zh/datasets/pose/hand-keypoints/

[giscus[bot]]

zh/datasets/pose/hand-keypoints/

探索用于高级姿势估计的手部关键点估计数据集。了解使用YOLO 进行训练的数据集、预训练模型、指标和应用。

https://docs.ultralytics.com/zh/datasets/pose/hand-keypoints/

[berlintop]

I used yolov11n and yolov11x to train many models based on this dataset. The log accuracy of small batch and large epoch training is very good, but it is very poor during actual inference, much worse than mediaPipa, especially when rotating, the key points are very inaccurate. I am very confused about this, is there any trick to the training parameters?

[glenn-jocher]

@737296 the issue you described could be due to overfitting or suboptimal augmentation and training practices. To address inaccuracies during inference, consider increasing data augmentation (e.g., rotation, scaling), fine-tuning learning rates or epochs, and ensuring sufficient diversity in the dataset. MediaPipe's robustness might stem from its architecture and preprocessing, so refining training parameters or exploring a different YOLO11 variant could help. Refer to the Hand Keypoints Dataset documentation for guidance on dataset setup and training. Let us know if further clarification is needed!

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! It’s great to see your exploration of hand keypoints estimation datasets for advanced pose estimation using YOLO. We recommend visiting the Docs for an in-depth understanding of this topic, including datasets, pretrained models, metrics, and potential applications.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please include detailed information such as example data, training configurations, and any relevant logs. Make sure to follow our Tips for Best Training Results for optimal performance.

Join the Ultralytics community where it suits you best:

• For real-time discussion, connect on Discord 🎧
• Share or browse insights on Discourse
• Engage with our community on Reddit

Upgrade

Ensure you’re using the latest version of ultralytics with all requirements in a Python>=3.8 environment and PyTorch>=1.8 for best results:

pip install -U ultralytics

Environments

Run YOLO in these verified environments with all dependencies preinstalled:

• Notebooks with free GPU:
• Google Cloud Deep Learning VM: GCP Quickstart Guide
• Amazon Deep Learning AMI: AWS Quickstart Guide
• Docker Image: Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLO Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

This is an automated response, but don’t worry! An Ultralytics engineer will be here shortly to assist you further 😊.