ultralytics 8.4.107

v8.4.107 - Add Huawei Ascend export (#25432)

on Python PyPI

🌟 Summary

Huawei Ascend support arrives in Ultralytics, enabling YOLO models to export and run as hardware-optimized .om models on Ascend NPUs. 🚀

📊 Key Changes

• Huawei Ascend export and inference 🧠

  • Adds format=ascend to compile YOLO models through Huawei’s CANN ATC compiler.
  • Produces self-describing .om files with metadata, targeting a specified Ascend SoC through name, such as Ascend310P3 or Ascend310B4.
  • Adds AutoBackend support using ais_bench for inference on Ascend hardware.
  • Supports detection, segmentation, pose, OBB, classification, semantic segmentation, and depth models.
  • Uses static-shape FP16 compilation and supports host-side export without requiring an attached Ascend device.
  • Adds aliases including huawei, cann, and om, while preventing cann from being incorrectly routed to NCNN.
  • Adds documentation and API references for the new Ascend exporter and inference backend.

• Ultralytics Platform support ☁️

  • Increases supported deployment formats from 19 to 20 by adding Huawei Ascend export.
  • Ascend export is now reflected throughout the Ultralytics Platform documentation and format selection guides.

• Faster and cleaner FLOPs profiling ⚡

  • Profiles models directly instead of creating expensive deep copies.
  • Removes the unused get_flops_with_torch_profiler utility.
  • Updates ultralytics-thop to version 2.1.0 or newer for improved profiling performance and cleanup reliability.

• More reliable distributed training 🌐

  • DDP now prefers random available ports below the operating system’s ephemeral range.
  • Reduces rendezvous failures caused by port reuse, while retaining an operating-system fallback.

• Tracking and evaluation fixes ✅

  • Restores TrackTrack deleted-detection recovery for PyTorch models.
  • Corrects class-name and AP alignment in PR_curve.png when classes have no predictions.
  • Prevents empty segmentation annotations from corrupting bounding boxes in GroundingDataset.
  • Makes optimizer names case-insensitive for MuSGD, so values such as musgd work correctly.

• Clearer low-disk-space reporting 💾

  • Reports available disk space in KB below 1 MB instead of displaying ambiguous 0.0 MB values.

🎯 Purpose & Impact

• Broader hardware deployment: Users targeting Huawei Atlas boards and OrangePi AIPro devices now have a direct path from a trained YOLO checkpoint to accelerated Ascend inference. 🏭🤖

• Simpler deployment workflow: Export with a familiar command such as:

  yolo export model=yolo26n.pt format=ascend name=Ascend310B4

  The generated model directory can then be loaded through standard Ultralytics prediction APIs.

• Improved edge performance potential: Ascend’s AI Core executes compiled FP16 models on-device, supporting low-power applications such as robotics, industrial inspection, and smart cameras.

• Faster development workflows: FLOPs profiling should require less time and memory, particularly for larger models.

• More dependable training and tracking: Distributed jobs, TrackTrack recovery, optimizer selection, and evaluation plots behave more consistently.

• Better diagnostics: Disk-space errors now provide actionable information, making it easier to distinguish a nearly full disk from one with only a small amount of free space.

⚠️ Ascend export requires the Linux-based CANN toolkit and the atc compiler. Ascend inference additionally requires the CANN runtime and ais_bench on the target device.

What's Changed

• Report disk space in KB below 1 MB by @glenn-jocher in #25433
• Profile models directly instead of copying them for FLOPs by @glenn-jocher in #25434
• Pick DDP rendezvous ports below the OS ephemeral range by @glenn-jocher in #25435
• Drop get_flops_with_torch_profiler and require thop>=2.1.0 by @glenn-jocher in #25438
• Fix TrackTrack deleted-detection recovery with PyTorch models by @JESUSROYETH in #25418
• Fix misaligned class labels and AP values in PR_curve.png when a class has no predictions by @JESUSROYETH in #25419
• Fix bbox corruption from empty segmentation in GroundingDataset by @JESUSROYETH in #25420
• Canonicalize the optimizer name before the MuSGD and auto checks by @JESUSROYETH in #25417
• Add Huawei Ascend export by @glenn-jocher in #25432

Full Changelog: v8.4.106...v8.4.107