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This PR contains the following updates:

Package Change Age Confidence Type Update Pending
@iconify-json/material-symbols 1.2.87 → 1.2.88 age confidence devDependencies patch
@inlang/paraglide-js (source) 2.23.0 → 2.23.2 age confidence devDependencies patch
@tanstack/router-plugin (source) 1.168.23 → 1.168.27 age confidence dependencies patch 1.168.29 (+1)
@tanstack/solid-form (source) 1.33.2 → 1.33.4 age confidence dependencies patch 1.33.5
@tanstack/solid-router (source) 1.170.18 → 1.170.22 age confidence dependencies patch 1.170.24 (+1)
@tanstack/solid-router-devtools (source) 1.167.0 → 1.167.1 age confidence devDependencies patch
@tanstack/solid-virtual (source) 3.13.35 → 3.13.36 age confidence dependencies patch
@types/three (source) 0.185.1 → 0.185.4 age confidence devDependencies patch
python (source) ==3.14.6 → ==3.14.7 age confidence requires-python patch
ultralytics (changelog) ==8.4.107 → ==8.4.118 age confidence project.dependencies patch
vite-plugin-solid 2.11.13 → 2.11.14 age confidence devDependencies patch

Release Notes

opral/paraglide-js (@​inlang/paraglide-js)

v2.23.2

Patch Changes
  • b8af4b1: Embed TypeScript sources in published JavaScript sourcemaps and stop emitting declaration sourcemaps that reference unpublished source files.

v2.23.1

Patch Changes
  • 6f9b062: Keep the server middleware's AsyncLocalStorage available when module instrumentation snapshots mutable exports.
TanStack/router (@​tanstack/router-plugin)

v1.168.27

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v1.168.26

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v1.168.25

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v1.168.24

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TanStack/form (@​tanstack/solid-form)

v1.33.4

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Patch Changes

  • #​2263 d7fd741 - Generate the default formId with Solid's createUniqueId so it is SSR-safe. Previously, when no formId was configured, createForm did not provide a fallback, so FormApi generated a random UUID that differed between the server render and the client render. Binding that generated id (<form id={form.formId}>) produced a hydration mismatch under SolidStart. An explicitly provided formId was already forwarded and is unchanged. This mirrors the existing behaviour of the React, Preact, and Vue adapters.

  • Updated dependencies []:

v1.33.3

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TanStack/router (@​tanstack/solid-router)

v1.170.22

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v1.170.21

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v1.170.20

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v1.170.19

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Patch Changes

  • #​7805 45c4ad8 - Rewrite match loading around a lane-based scheduler that tracks each navigation, preload, and background reload as an ordered unit of work. This fixes pending/redirect/retry state leaking between overlapping navigations, restores correct SSR status codes for redirects, errors, and not-found responses, and closes hydration gaps where the client re-ran work the server had already completed.

    • Invalidation now retires matching active preloads so older speculative loader results cannot become fresh cache data after invalidation.
    • Route headers() now only runs on the server, matching the documented behavior — it is no longer invoked during client-side asset projection.
    • The documented default gcTime and preloadGcTime now match the existing runtime default of 5 minutes (300_000).

