pypi ultralytics 8.3.7
v8.3.7 - `ultralytics 8.3.7` custom Predictor args fix (#16734)
on Python PyPI

8 hours ago

🌟 Summary

The v8.3.7 release introduces several enhancements, with a focus on fixing argument handling for custom predictors in YOLO, along with updates for performance and stability improvements.

📊 Key Changes

  • Custom Predictor Argument Fix: Corrected the usage of arguments in model.predict() to support custom predictors.
  • Docker Image Update: The base Docker image is now upgraded to utilize PyTorch 2.4.1, with CUDA 12.1 and cuDNN 9.
  • New Script for Synthetic Datasets: Added a function to create synthetic COCO datasets, supporting data testing and augmentation.
  • Enhanced AutoBatch Memory Management: Improved GPU memory handling during autobatching to optimize resource usage.
  • Added OMP_NUM_THREADS=1: Adjusted Docker configurations for improved CPU management and performance.

🎯 Purpose & Impact

  • 🛠 Improved Custom Predictor Functionality: Ensures smoother operation for users implementing custom predictors, providing accurate argument handling on initialization.
  • 🚀 Performance Boost: The Docker update incorporates the latest improvements in PyTorch, enhancing model training and inference speed.
  • 🌟 Simplified Data Handling: The synthetic COCO dataset script facilitates the creation of testing datasets, helping developers easily prepare data for model validation.
  • 🧹 Efficient Resource Management: Changes in autobatch memory handling and Docker configuration aim to reduce memory overhead and CPU saturation, ensuring better performance on a variety of systems.
  • 📈 Streamlined Development: These updates collectively enhance the developer experience by optimizing workflows, reducing potential bugs, and improving code clarity.

What's Changed

Full Changelog: v8.3.6...v8.3.7

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