ultralytics 8.4.50

v8.4.50 - ultralytics `8.4.50` New Export Integration: Deepx (#23553)

on Python PyPI

🌟 Summary

Ultralytics 8.4.50 is mainly a deployment-focused release 🚀, led by a new DeepX export and inference integration that makes it easier to run YOLO models on DeepX NPU edge hardware, along with a few quality-of-life fixes for tuning reliability, mixed-precision model fusion, and RT-DETR documentation.

📊 Key Changes

• New DeepX export support added 🧠⚡
  You can now export Ultralytics models directly with format="deepx" as part of the normal export workflow.

  • Export first converts the model to ONNX, then compiles it into DeepX’s .dxnn format.
  • Export generates a complete deployment folder with:
    • compiled model file
    • config.json for preprocessing and calibration
    • metadata.yaml for model information
  • DeepX export is designed for INT8 quantized deployment and automatically enables int8=True.
  • If no calibration dataset is provided, it defaults to coco128.yaml.
  • Supports standard Ultralytics tasks across modern model families, including detection, segmentation, pose, OBB, and classification.

• New DeepX inference backend added 🔌
  Exported DeepX models can now be loaded back into Ultralytics for inference through a dedicated backend, making deployment more integrated and easier to use.

• DeepX documentation and references added 📘
  This release includes a full DeepX integration guide, reference pages, export docs, and navigation updates so users can more easily learn and adopt the new workflow.

• Hyperparameter tuning now reports failures more honestly 🛠️
  The tuner no longer claims all iterations completed successfully when training runs actually failed.

  • Failed runs are now reflected in the status output
  • Misleading best_hyperparameters.yaml files are no longer written when no valid result exists

• Fix for dtype issues during Conv/BN and Deconv/BN fusion 🎯
  Fusing models in float16 or bfloat16 now works correctly, avoiding dtype mismatch errors during optimization.

• RT-DETR docs improved with eval_idx guidance 📉➡️⚡
  The docs now explain how to reduce RT-DETR decoder layers at inference time to trade a small amount of accuracy for lower latency, without retraining.

🎯 Purpose & Impact

• Biggest impact: easier edge deployment on DeepX hardware 🚀
  This is the headline feature of 8.4.50. Users targeting low-power embedded AI systems can now export YOLO models directly into a format optimized for DeepX NPUs, making deployment much smoother.

• Better out-of-the-box hardware acceleration workflows ⚙️
  By automatically handling INT8 export, calibration setup, and preprocessing config generation, the new DeepX integration reduces manual work and lowers the barrier to specialized edge deployment.

• Improved production readiness for embedded use cases 🤖
  This helps teams building applications like smart cameras, robotics, industrial automation, and other edge AI systems where speed and power efficiency matter.

• More trustworthy tuning results ✅
  Users running hyperparameter search will now get clearer feedback when experiments fail, helping avoid false confidence and bad downstream decisions.

• More stable model optimization in reduced precision 🧩
  The fusion fix is especially useful for advanced users working with float16 or bfloat16, improving reliability in optimized inference pipelines.

• Helpful RT-DETR speed tuning guidance for latency-sensitive users ⏱️
  While this is docs-only, it gives users a practical way to make RT-DETR faster in deployment scenarios without changing training.

Notes

• DeepX export currently requires x86-64 Linux 🐧
• ARM64 export is not supported
• Inference support depends on the DeepX runtime and dx_engine

Overall, 8.4.50 is a strong deployment release, with DeepX integration as the standout addition 🌟—especially valuable for anyone moving Ultralytics models onto specialized edge AI hardware.

What's Changed

• Fix tuner reporting iterations complete when every iteration failed by @raimbekovm in #24477
• Docs: add RT-DETE eval_idx instruction by @artest08 in #24465
• Fix dtype mismatch in fuse_conv_and_bn and fuse_deconv_and_bn by @raimbekovm in #24480
• ultralytics 8.4.50 New Export Integration: Deepx by @ambitious-octopus in #23553

Full Changelog: v8.4.49...v8.4.50