    Removed / changed exported internals

    • RouterState no longer includes loadedAt, isTransitioning, statusCode, or redirect. Use match.updatedAt in place of loadedAt; subscribe to router.state.status / router.state.isLoading in place of isTransitioning; server response status and redirect handling are now internal to the server loader and are no longer exposed on router.state.
    • RouteMatch.fetchCount has been removed, with no replacement — it was purely informational.
    • RouteMatch.status no longer includes 'redirected' (it remains 'pending' | 'success' | 'error' | 'notFound') — redirected matches are dropped from the match list instead of being rendered.
    • RouteMatch.globalNotFound has been renamed and privatized to the internal _notFound field. Use match.status === 'notFound' instead.
    • The exported React, Solid, and Vue Match components now accept routeId instead of matchId.
    • The exported RouterStores adapter contract now uses route-keyed presentation stores: matchesId is replaced by ids, matchStores by byRoute, and getRouteMatchStore() by getMatchStore(). The separate loadedAt, isLoading, isTransitioning, statusCode, and redirect stores have been removed, along with the pending/cache stores and their setters. StoreConfig.init has also been removed. Read application-facing state from router.state; preload and cache coordination are now internal.
    • Removed RouterCore members getMatch(), updateMatch(), cancelMatch(), and cancelMatches() — read matches from router.state.matches (e.g. router.state.matches.find((m) => m.id === id)); there is no replacement for mutating or cancelling an individual in-flight match from outside the router.
    • Removed RouterCore.hasNotFoundMatch() — use router.state.matches.some((m) => m.status === 'notFound').
    • Removed RouterCore.looseRoutesById — use routesById.
    • Removed RouterCore.isPrerendering(), RouterCore.isViewTransitionTypesSupported, and RouterCore.viewTransitionPromise, with no replacement.
    • Removed RouterCore.getParsedLocationHref() and RouterCore.clearExpiredCache(), with no replacement — expired cache entries are now reconciled automatically as part of match commit.
    • Removed RouterCore.latestLoadPromise and RouterCore.beforeLoad(), with no replacement.
    • RouterCore.commitLocationPromise and RouterCore.pendingBuiltLocation have been replaced by the internal _commitPromise and _pendingLocation fields.
    • Removed the exported GetMatchFn and UpdateMatchFn types, along with the methods they typed.
    • Removed the standalone getMatchedRoutes() export from @tanstack/router-core — use the router.getMatchedRoutes() instance method instead.
    • RouterCore.loadRouteChunk() no longer accepts an array of component types as its second argument. One-argument usage is unchanged; the optional second argument is now 'errorComponent', 'notFoundComponent', or false for internal boundary loading.
    • Removed Redirect.redirectHandled, which was internal redirect bookkeeping.
    • MatchRoutesOpts.preload and MatchRoutesOpts.dest have been removed.
    • StartTransitionFn is now (fn, expected) => Promise<boolean> (previously (fn) => void). This only affects custom framework adapters that implement startTransition.

  • Updated dependencies [45c4ad8]:

TanStack/router (@​tanstack/solid-router-devtools)

v1.167.1

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

v3.13.36

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python/cpython (python)

v3.14.7

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ultralytics/ultralytics (ultralytics)

v8.4.118: - Add standalone LLM model interface (#​25761)

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🌟 Summary

Ultralytics v8.4.118 introduces a standalone OpenAI-compatible LLM interface alongside YOLO, while improving OBB training, dataset handling, model training reliability, and documentation workflows. 🚀

📊 Key Changes

  • 🤖 New standalone LLM model interface by @​glenn-jocher

    • Add from ultralytics import LLM for text and image-based language-model requests.
    • Supports OpenAI Responses and Chat Completions APIs, including synchronous and asynchronous calls.
    • Accepts images from local paths, URLs, data URLs, NumPy arrays, and PIL images.
    • Supports reusable prompts, request overrides, conversation state, API keys, and OpenAI-compatible service endpoints.
    • Uses the optional openai dependency and remains independent of Ultralytics Platform and workflow-runtime components.

  • 📐 Improved oriented bounding box training

    • Mosaic, CutMix, and RandomPerspective now preserve OBB orientation when objects are clipped by image boundaries.
    • Prevents clipped objects from receiving incorrect rotation angles during training.

  • ⚡ Faster CopyPaste augmentation

    • Batches instance concatenation instead of repeatedly copying growing arrays.
    • Reduces unnecessary processing overhead, especially when many objects are copied.

  • 🧠 More reliable YOLOE behavior

    • Validates visual prompts before modifying model state.
    • Accepts flat prompts for supported batched image sources.
    • Rejects invalid string class labels and mismatched vocabularies earlier with clearer errors.
    • Preserves gradient settings when converting YOLOE convolution layers to linear layers.

