yolov8 keypoint detection results about ultralytics HOT 2 OPEN

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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.

from ultralytics.

Hello! Thanks for reaching out and providing detailed insights into your training process. 😊 Let's address your concerns:

1. Discrepancy between Training Loss and Predicted Results: Training logs primarily show the model's ability to learn the representational features from the training data. However, actual performance can differ due to various factors such as overfitting, insufficient generalization, and differences between the training and validation/testing environments. It's important to ensure that your validation data closely represents real-world data.

2. Increasing the kobj Loss Weight: Adjusting the kobj (keypoint objectness loss gain) might impact the performance, specifically the accuracy of keypoint detection. Increasing kobj can help the model to focus more on the keypoints, but it might require experimental tuning to find the optimal balance without degrading other aspects of model performance.

3. Improving Keypoint Detection: Here are a couple of suggestions:

   • Data Augmentation: Consider increasing data diversity if your dataset lacks variation, especially in keypoints' positions or backgrounds.
   • Experiment with Hyperparameters: Besides adjusting kobj, tweaking other hyperparameters like learning rate or different loss gains might yield better performance.
   • Advanced Techniques: Implement advanced post-processing methods like Non-Maximum Suppression tailored for keypoints or explore other architectures known for better keypoint detection like HRNet if integrating externally.

Here is a snippet for adjusting kobj more dynamically:

from ultralytics import YOLO

# Load the model
model = YOLO('your_model_path.pt')

# Dynamically change the kobj
model.hyp['kobj'] = 1.5  # Increasing from 1.0 to 1.5

# Continue training or validation
# model.train(...)
# model.val(...)

Adjust the hyperparameters and methods as per your project's specific needs. Hope this helps, and wishing you success in your enhancements! 🚀

from ultralytics.