mirror of https://github.com/open-mmlab/mmyolo.git
44 lines
1.8 KiB
Markdown
44 lines
1.8 KiB
Markdown
# MMYOLO Model Assigner Visualization
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<img src="https://user-images.githubusercontent.com/40284075/208255302-dbcf8cb0-b9d1-495f-8908-57dd2370dba8.png"/>
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## Introduction
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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.
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Now, the script supports `YOLOv5`, `YOLOv7`, `YOLOv8` and `RTMDet`.
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## Usage
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### Command
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YOLOv5 assigner visualization command:
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```shell
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python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_assignervisualization.py
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```
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Note: `YOLOv5` does not need to load the trained weights.
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YOLOv7 assigner visualization command:
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```shell
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python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/yolov7_tiny_syncbn_fast_8xb16-300e_coco_assignervisualization.py -c ${checkpont}
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```
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YOLOv8 assigner visualization command:
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```shell
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python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/yolov8_s_syncbn_fast_8xb16-500e_coco_assignervisualization.py -c ${checkpont}
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```
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RTMdet assigner visualization command:
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```shell
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python projects/assigner_visualization/assigner_visualization.py projects/assigner_visualization/configs/rtmdet_s_syncbn_fast_8xb32-300e_coco_assignervisualization.py -c ${checkpont}
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```
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${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.
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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).
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