  • 🏋️ Training and inference stability fixes

    • Correctly resets dataloader workers when resuming after Mosaic augmentation is closed.
    • Allows repeated train() and tune() calls on the same model object.
    • Prevents duplicate World model callbacks across multi-dataset training.
    • Fixes classification prediction for models without predefined transforms.
    • Ensures classification validation loaders do not discard samples when compiling.
    • Fixes SAM and related predictor models being created with incompatible inference-only tensors.

  • 🗂️ Dataset and prediction improvements

    • Classification auto-splitting now recognizes all supported image formats, including JPEG, BMP, WebP, TIFF, AVIF, HEIC, and uppercase extensions.
    • Missing classification images now raise a clear FileNotFoundError instead of failing later with an unrelated directory error.
    • Preserves original filenames when loading images after EXIF correction.
    • Keeps bounding-box fallbacks for malformed grounding segmentation labels.

  • 📚 Documentation and deployment updates

    • Standardizes strict documentation validation on Zensical and updates contributor instructions.
    • Documentation redeployment now detects Python docstring and all configuration-file changes.
    • Restores model benchmark chart placeholders, including for YOLO26, while moving production site features to the centralized publisher.
    • Documents replacing the model behind an existing deployment without changing its endpoint URL, API key, or deployment identity.
    • Updates Albumentations examples for current 2.x constructor names and refreshes the Rust inference dependency to 0.0.34.
    • Adds API reference documentation for the new LLM interface.

🎯 Purpose & Impact

  • 🚀 Broader AI capabilities: Developers can now use Ultralytics as a unified entry point for YOLO vision models and OpenAI-compatible language models, including multimodal image understanding.
  • 🔌 Flexible integration: The new LLM class works with OpenAI and compatible providers without requiring Platform or workflow features.
  • 🎯 Better OBB accuracy: Rotated-object datasets should receive more consistent training targets when augmentation crops objects at image edges.
  • ⚡ Improved performance: CopyPaste augmentation can run more efficiently, particularly on images containing many instances.
  • 🛡️ More predictable training: Resume, compile, repeated training, World models, SAM, and classification workflows are less likely to produce silent errors or invalid results.
  • 🧰 Easier maintenance: Broader dataset format support, clearer validation errors, and more accurate documentation reduce setup and debugging time for users.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.117...v8.4.118

v8.4.117: - Route spatial Albumentations by type and carry masks, polygons and keypoints through it (#​25633)

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🌟 Summary

v8.4.117 improves augmentation correctness, model reliability, deployment safety, and documentation across Ultralytics YOLO and YOLO26. 🚀

📊 Key Changes

  • 🧩 Albumentations now handles spatial transforms by type

    • PR #​25633 replaces the fragile hardcoded transform-name list with recursive type detection, so wrapped transforms such as OneOf correctly update annotations.
    • Spatial augmentations now carry bounding boxes, polygons, masks, depth maps, and keypoints through the pipeline.
    • Background-only images receive pixel-level augmentations even when they have no annotations.
    • Keypoint flipping respects the configured flip_idx mapping.
    • Probability handling now makes a zero probability a reliable off switch.
    • Unsupported topology-changing transforms, such as grid shuffling with polygons or keypoints, are detected instead of silently corrupting labels.

  • 🔐 Improved security for dependency installation

    • check_requirements() now prevents untrusted requirement strings from being interpreted as shell commands.
    • This protects workflows that automatically install missing dependencies while loading model files.

  • 🛡️ More reliable dataset and mask processing

    • Fixed Windows semantic-mask shape handling so grayscale masks remain two-dimensional and semantic training no longer fails during Mosaic augmentation.
    • COCO conversion now keeps one consistent label format per file, warns about unusable polygons, and falls back to box-shaped polygons when necessary.
    • Mixed detection and segmentation rows are rejected instead of being silently misread.
    • Grounding dataset caching and validation were improved, including clearer errors for empty or invalid annotation sets.
    • Degenerate polygon contours are skipped when exporting labels.

