOLOE-26 prompt-free RKNN export for detection and segmentation.
    • Corrects pose loss selection for end-to-end models using non-Pose26 heads.
    • Prevents C2PSA failures when its channel count is below 64.
    • Fixes MuSGD handling of custom model head locations and higher-rank parameters.

  • Performance and stability improvements ⚡

    • Vectorizes Deep OC-SORT global motion compensation operations.
    • Reduces temporary memory usage and plotting time for segmentation masks by processing them in row bands.
    • Keeps accumulated heatmap overlays visible when a tracking frame temporarily contains no detections.
    • Prevents grayscale video stream failures from killing readers or crashing inference.
    • Makes mixed-text ordering deterministic across processes and distributed workers.
    • Keeps profiler samples when runtime variance reaches zero.

  • Documentation consistency and accuracy 📚

    • Establishes one canonical task and mode order across code, documentation, tables, and the Ultralytics Platform.
    • Consolidates supported-task tables into a shared macro and reports model-family-specific support more accurately.
    • Corrects multiple docstrings, doctest examples, export claims, task descriptions, and API references.
    • Updates Hailo, QNN, GraphDef, RKNN, and other integration pages with clearer model and hardware limitations.

🎯 Purpose & Impact

  • For Qualcomm users: IQ-8275 and QCS8275 devices can now be targeted directly during QNN export, simplifying deployment to Dragonwing hardware. Hardware-side BSP compatibility must still be verified.
  • For model developers: Heatmaps provide a clearer way to understand model decisions, while corrected FLOPs estimates make performance comparisons more trustworthy.
  • For edge deployment: RKNN and QNN improvements increase export compatibility and improve quantized inference reliability on specialized accelerators.
  • For training workflows: MuSGD, pose-loss, C2PSA, and higher-rank tensor fixes reduce silent training errors and improve support for custom architectures.
  • For tracking and streaming applications: Video recovery, heatmap persistence, and Deep OC-SORT updates improve robustness in real-world, imperfect inputs.
  • For documentation users: Shared and empirically validated compatibility tables make it easier to determine which model family, task, and deployment target is supported. 🚀

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.112...v8.4.113

v8.4.112: - Document supported tasks for every export format (#​25511)

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🌟 Summary

Release v8.4.112 makes model export support much clearer and more reliable, with comprehensive task documentation across formats and a fix enabling DEEPX classification exports. 📦✅

📊 Key Changes

  • Documented supported tasks for every export format 📚

    • Added consistent Supported Tasks tables to 20 integration pages.
    • Clearly lists support for all seven Ultralytics tasks:
      • Object detection
      • Instance segmentation
      • Semantic segmentation
      • Pose estimation
      • OBB detection
      • Classification
      • Depth estimation
    • Documents model-family limitations, such as semantic segmentation and depth estimation being YOLO26-only for many formats.
    • Explicitly identifies unsupported combinations, including:
      • Axelera depth estimation
      • Hailo YOLO26 instance segmentation, pose, and OBB
      • Sony IMX500 semantic segmentation, OBB, and depth estimation

  • Verified export coverage across formats 🔍

    • Completed 77 local export and inference checks across seven tasks.
    • Coverage was verified for TorchScript, ONNX, OpenVINO, CoreML, TensorFlow formats, PaddlePaddle, MNN, NCNN, ExecuTorch, and LiteRT.
    • TensorRT and RKNN support was confirmed through existing CI test matrices.

  • Fixed DEEPX classification export 🛠️

    • Corrected calibration dataset discovery for classification models.
    • Classification datasets store their image directory as root, rather than img_path; the exporter now handles this correctly.
    • Updated DEEPX smoke tests to cover every task-specific default model instead of only YOLO26 detection.

  • Removed outdated GraphDef benchmark restrictions ⚡

    • TensorFlow GraphDef benchmarks no longer reject OBB or pose models based on obsolete limitations.

  • Minor documentation and release updates ✨

    • Updated the package version to 8.4.112.
    • Improved wording around Huawei Ascend, TensorFlow GraphDef, NCNN, RKNN, and Hailo support.
    • Clarified that Edge TPU task support may still involve CPU execution for unsupported operations.

