chore(deps): bump the pip-minor-and-patch group in /omniparser with 3 updates

[dependabot]

Bumps the pip-minor-and-patch group in /omniparser with 3 updates: ultralytics, supervision and einops.

Updates ultralytics from 8.3.70 to 8.4.51

Release notes

Sourced from ultralytics's releases.

v8.4.51 - Add Git commit message to training metadata (#24505)

🌟 Summary

Ultralytics v8.4.51 focuses mainly on better training traceability and clearer deployment/docs updates 📦📝, with the most important change adding the Git commit message to training metadata so models are easier to track, reproduce, and audit.

📊 Key Changes

• Training metadata now includes the Git commit message 🧾
  The headline update from @​glenn-jocher adds the current commit subject into:

  • saved checkpoints as git.message
  • Platform training environment metadata as gitCommitMessage
  • Git repository utilities via GitRepo.message

• More robust Git metadata handling 🔧
  Git repository parsing was improved to better read metadata from Git internals, including shared/worktree-style layouts. This helps Ultralytics capture version information more reliably during training.

• Major augmentation pipeline refactor 🛠️
  A substantial internal refactor by @​Laughing-q introduced a more unified transform system:

  • BaseTransform now standardizes how image, instance, and semantic-mask transforms are applied
  • augmentations like Mosaic, MixUp, CutMix, CopyPaste, RandomPerspective, RandomFlip, and LetterBox were reorganized around this shared structure

• OpenVINO docs updated with YOLO26 benchmarks 🚀
  The OpenVINO documentation now highlights YOLO26 benchmark results instead of older YOLO11 benchmarks, with refreshed performance data across newer Intel CPUs, GPUs, and NPUs.

• DeepX export documentation expanded 📤
  DeepX was added to the export formats table, with supported export arguments and output folder behavior documented more clearly.

• RT-DETR inference tuning guidance added ⚡
  Docs now explain that users can reduce query count for faster RT-DETR inference, helping users trade a bit of accuracy for lower latency when needed.

• YOLOE export behavior clarified ⚠️
  The docs now clearly warn that exported YOLOE models are static: once exported, prompt-based class configuration is baked into the model and cannot be changed later.

• Ultralytics Platform GPU docs refreshed ☁️
  Platform docs now reflect:

  • more available GPU types
  • new B300 GPU availability
  • updated plan access details
  • revised GPU pricing

• Test/CI compatibility improvement for Axelera export 🧪
  Axelera export tests are now limited to supported PyTorch versions, reducing false failures in CI.

• General documentation cleanup 📚
  Smaller updates include a fixed DeepX link, README simplification, removal of old Weglot docs overrides, and wording/casing polish across docs.

🎯 Purpose & Impact

• Easier experiment tracking and reproducibility 🔍
  Adding the Git commit message makes it much easier to tell what exact code change produced a trained model, especially when many experiments are run close together.

• Better debugging and collaboration 🤝
  Teams using local training or the Ultralytics Platform can now connect checkpoints and cloud runs to a human-readable commit description, not just a hash.

... (truncated)

Commits

• 14ea57b Add Git commit message to training metadata (#24505)
• ae09739 Update Platform GPU docs for B300 (#24506)
• d3d3116 Update OpenVINO benchmarks with YOLO26 (#24285)
• c8de052 docs: normalize casing for PyTorch / NumPy / Matplotlib in prose (#24499)
• 39d1d03 Warn that exported YOLOE models are static (#24496)
• ea872d4 docs: add RT-DETR query count tip (#24466)
• 0597497 Refactor BaseTransform and unify augmentation pipeline (#24434)
• aae4c3b Add DeepX to export formats table and optimize arg docs (#24488)
• 96d1d17 Scope Axelera export to 2.8.0<=torch<2.12.0 in Tests CI (#24487)
• a2d3970 Clean up integrations section in README (#24486)
• Additional commits viewable in compare view

Updates supervision from 0.18.0 to 0.28.0

Release notes

Sourced from supervision's releases.

supervision-0.28.0: CompactMask & SAM3

🔦 Spotlight

Memory-efficient masks with sv.CompactMask

Segmentation models produce one full-resolution bitmap per instance. On a 1920×1080 image with 28 detections that is ~55 MB of mask data. Most pixels are background. sv.CompactMask stores only the tight bounding-box crop, RLE-encoded — the same 28 masks drop to ~237 KB of crops, a 240× reduction before RLE kicks in.