  • 🧠 Depth estimation improvements

    • Depth postprocessing now aligns PyTorch, Hailo, and exported-model outputs before removing padding, producing more consistent results across inference backends.
    • Depth validation no longer inherits rectangular batching behavior that conflicts with its intentional square stretching.
    • The KITTI depth configuration now uses the canonical 652-frame left-camera evaluation split, avoiding test-set overlap and making reported results more reproducible.
    • Depth metrics and documentation were updated to reflect per-image evaluation behavior.

  • ⚡ Faster and more consistent inference

    • YOLO26 end-to-end postprocessing uses grouped top-k selection, improving TensorRT FP16 latency by approximately 1.8% to 8.1% without changing mAP.
    • RT-DETR FLOPs profiling is faster and now supports attention-based decoder architectures correctly.
    • SavedModel INT8 calibration avoids an unnecessary NumPy copy.
    • GMC tracking now handles textureless frames and incomplete feature matches without stopping, allowing tracking to recover cleanly.
    • SAM3 video masks use the model’s configured threshold consistently instead of a fixed threshold.

  • 🎯 Expanded model and training support

    • Pose training now accepts dataset-defined kpt_oks_sigmas, with validation that the configuration matches the model’s keypoint count.
    • OBB utilities handle empty inputs more safely, and documentation clarifies long-edge canonicalization and prediction formats.
    • Predictors now report unsupported options such as augmentation, embeddings, and visualization instead of silently ignoring them.
    • Cached predictors are refreshed after training so predictions use the newly trained weights and class names.
    • Embedding requests now provide clearer errors for exported or third-party models that do not expose compatible internal layers.
    • YOLOE class and visual-prompt validation was strengthened, supporting more reliable prompt-free vocabulary workflows.

  • 📚 Documentation and platform updates

    • Export documentation now covers additional options such as name, split, conf, iou, max_det, and agnostic_nms.
    • Classification dataset inputs are consistently documented as directories or built-in dataset names rather than YAML files.
    • Added reusable depth-speed comparison documentation and a new YAML2ModelGraph integration guide for generating YOLO architecture diagrams from model YAML files.
    • Added Platform troubleshooting guidance for datasets, training, deployment, billing, and common questions.
    • FAQ and supporting sections were reorganized for more consistent documentation rendering.
    • Rust inference documentation now references ultralytics-inference version 0.0.33.
    • Documentation publishing is now restricted to the main branch to prevent accidental production releases.

🎯 Purpose & Impact

  • ✅ More trustworthy augmentation: Labels and auxiliary data remain aligned when using custom or nested Albumentations pipelines, reducing silent training errors.
  • 🧪 Better training stability: Depth, semantic segmentation, pose, OBB, grounding, and tracking workflows handle edge cases more gracefully.
  • 🚀 Improved deployment performance: YOLO26 TensorRT exports can achieve lower postprocessing latency, while depth predictions behave more consistently across backends.
  • 🔒 Safer model loading: Dependency checks no longer expose shell execution risks through malicious requirement strings.
  • 📊 More reproducible evaluation: The corrected KITTI split and clarified depth metrics make comparisons easier to interpret, although results from older downloaded datasets may require rebuilding.
  • 📖 Clearer user experience: Updated documentation explains task-specific inputs, export controls, model limitations, and platform troubleshooting in more practical terms.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.116...v8.4.117

v8.4.116: - Raise minimum opencv-python to 4.7.0 (#​25702)

Compare Source

🌟 Summary

🚀 v8.4.116 improves installation reliability, expands YOLOE and Platform workflows, strengthens tracking and export support, and refreshes YOLO26 documentation.

📊 Key Changes

  • 🔧 OpenCV compatibility fix — current PR #​25702 by @​Y-T-G

    • Raises the minimum opencv-python version from 4.6.0 to 4.7.0.
    • Keeps the exclusion for 4.13.0.90, which is affected by a FIPS self-test crash.
    • Removes an outdated ONNX DNN backend requirement check.
    • This aligns the dependency with cv2.imdecodemulti, which Ultralytics uses internally.