🎯 Purpose & Impact

  • Easier format selection: Users can now quickly determine whether their task and model family are compatible with a target export format. 🧭
  • Fewer failed deployments: Explicit support tables reduce confusion caused by previously undocumented or implied limitations.
  • Improved classification deployment: DEEPX users can now export classification models successfully, including through automated smoke testing.
  • Better confidence in task support: Broad empirical validation helps ensure documentation reflects actual export and inference behavior.
  • More accurate benchmarking: Removing outdated GraphDef checks allows supported OBB and pose workflows to be benchmarked properly.
  • Important practical note: A task marked as supported does not always mean the entire model runs on the accelerator; formats such as Edge TPU may execute some operations on the CPU.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.4.111...v8.4.112

v8.4.111: - Add Huawei Ascend NPU training support (#​25500)

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🌟 Summary

🚀 Ultralytics 8.4.111 expands hardware support with validated Huawei Ascend NPU training, broader accelerator compatibility, and important tracking, MPS, deployment, and documentation improvements.

📊 Key Changes

  • Huawei Ascend NPU training support 🧠

    • Added single- and multi-NPU training and validation through torch_npu.
    • Supports standard training features including AMP, checkpointing, resume, AutoBatch, profiling, memory management, and validation.
    • Multi-NPU training uses Huawei’s HCCL distributed backend.
    • Example device selections include device=npu:0 and device=npu:0,1.
    • Ascend documentation now covers the workflow from training through .om export and deployment.

  • Unified accelerator handling ⚙️

    • Device behavior is now derived from the selected PyTorch device rather than being hard-coded for CUDA.
    • Improves compatibility with:
      • Huawei Ascend npu
      • Intel xpu
      • AMD ROCm through PyTorch’s standard CUDA-style device interface
    • Distributed training now selects the appropriate backend: NCCL for NVIDIA, HCCL for Ascend, and XCCL for Intel.

  • AMD ROCm integration guide 🔴

    • Added documentation for training, validation, and inference on supported AMD GPUs using PyTorch ROCm.
    • Clarifies that ROCm uses device=0 or device=cuda:0, not device=rocm:0.
    • Clearly distinguishes supported ROCm workflows from currently unsupported or separate technologies such as MIGraphX, DirectML, and Ryzen AI NPU.

  • Improved accelerator-aware data loading and profiling 📈

    • Dataloaders, pinned memory, synchronization, memory checks, profiling, automatic batch sizing, and mixed precision now account for more device types.
    • NPU and XPU execution avoids unsupported operations such as torchvision NMS kernels where necessary.

  • Tracking and numerical stability improvements 🎯

    • Object counting now detects objects that pass through polygon regions between frames, even when no centroid is sampled inside the region.
    • Deep OC-SORT preserves homography precision.
    • Kalman filter states consistently use float64.
    • Re-identification features are stored safely as float32.
    • GMC matching now retains valid zero-variance and boundary-distance matches.
    • NumPy assignment now raises a clear error for infeasible cost matrices instead of potentially hanging.

  • Apple MPS reliability fixes 🍎

    • Avoids problematic in-place operations on strided MPS tensors across affected macOS and PyTorch versions.
    • Prevents autocast crashes on MPS with PyTorch versions older than 2.5.

  • Documentation and platform updates 📚

    • Added LabelMe dataset import instructions for converting offline annotations to YOLO format and uploading them to the Ultralytics Platform.
    • Updated Rust inference documentation for ultralytics-inference 0.0.32, including Intel CPU, GPU, and NPU device options.
    • Refreshed Raspberry Pi 5 YOLO26 benchmarks with LiteRT and clarified that LiteRT export must be performed off-device.
    • Added a monocular depth estimation tutorial to the YOLO26 documentation.
    • Normalized GPU names in usage telemetry to improve reporting consistency.

🎯 Purpose & Impact

  • 🌍 Broader hardware choice: Users can train Ultralytics models on Huawei Ascend NPUs, AMD GPUs, Intel accelerators, and NVIDIA GPUs through more consistent device handling.
  • 🚀 Easier enterprise and edge deployment: Ascend training now connects directly to existing export and deployment workflows, while AMD and Raspberry Pi guidance makes hardware-specific setup clearer.
  • 🧩 Less backend-specific code: The shared PyTorch device abstraction reduces the need for separate trainers or parallel implementations for each accelerator.
  • 📊 More dependable tracking: Object counters and trackers should behave more accurately in fast-motion, low-variance, and mixed-precision scenarios.
  • 🛡️ Improved reliability: MPS fixes prevent crashes and inconsistent detections, while the assignment fallback now fails safely on impossible inputs.
  • 📖 Better onboarding: New LabelMe, AMD, Ascend, Rust, Raspberry Pi, and depth resources help users move from dataset preparation to training and deployment more easily.
  • 🔢 The package version is updated to 8.4.111.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.110...v8.4.111

v8.4.110: - Expand RKNN export support to all tasks (#​25490)

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🌟 Summary

🚀 Version 8.4.110 expands RKNN export to every supported YOLO task and improves GPU-friendly tensor image plotting, making Rockchip deployment more versatile and visualization more efficient.

