Comments (2)
👋 Hello @kleopard, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
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 provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
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Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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The thresholding of keypoints with a confidence of less than 0.5 to coordinates of 0 is a method to enhance the reliability of the keypoints detected by the model. Here, keypoints with low confidence are typically considered unreliable or 'not visible,' thus setting them to 0 helps in disregarding these points for further processing or analysis.
This implementation effectively filters out less confident detections, ensuring that only keypoints with a reasonably high confidence influence downstream tasks, such as pose estimation, thereby improving accuracy and robustness of results.
If keypoint precision is crucial for your application and you wish to handle low confidence keypoints differently, consider adjusting the confidence threshold or modifying how such keypoints are utilized in your workflow. Here's how you might adjust the confidence threshold:
confidence_threshold = 0.3 # Example threshold
mask = keypoints[..., 2] < confidence_threshold
keypoints[..., :2][mask] = 0
I hope this clarifies your query! 😊
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Related Issues (20)
- (YOLOv8的anchor机制,可以根据训练样本自动调整anchor吗?anchor是聚类生成,不是设定的吧?)Can the yolov8 training process automatically adjust the anchor size according to the anchor of the training set? Since my detection targets are all small targets, it should be better to adjust anchor HOT 2
- ultralytics 8.2.26 export to openvino int8 quantization, performance drop significantly HOT 6
- Why pad 0.5 here? HOT 2
- GPU_mem not correlated with task manager GPU memory usage HOT 3
- Using BayesOpt as Search Algorithm in Yolov8 Segmentation HOT 5
- YOLOV8 CBAM adding issuse HOT 7
- v8Detection loss backward HOT 3
- how to change a label's name? HOT 7
- Enforce tests install for `thop` package
- about physical memory and virtual memory HOT 3
- models/yolov9/ HOT 8
- ImportError: cannot import name 'YOLOv10' from 'ultralytics IDE: VisualStudio HOT 7
- Loss Decrease after Resuming from last.pt HOT 3
- The result of val in confusion matrix HOT 5
- using multi class segmentation dataset for lower number of class segmentation task? HOT 3
- About TensorRT speed test HOT 7
- Output shape of [1,5,2100] HOT 4
- RT-DETR model hyperparameters HOT 4
- cannot set tensor for ultralytics/examples/YOLOv8-OpenCV-int8-tflite-Python /main.py HOT 2
- Object tracking, where is the c++ example? HOT 1
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