mirror of https://github.com/open-mmlab/mmyolo.git
80 lines
4.3 KiB
Markdown
80 lines
4.3 KiB
Markdown
# Visualize dataset analysis
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`tools/analysis_tools/dataset_analysis.py` help users get the renderings of the four functions, and save the pictures to the `dataset_analysis` folder under the current running directory.
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Description of the script's functions:
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The data required by each sub function is obtained through the data preparation of `main()`.
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Function 1: Generated by the sub function `show_bbox_num` to display the distribution of categories and bbox instances.
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<img src="https://user-images.githubusercontent.com/90811472/200314770-4fb21626-72f2-4a4c-be5d-bf860ad830ec.jpg"/>
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Function 2: Generated by the sub function `show_bbox_wh` to display the width and height distribution of categories and bbox instances.
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<img src="https://user-images.githubusercontent.com/90811472/200315007-96e8e795-992a-4c72-90fa-f6bc00b3f2c7.jpg"/>
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Function 3: Generated by the sub function `show_bbox_wh_ratio` to display the width to height ratio distribution of categories and bbox instances.
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<img src="https://user-images.githubusercontent.com/90811472/200315044-4bdedcf6-087a-418e-8fe8-c2d3240ceba8.jpg"/>
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Function 3: Generated by the sub function `show_bbox_area` to display the distribution map of category and bbox instance area based on area rules.
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<img src="https://user-images.githubusercontent.com/90811472/200315075-71680fe2-db6f-4981-963e-a035c1281fc1.jpg"/>
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Print List: Generated by the sub function `show_class_list` and `show_data_list`.
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<img src="https://user-images.githubusercontent.com/90811472/200315152-9d6df91c-f2d2-4bba-9f95-b790fac37b62.jpg"/>
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```shell
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python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
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[--type ${TYPE}] \
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[--class-name ${CLASS_NAME}] \
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[--area-rule ${AREA_RULE}] \
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[--func ${FUNC}] \
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[--out-dir ${OUT_DIR}]
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```
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E,g：
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1.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, By default,the data loading type is `train_dataset`, the area rule is `[0,32,96,1e5]`, generate a result graph containing all functions and save the graph to the current running directory `./dataset_analysis` folder:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py
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```
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2.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the data loading type from the default `train_dataset` to `val_dataset` through the `--val-dataset` setting:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--val-dataset
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```
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3.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the display of all generated classes to specific classes. Take the display of `person` classes as an example:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--class-name person
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```
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4.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, redefine the area rule through `--area-rule` . Take `30 70 125` as an example, the area rule becomes `[0,30,70,125,1e5]`：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--area-rule 30 70 125
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```
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5.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the display of four function renderings to only display `Function 1` as an example:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--func show_bbox_num
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```
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6.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, modify the picture saving address to `work_dirs/dataset_analysis`:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--out-dir work_dirs/dataset_analysis
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```
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