📊 Key Changes

  • Expanded RKNN export support by @​glenn-jocher:

    • RKNN models can now be exported for:
      • Object detection
      • Instance segmentation
      • Classification
      • Pose estimation
      • Oriented bounding boxes (OBB)
      • Semantic segmentation
      • Depth estimation
    • Export smoke tests now cover all current task models using rknn-toolkit2.
    • Documentation and export tables now show task-specific model names and artifacts.
    • Package version bumped to 8.4.110.

  • More efficient tensor-based plotting:

    • Results.plot() and Annotator now accept contiguous HWC BGR torch.Tensor images.
    • Tensor images can remain on their original device during mask compositing, avoiding unnecessary CPU transfers.
    • Conversion to NumPy is delayed until required by OpenCV, PIL, display, saving, or the returned result.

  • Documentation improvements:

    • Corrected “setup” to “set up” in the DeepSparse tutorial.
    • Updated plotting API documentation to include tensor image support.
    • Added task-specific export configuration to classification, depth, OBB, pose, segmentation, and semantic segmentation pages.

🎯 Purpose & Impact

  • Broader Rockchip NPU deployment 📱: Users can deploy more types of YOLO26 models on RKNN-compatible Rockchip hardware, rather than being limited to detection models.
  • Simpler export workflows 🛠️: Task-specific documentation and artifact names make it clearer which model to export and how to use the resulting RKNN file.
  • Improved visualization performance ⚡: GPU-backed tensor images can avoid an unnecessary image copy to the host during mask plotting, which may improve performance for accelerated pipelines.
  • Better reliability ✅: Expanded validation confirms RKNN conversion and basic inference coverage across all seven supported tasks.
  • No major behavior changes for existing users: NumPy image workflows remain supported, while tensor-based workflows gain improved device efficiency.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.109...v8.4.110

v8.4.109: - Log GFLOPs at the training imgsz (#​25483)

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🌟 Summary

📊 v8.4.109 improves profiling accuracy, export reliability, inference efficiency, tracking stability, and Ultralytics Platform integrations—led by correctly reporting GFLOPs for the actual training image size.

📊 Key Changes

  • Accurate GFLOPs logging at training resolution 🎯

    • model_info_for_loggers() now passes the trainer’s actual imgsz to FLOPs profiling.
    • Logging integrations—including W&B, Comet, ClearML, DVC, Neptune, HUB, and Platform—no longer report the default 640-pixel GFLOPs for every run.
    • Classification and other models with size-independent operations also receive more accurate measurements through the updated ultralytics-thop dependency.

  • Improved model fusion and profiling ⚙️

    • model.fuse() now accepts an imgsz argument so reported model information matches the intended input resolution.
    • Export workflows pass the export image size during fusion and profiling.

  • More reliable INT8 calibration 📦

    • Classification exports now honor the fraction setting when selecting calibration images.
    • OpenVINO INT8 calibration uses the complete requested calibration dataset instead of silently limiting calibration to NNCF’s default 300 batches.

  • Faster and more efficient mask plotting 🖼️

    • Removed an unused GPU image construction and unnecessary tensor allocations.
    • Mask rendering now avoids redundant resizing and host-to-device transfers, reducing memory use and plotting overhead.

  • Inference warmup now uses the real input shape 🔥

    • Predictors warm up using the first preprocessed input rather than a fixed square dummy image.
    • This better supports rectangular images, tensor sources, dynamic shapes, and shape-specialized backends.

  • Training recovery behavior made safer 🛡️

    • Healthy runs are no longer stopped simply because an early validation fitness value rounds to zero.
    • Recovery remains enabled for genuine NaN or infinite loss and fitness values.

  • Tracking and numerical stability improvements 🚦

    • ReID embeddings are consistently processed as float32 for more dependable association decisions.
    • Embedding-distance calculations use the appropriate dtype precision.
    • macOS tracking workflows avoid spurious NumPy warnings and can run faster during covariance updates.

  • Heatmap and validation performance improvements 📈

    • Heatmap regions are drawn once per frame, preventing flicker when no objects are tracked and avoiding repeated work.
    • Confusion-matrix matching replaces repeated array scans with direct lookups.

  • Expanded Ultralytics Platform documentation and integrations 🌐

    • Added Labelbox import documentation, including direct NDJSON upload workflows.
    • Added coming-soon documentation for CVAT and Label Studio integrations, with supported YOLO export paths available today.
    • Updated Cityscapes guidance and removed broken Platform dataset links.
    • For dataset annotation, training, and deployment, users can use Ultralytics Platform.