  • 🧠 Reusable YOLOE prompt embeddings

    • Adds save_prompt_embeddings() and load_prompt_embeddings() for storing text or visual prompt configurations in NPZ files.
    • Profiles are validated against the source YOLOE model and can be reused before exporting to formats such as ONNX, OpenVINO, TensorRT, CoreML, LiteRT, and RKNN.
    • Exported models remain standard single-input models and do not require the NPZ file at runtime.

  • 📚 Improved model guidance

    • Reworks the model index into a task-and-mode comparison table.
    • Positions YOLO26 as the recommended model for new projects, with YOLO11 as a mature production alternative.
    • Clarifies support for YOLO12, OBB, SAM models, YOLOE, YOLO-World, RT-DETR, and other model families.
    • Adds a YOLO26 custom-dataset training video and highlights monocular depth estimation.

  • 🎯 Broader and safer tracking support

    • Documents and supports OBB tracking alongside detection, segmentation, and pose.
    • Rejects unsupported semantic and depth tracking tasks with a clear error before processing begins.
    • Skips unnecessary camera-motion compensation work when gmc_method: none.
    • Keeps OC-SORT observation history bounded on all track lifecycle paths.

  • 📦 More efficient model export

    • Streams ONNX and QNN calibration data instead of retaining all transformed images in memory.
    • Reduces calibration memory usage substantially for large datasets.
    • Updates anchor creation to use CoreML-friendly tensor operations, improving dynamic CoreML export compatibility.

  • 🧪 Depth and segmentation fixes

    • Excludes ground-truth depth values outside the configured valid range during calibration, keeping calibration consistent with validation metrics.
    • Fixes FP16 segmentation with class-agnostic NMS.
    • Preserves YOLOE one-to-one classifier weights during linear probing, preventing a severe accuracy drop.
    • Makes pose activation-map gradients compatible with autograd and torch.compile.

  • 🖼️ Visualization and analytics improvements

    • Restores percentage labels in analytics pie charts.
    • Speeds up semantic-mask overlay rendering by replacing repeated full-image scans with a palette lookup.

  • ☁️ Expanded Ultralytics Platform workflows

    • Adds documented custom metadata support for datasets, images, projects, and models.
    • Supports nested metadata, metadata search, NDJSON image metadata, and Dataset Ingest API uploads.
    • Refreshes Platform integration screenshots and documents native Platform support for YOLOv8 and YOLOv5.
    • Refactors Platform callbacks to load only where needed, reducing unnecessary imports in prediction, validation, and export paths.

  • 🛡️ Reliability and infrastructure

    • Makes downloads atomic, preventing concurrent test or application processes from reading partially written files.
    • Updates CI runner images to Node.js 24 for compatibility with newer tooling.
    • Refreshes OpenVINO benchmark data across Intel CPUs and NPUs.
    • Improves documentation deployment detection when configuration defaults change.
🎯 Purpose & Impact
  • ✅ Fewer installation failures: Users relying on OpenCV image decoding now receive a compatible version automatically.
  • 🚀 Simpler YOLOE deployment: Prompt configurations can be prepared once and reused across multiple export targets.
  • 💾 Lower memory usage: Large ONNX and QNN calibration jobs are more practical, especially on limited-memory systems.
  • 🎥 More capable tracking: OBB tracking is now clearly supported, while unsupported tasks fail with actionable messages instead of obscure runtime errors.
  • 📈 Better model fine-tuning: YOLOE linear probing and depth calibration now preserve pretrained performance more reliably.
  • 🧩 Improved deployment compatibility: Dynamic CoreML exports and FP16 segmentation workflows are more robust.
  • 🔍 Better Platform organization: Custom metadata helps teams track provenance, review status, equipment, projects, and deployment context.
  • 📖 Clearer onboarding: The refreshed model documentation makes it easier to choose the right Ultralytics model and understand its supported modes.
What's Changed

✂ Note

PR body was truncated to here.

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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 15 times, most recently from d11412e to 61cef7a Compare August 10, 2026 01:08
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