HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.0.203 DDP argparse and Tracker fixes (#6007)

Browse source
This commit is contained in:
Glenn Jocher 2023-10-29 21:14:50 +01:00 • committed by GitHub
parent ab0b47e386
commit 465df3024f
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
16 changed files with 50 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

1
docs/guides/index.md
View file

@ -15,6 +15,7 @@ Whether you're a beginner or an expert in deep learning, our tutorials offer val
Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO.
* [YOLO Common Issues](yolo-common-issues.md) ⭐ RECOMMENDED: Practical solutions and troubleshooting tips to the most frequently encountered issues when working with Ultralytics YOLO models.
* [YOLO Performance Metrics](yolo-performance-metrics.md) ⭐ ESSENTIAL: Understand the key metrics like mAP, IoU, and F1 score used to evaluate the performance of your YOLO models. Includes practical examples and tips on how to improve detection accuracy and speed.
* [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
* [Hyperparameter Tuning](hyperparameter-tuning.md) 🚀 NEW: Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms.
* [SAHI Tiled Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLOv8 for object detection in high-resolution images.

8
docs/guides/performance-metrics.md → docs/guides/yolo-performance-metrics.md
View file

@ -46,13 +46,13 @@ One of the sections of the output is the class-wise breakdown of performance met
- **Box(P, R, mAP50, mAP50-95)**: This metric provides insights into the model's performance in detecting objects:
- **P (Precision)**: The accuracy of the detected objects, indicating how many detections were correct.
- **P (Precision)**: The accuracy of the detected objects, indicating how many detections were correct.
- **R (Recall)**: The ability of the model to identify all instances of objects in the images.
- **R (Recall)**: The ability of the model to identify all instances of objects in the images.
- **mAP50**: Mean average precision calculated at an intersection over union (IoU) threshold of 0.50. It's a measure of the model's accuracy considering only the "easy" detections.
- **mAP50**: Mean average precision calculated at an intersection over union (IoU) threshold of 0.50. It's a measure of the model's accuracy considering only the "easy" detections.
- **mAP50-95**: The average of the mean average precision calculated at varying IoU thresholds, ranging from 0.50 to 0.95. It gives a comprehensive view of the model's performance across different levels of detection difficulty.
- **mAP50-95**: The average of the mean average precision calculated at varying IoU thresholds, ranging from 0.50 to 0.95. It gives a comprehensive view of the model's performance across different levels of detection difficulty.
#### Speed Metrics