Ultralytics YOLO

Glenn Jocher, Jing Qiu, Ayush Chaurasia

🌟 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 https://github.com/ultralytics/ultralytics/pull/25433 Profile models directly instead of copying them for FLOPs by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25434 Pick DDP rendezvous ports below the OS ephemeral range by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25435 Drop get_flops_with_torch_profiler and require thop>=2.1.0 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25438 Fix TrackTrack deleted-detection recovery with PyTorch models by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25418 Fix misaligned class labels and AP values in PR_curve.png when a class has no predictions by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25419 Fix bbox corruption from empty segmentation in GroundingDataset by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25420 Canonicalize the optimizer name before the MuSGD and auto checks by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25417 Add Huawei Ascend export by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25432 Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.106...v8.4.107