pypi ultralytics 8.4.121
v8.4.121 - Fix OpenVINO INT8 detection head scope (#25841)
on Python PyPI

7 hours ago

🌟 Summary

v8.4.121 improves OpenVINO INT8 export reliability for YOLO26 models while delivering broad Platform API, dataset, annotation, deployment, and documentation updates. 🚀

📊 Key Changes

  • Fixed OpenVINO INT8 detection-head handling by @glenn-jocher:

    • Replaced fragile PyTorch-based layer matching with exact names from the converted OpenVINO graph.
    • Keeps Detect decoding, DFL, and Sigmoid operations in floating point as intended.
    • Preserves strict NNCF validation during quantization.
    • Verified with a successful YOLO26n-P2 INT8 export using 55 exact ignored operations, with no unwanted FakeQuantize nodes. ✅
    • This directly addresses export failures reported in Sentry and is the most important change in this release.

  • Expanded Ultralytics Platform API documentation:

    • Documented the generated ultralytics-platform Python SDK alongside REST examples.
    • Updated endpoint paths, authentication, pagination, rate limits, response formats, and OpenAPI guidance.
    • Added coverage for images, dataset ingestion, exports, storage integrations, billing, usage, trash, training, deployments, and account APIs.
    • Clarified that workspace API keys have owner-level permissions and are managed by workspace owners.

  • Improved Platform dataset and annotation workflows 🏷️:

    • Added clearer documentation for URL, cloud-storage, and On Premise dataset imports.
    • Documented class merging/deletion, conflict handling, dataset readiness checks, clustering, version restore, and expanded annotation controls.
    • Added support documentation for COCO and NDJSON imports, while clarifying that Pascal VOC XML labels are not imported.
    • Documented annotation visibility controls, copy/paste workflows, and new keyboard shortcuts.

  • Updated Platform account, billing, and team documentation 💳:

    • Added the Usage tab, detailed credit metering, monthly credit expiration, auto top-up behavior, seat billing, renewals, and downgrade effects.
    • Clarified workspace roles, owner-only API keys, team invitations, seat reuse, ownership transfer, and team deletion.
    • Expanded activity exports, trash permissions, storage usage, and account deletion guidance.

  • Improved deployment and inference documentation 🌐:

    • Clarified dedicated endpoint lifecycle operations, authentication, rate limits, model replacement, health checks, metrics, logs, and capacity behavior.
    • Documented video inference, endpoint-specific API references, depth response options, and generated deployment URLs.

  • Security and CI improvements 🔒:

    • Prevented checkout credentials from being copied into Docker images.
    • Moved workflow secrets into environment variables instead of embedding them in scripts.
    • Updated self-hosted runner cleanup actions to v1.4.39.
    • Reduced individual SlowTests attempts from 180 to 120 minutes while retaining one retry.
    • Changed Dependabot GitHub Actions checks from daily to weekly.

  • Dependency and documentation maintenance 📚:

    • Bumped the package version to 8.4.121.
    • Allowed newer setuptools and NNCF versions, including NNCF 3.x.
    • Updated Rust inference examples to ultralytics-inference 0.0.35.
    • Corrected task banners and documentation to consistently represent YOLO26’s supported task coverage.

🎯 Purpose & Impact

  • More dependable edge deployment: YOLO26 users exporting INT8 models through OpenVINO should see fewer conversion failures and more predictable quantization behavior. ⚡
  • Better model accuracy preservation: Keeping detection-head decode operations in floating point helps protect inference correctness while still applying INT8 compression elsewhere.
  • Easier Platform automation: The updated REST contract and generated Python SDK documentation make it simpler to build integrations, manage datasets, start training, create exports, and operate deployments.
  • Clearer team and billing behavior: Users can better understand permissions, shared workspace costs, credit expiration, seat charges, and training metering before taking action.
  • More capable dataset preparation: New import, annotation, class-management, clustering, and versioning guidance supports more complete end-to-end computer vision workflows.
  • Safer builds and more stable CI: Docker images are less likely to contain authentication credentials, while CI jobs are better protected against hangs and stale runner state. 🛡️

What's Changed

Full Changelog: v8.4.120...v8.4.121

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