It's a drop-in replacement: annotators, filters, and area all work unchanged.

import supervision as sv
any segmentation model — RF-DETR Seg, YOLO-Seg, SAM3
detections = model.predict(image)  # sv.Detections with dense masks
dense_mb = detections.mask.nbytes / 1024 / 1024
compact = sv.CompactMask.from_dense(
masks=detections.mask,
xyxy=detections.xyxy,
image_shape=image.shape[:2],
)
detections.mask = compact  # swap in — API unchanged
filter by pixel area without materialising dense masks
large = detections[compact.area > 1000]
annotators call .to_dense() internally
annotated = sv.MaskAnnotator().annotate(image.copy(), detections)

---

SAM3 text-prompted segmentation

SAM3 segments objects by free-text prompt — no class list, no bounding boxes. sv.Detections.from_sam3() parses both PCS (multi-prompt) and PVS (video) response formats into a standard sv.Detections, with class_id set to the prompt index.

import requests, base64
import supervision as sv
PROMPTS = ["person", "bag"]
with open("image.jpg", "rb") as f:
img_b64 = base64.b64encode(f.read()).decode()
response = requests.post(
</tr></table>

... (truncated)

Changelog

Sourced from supervision's changelog.

0.28.0 Apr 30, 2026

• Added #2159: sv.CompactMask for memory-efficient mask storage. Masks are stored as crop-region bounding boxes plus RLE-encoded data instead of full-resolution bitmaps, reducing memory by up to 240× for sparse masks. Integrates transparently with sv.Detections.mask — filtering, merging, and area all work without materialising the full array.

• Added #2227: sv.CompactMask.resize(new_image_shape) rescales all stored crops to match a new image resolution, enabling use across frames or after image resizing pipelines.

• Added #2178: sv.Detections.from_inference now supports compressed COCO RLE masks. Inference responses with rle or rle_mask fields containing a compressed counts string (as produced by pycocotools) are decoded directly into binary masks, avoiding a lossy polygon round-trip.

• Added #2004: sv.Color.from_hex now accepts 8-digit hexadecimal RGBA codes (e.g. #ff00ff80). Color.as_hex() serialises back, including alpha when not fully opaque. New utility functions sv.hex_to_rgba, sv.rgba_to_hex, and sv.is_valid_hex are exported at the top level.

• Added #709: sv.BlurAnnotator and sv.PixelateAnnotator now support dynamic sizing. When kernel_size=None or pixel_size=None (the new default), the size is computed per detection as a fraction of the shorter bounding-box dimension, producing consistent visual results across objects of different sizes.

• Added #2186: sv.InferenceSlicer now emits a warning when detections returned by the callback fall outside the tile boundaries, helping catch coordinate-system bugs in custom callbacks.

• Added #2103, #2152: New sv.Detections.from_sam3() classmethod parses SAM3 PCS (text-prompted) and PVS (visual-prompted video segmentation) response formats into a standard sv.Detections, both from the local inference package and from Roboflow-hosted server responses.

• Added #2154: The library now uses Python's logging module instead of print for diagnostic output. Messages are emitted under the supervision logger so applications can capture, filter, or silence them through standard logging configuration.

• Added #932: sv.ImageAssets for downloading sample images alongside existing video assets, useful for examples and tutorials.

• Changed #2169: sv.MeanAveragePrecisionResult and related metric arrays (mAP_scores, ap_per_class, iou_thresholds, precision/recall) are now float32 instead of float64. Reduces memory and speeds up computation; numerical results may differ in the last few digits.

• Changed #2178: sv.rle_to_mask and sv.mask_to_rle moved to supervision.detection.utils.converters. The old import path supervision.dataset.utils continues to work but is deprecated.