  • System and CI maintenance 🧰

    • Removed stale persistent CPU-name caching and the macOS subprocess probe.
    • CI now tests newer OpenVINO Conda packages and uses actions/stale@v11.
    • Cleaned up augmentation documentation image sizing for consistent rendering.

🎯 Purpose & Impact

  • ✅ More trustworthy experiment tracking: GFLOPs now reflect the resolution actually used for training or export, making model comparisons and resource estimates more meaningful.
  • 🚀 Better deployment preparation: INT8 calibration respects user-selected data limits and OpenVINO can use the full requested calibration set, potentially improving quantization quality.
  • ⚡ Lower inference and visualization overhead: Real-shape warmup and optimized mask plotting can reduce first-inference delays, memory use, and unnecessary computation.
  • 🛡️ Fewer unexpected failures: Valid early-stage training runs are less likely to be incorrectly treated as corrupted, while real numerical failures still trigger recovery.
  • 🎥 More stable tracking outputs: ReID precision and macOS matrix-operation fixes improve consistency and reduce distracting runtime warnings.
  • 🌍 Simpler dataset workflows: New Platform integration guidance helps users move data from labeling tools into annotation, training, and deployment workflows with fewer conversion steps.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.4.108...v8.4.109

v8.4.108: - Fix MuSGD crash on non-matrix params like LayerScale (#​25447)

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🌟 Summary

🛠️ v8.4.108 improves MuSGD stability, inference and tracking efficiency, analytics quality, ONNX compatibility, and documentation usability—without changing model accuracy or architecture.

📊 Key Changes

  • Fixed a MuSGD training crash by @​Y-T-G:

    • MuSGD now applies its Muon-style update only to supported 2D linear weights and 4D convolution filters.
    • LayerScale gains, normalization weights, biases, and other parameters use the existing SGD update path.
    • Prevents AssertionError failures when training models containing parameters such as (C, 1, 1) LayerScale tensors.

  • Improved prediction performance reporting:

    • Predictors now retain per-image preprocessing, inference, and postprocessing times through predictor.speed.
    • Added average processed image area through predictor.pixels.
    • Timing information is reset correctly for each run and supports mixed image sizes.

  • Reduced tracking overhead and preserved device placement:

    • TrackTrack computes motion cues only for detection pairs supported by IoU, reducing unnecessary CPU work, especially in crowded scenes.
    • Removed redundant NumPy array copies in ByteTrack and TrackTrack.
    • Tracked boxes now remain on the same device as the original detections, avoiding unexpected CPU/GPU data splits.

  • Improved ONNX Runtime example compatibility:

    • Corrected image height/width handling during letterbox preprocessing.
    • Added fallback support for models with dynamic input dimensions.

  • Strengthened training and inference analytics:

    • Training events now report final outcomes such as fitness, duration, completed epochs, optimizer, architecture, and dataset information.
    • Multi-GPU runs now record the GPU count instead of a simple distributed-training flag.
    • Invalid modes and tasks are excluded from analytics events.

  • Expanded Ultralytics Platform integration:

    • Model weights for YOLO26, YOLO11, YOLOv8, and supported YOLOv5 variants now link to their corresponding Ultralytics Platform model pages.
    • Huawei Ascend 310B1 and 310B4 targets are now documented as supported on Platform.

  • Improved generated reference documentation:

    • Fixed broken tables for union types such as str | Path.
    • Preserved multi-line return descriptions.
    • Documented inherited constructors correctly.
    • Improved source panels, examples formatting, frontmatter handling, and empty argument tables.

  • Updated documentation links and CI reliability:

    • Replaced unstable ADE20K and Flickr30K links with maintained GitHub sources.
    • Excluded Gitee links from automated checks to avoid false failures caused by bot protection.

🎯 Purpose & Impact

  • ✅ MuSGD users can train a wider range of models reliably, including architectures with LayerScale or other non-matrix parameters.
  • 🚀 Inference and tracking are more efficient, with lower avoidable CPU work and better GPU/CPU device consistency.
  • 📈 Performance measurements and analytics are more useful, making it easier to compare real-world runs across hardware and distributed setups.
  • 🔄 ONNX Runtime examples work with more exported models, including dynamic-shape models.
  • 📚 Documentation is easier to navigate and more accurate, with working model links, corrected API references, and clearer examples.
  • 🌐 Users can more easily explore, train, and deploy supported models through the Ultralytics Platform, the recommended workflow for managing YOLO projects.

What's Changed

✂ Note

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@renovate renovate Bot added the dependencies label Aug 3, 2026
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renovate Bot force-pushed the renovate/all-patch-dependencies branch 4 times, most recently from f7dca67 to f91f808 Compare August 4, 2026 06:48
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