ultralytics 8.4.112

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

on Python PyPI

🌟 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• Removed outdated GraphDef benchmark restrictions ⚡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

• Document supported tasks for every export format by @glenn-jocher in #25511

Full Changelog: v8.4.111...v8.4.112