• Fixed #2178: sv.rle_to_mask now returns NDArray[bool] as declared in its signature. Previously the implementation returned uint8 despite the bool annotation; code that relied on the undocumented uint8 output (e.g. mask * 255 producing uint8) should wrap the result with .astype(np.uint8).

• Fixed #2210: sv.VideoInfo.fps now returns a float instead of a truncated int. Previously, frame rates like 23.976, 29.97, and 59.94 were silently truncated, causing frame-timing drift that accumulates over long videos. The type of VideoInfo.fps has changed from int to float; callers that pass fps to APIs requiring an integer (such as deque(maxlen=...) or TraceAnnotator(trace_length=...)) should wrap the value with int().

• Fixed #2209: sv.Detections.is_empty() now returns True for detections filtered down to zero rows, even when tracker_id is an empty array. Previously this case incorrectly returned False.

• Fixed #2199: sv.CSVSink now correctly slices numpy array values in custom_data per row. Previously the full array was written for every detection.

• Fixed #2216: sv.CSVSink and sv.JSONSink now slice plain Python list and tuple values in custom_data per detection row. Lists and tuples matching the detection count are indexed per row, consistent with np.ndarray behavior.

• Fixed #2217: sv.TraceAnnotator no longer crashes in smooth mode when a tracker remains stationary. Duplicate consecutive points caused splprep to fail; the annotator now deduplicates anchor points and falls back to a raw polyline when fewer than 4 unique points are available.

• Fixed #2218: load_coco_annotations now rejects COCO annotations whose file_name escapes the images directory via ../ traversal or absolute paths, preventing path-traversal attacks from malicious annotation files.

• Fixed #2187: Extreme memory usage when loading OBB (oriented bounding box) datasets, caused by allocating full-image masks for each rotated box, has been resolved.

• Fixed #2188: sv.KeyPoints boolean mask indexing now works correctly when all instances have the same keypoint count (uniform-count selection).

• Fixed #2185: sv.DetectionDataset.as_coco() now preserves area and iscrowd fields instead of silently dropping them in the round-trip.

• Fixed #1746: Precision loss when converting annotations with force_mask=True in dataset format converters.

• Fixed #1991: sv.PolygonZone no longer double-counts the same object when multiple zones overlap. Detection bounding boxes were incorrectly clipped to each zone's ROI before anchor computation, causing the same detection to appear at a different anchor point in each zone; anchor is now computed from the original bounding box so containment is independent per zone.

• Fixed #1868: sv.LineZone no longer mis-attributes crossings when a tracker reuses the same tracker_id across different classes. Class-aware bookkeeping prevents a new object from inheriting another class's prior crossing state.

... (truncated)

Commits

• 87b1b86 releasing 0.28.0 (#2220)
• 1170a90 chore(pre_commit): ⬆ pre_commit autoupdate (#2235)
• 2e0e8d9 ⬆️ Update wheel requirement from <0.47,>=0.40 to >=0.40,<0.48 (#2234)
• 15730b2 docs: update author references and improve metadata (#2233)
• 2db1e3a docs: improve SEO and GEO docs publishing (#2232)
• 840226a chore: update pre-commit hooks configuration
• dc03cea fix(docs): expand llms.txt, add FAQ schema, author attribution to how… (#2231)
• f9ab57a ⬆️ Bump nbconvert from 7.17.0 to 7.17.1 in the uv group across 1 directory (#...
• fb19852 chore(pre_commit): ⬆ pre_commit autoupdate (#2225)
• 60f8d28 releasing 0.28.0.rc2
• Additional commits viewable in compare view

Updates einops from 0.8.0 to 0.8.2

Release notes

Sourced from einops's releases.

v0.8.1 Multiple improvements

What's Changed

TLDR:

• ellipsis is added to EinMix
• tests moved into the package
• devcontainer provided
• added backend for pyTensor
• niceties: citation, docs, fixed broken links
• this did not require any changes in einops, but array API is supported by more libs these days, and einops can operate on them

PRs:

