🌟 Summary
The latest release, v8.3.33, primarily focuses on refining object counting in the Ultralytics YOLO framework, boosting accuracy for tracking objects across specified regions.
📊 Key Changes
- Object Counting Enhancement: Overhauled the object counting logic by focusing on centroids for more precise tracking, especially in complex shapes and motions.
- Updated Documentation: Clarified the
retina_masksanddevicearguments in the documentation for better user comprehension. - Expanded Hardware Compatibility: Enabled MNN export on Raspberry Pi and NVIDIA Jetson platforms.
- CI/CD Improvements: Upgraded GitHub workflow actions for better integration with Codecov and Slack.
🎯 Purpose & Impact
- Improved Counting Accuracy: By utilizing centroids over bounding boxes, the update ensures more reliable object tracking and counting, crucial for applications needing high precision. 🎯
- User Clarity: Enhanced documentation provides clearer guidelines, helping both novice and expert users understand configuration impacts better.
- Broader Device Support: Allowing MNN exports on more devices fosters flexibility and innovation, broadening the community's ability to deploy models on diverse hardware setups.
- Streamlined Workflows: Upgrades to GitHub actions contribute to more efficient development cycles and error handling, ensuring smoother operations and faster updates.
What's Changed
- Update
retina_masksdescription by @Y-T-G in #17587 - Enable MNN on RPi and Jetson by @lakshanthad in #17583
- Bump codecov/codecov-action from 4 to 5 in /.github/workflows by @dependabot[bot] in #17597
- Bump slackapi/slack-github-action from 1.27.0 to 2.0.0 in /.github/workflows by @dependabot[bot] in #17596
- Update
deviceargument description for benchmark by @Y-T-G in #17550 ultralytics 8.3.33Solutions counter direction fix by @RizwanMunawar in #17607
Full Changelog: v8.3.32...v8.3.33