| # MMYOLO Model Assigner Visualization | |
| <img src="https://user-images.githubusercontent.com/40284075/208255302-dbcf8cb0-b9d1-495f-8908-57dd2370dba8.png"/> | |
| ## Introduction | |
| This project is developed for easily showing assigning results. The script allows users to analyze where and how many positive samples each gt is assigned in the image. | |
| Now, the script supports `YOLOv5`, `YOLOv7`, `YOLOv8` and `RTMDet`. | |
| ## Usage | |
| ### Command | |
| YOLOv5 assigner visualization command: | |
| ```shell | |
| python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_assignervisualization.py | |
| ``` | |
| Note: `YOLOv5` does not need to load the trained weights. | |
| YOLOv7 assigner visualization command: | |
| ```shell | |
| python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/yolov7_tiny_syncbn_fast_8xb16-300e_coco_assignervisualization.py -c ${checkpont} | |
| ``` | |
| YOLOv8 assigner visualization command: | |
| ```shell | |
| python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/yolov8_s_syncbn_fast_8xb16-500e_coco_assignervisualization.py -c ${checkpont} | |
| ``` | |
| RTMdet assigner visualization command: | |
| ```shell | |
| python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/rtmdet_s_syncbn_fast_8xb32-300e_coco_assignervisualization.py -c ${checkpont} | |
| ``` | |
| ${checkpont} is the checkpont file path. Dynamic label assignment is used in `YOLOv7`, `YOLOv8` and `RTMDet`, model weights will affect the positive sample allocation results, so it is recommended to load the trained model weights. | |
| If you want to know details about label assignment, you can check the [RTMDet](https://mmyolo.readthedocs.io/zh_CN/latest/algorithm_descriptions/rtmdet_description.html#id5). | |