• Update README.md by @​project-delphi in arogozhnikov/einops#316
• change tests for numpy2.0 + fix for numpy regression by @​arogozhnikov in arogozhnikov/einops#325
• PR from @​pzread: fix tinygrad concat + changed formatting to fix CI errors by @​arogozhnikov in arogozhnikov/einops#329
• Add devcontainer and dockerfile by @​arogozhnikov in arogozhnikov/einops#330
• delete experimental/indexing by @​arogozhnikov in arogozhnikov/einops#333
• Use same ruff version in CI and in pre-commit, reformat notebooks by @​arogozhnikov in arogozhnikov/einops#336
• Delete chainer backend by @​arogozhnikov in arogozhnikov/einops#335
• Mention support of cupy, pydata/sparse, ndonnx and others by @​arogozhnikov in arogozhnikov/einops#337
• Moving tests into source tree by @​arogozhnikov in arogozhnikov/einops#338
• Update documentation, mention changes in testing + devcontainers by @​arogozhnikov in arogozhnikov/einops#339
• Auto-deploy mkdocs + update mkdocs requirements by @​arogozhnikov in arogozhnikov/einops#341
• Add dict syntax to the einops layers example by @​eadadi in arogozhnikov/einops#342
• include CITATION.cff for einops by @​luke-carlson in arogozhnikov/einops#345
• mention that cupy and jax now support array api by @​arogozhnikov in arogozhnikov/einops#348
• Fix docs broken links after master -> main migration by @​VladKha in arogozhnikov/einops#349
• [Paddle] Update _backends.py to support pir.Value by @​HydrogenSulfate in arogozhnikov/einops#353
• Update docstring for einops.reduce by @​arogozhnikov in arogozhnikov/einops#358
• add a comment about axes names by @​arogozhnikov in arogozhnikov/einops#359
• Add support of ellipsis to einmix by @​arogozhnikov in arogozhnikov/einops#360
• Add PyTensor backend by @​ricardoV94 in arogozhnikov/einops#362
• rename input_dict, document parameter by @​arogozhnikov in arogozhnikov/einops#363

New Contributors

• @​project-delphi made their first contribution in arogozhnikov/einops#316
• @​eadadi made their first contribution in arogozhnikov/einops#342
• @​luke-carlson made their first contribution in arogozhnikov/einops#345
• @​VladKha made their first contribution in arogozhnikov/einops#349
• @​HydrogenSulfate made their first contribution in arogozhnikov/einops#353
• @​ricardoV94 made their first contribution in arogozhnikov/einops#362

Full Changelog: arogozhnikov/einops@v0.8.0...v0.8.1

Commits

• 8e911db update testing (#395)
• 3cdcbd5 add mlx backend (#376)
• 5197457 Skip allow_in_graph if torch >= 2.8 (#394)
• 3bd7454 switch off debug importing (#393)
• ab3f630 Patch #391 (conflicts in multithreading initialization) (#392)
• 43a12ee A bit more of compile tests (#390)
• 71b3b23 mention ein color and AF3 (#389)
• 361b11e add einops.jl and einops for R in docs_src/pages/projects.md (#383)
• b131c81 Extend tests for torch.compile (#382)
• 5dac404 mention cubed as one more backend (#375)
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore <dependency name> major version will close this group update PR and stop Dependabot creating any more for the specific dependency's major version (unless you unignore this specific dependency's major version or upgrade to it yourself)
• @dependabot ignore <dependency name> minor version will close this group update PR and stop Dependabot creating any more for the specific dependency's minor version (unless you unignore this specific dependency's minor version or upgrade to it yourself)
• @dependabot ignore <dependency name> will close this group update PR and stop Dependabot creating any more for the specific dependency (unless you unignore this specific dependency or upgrade to it yourself)
• @dependabot unignore <dependency name> will remove all of the ignore conditions of the specified dependency
• @dependabot unignore <dependency name> <ignore condition> will remove the ignore condition of the specified dependency and ignore conditions

[dependabot]

Labels

The following labels could not be found: dependencies, security. Please create them before Dependabot can add them to a pull request.

Please fix the above issues or remove invalid values from dependabot.yml.