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Useful tools

We provide lots of useful tools under the tools/ directory. In addition, you can also quickly run other open source libraries of OpenMMLab through MIM.

Take MMDetection as an example. If you want to use print_config.py, you can directly use the following commands without copying the source code to the MMYOLO library.

mim run mmdet print_config [CONFIG]

Note: The MMDetection library must be installed through the MIM before the above command can succeed.

Visualization

Visualize COCO labels

tools/analysis_tools/browse_coco_json.py is a script that can visualization to display the COCO label in the picture.

python tools/analysis_tools/browse_coco_json.py ${DATA_ROOT} \
                                                [--ann_file ${ANN_FILE}] \
                                                [--img_dir ${IMG_DIR}] \
                                                [--wait-time ${WAIT_TIME}] \
                                                [--disp-all] [--category-names CATEGORY_NAMES [CATEGORY_NAMES ...]] \
                                                [--shuffle]

E.g:

  1. Visualize all categories of COCO and display all types of annotations such as bbox and mask:
python tools/analysis_tools/browse_coco_json.py './data/coco/' \
                                                --ann_file 'annotations/instances_train2017.json' \
                                                --img_dir 'train2017' \
                                                --disp-all
  1. Visualize all categories of COCO, and display only the bbox type labels, and shuffle the image to show:
python tools/analysis_tools/browse_coco_json.py './data/coco/' \
                                                --ann_file 'annotations/instances_train2017.json' \
                                                --img_dir 'train2017' \
                                                --shuffle
  1. Only visualize the bicycle and person categories of COCO and only the bbox type labels are displayed:
python tools/analysis_tools/browse_coco_json.py './data/coco/' \
                                                --ann_file 'annotations/instances_train2017.json' \
                                                --img_dir 'train2017' \
                                                --category-names 'bicycle' 'person'
  1. Visualize all categories of COCO, and display all types of label such as bbox, mask, and shuffle the image to show:
python tools/analysis_tools/browse_coco_json.py './data/coco/' \
                                                --ann_file 'annotations/instances_train2017.json' \
                                                --img_dir 'train2017' \
                                                --disp-all \
                                                --shuffle

Visualize Datasets

tools/analysis_tools/browse_dataset.py helps the user to browse a detection dataset (both images and bounding box annotations) visually, or save the image to a designated directory.

python tools/analysis_tools/browse_dataset.py ${CONFIG} \
                                              [-h] \
                                              [--output-dir ${OUTPUT_DIR}] \
                                              [--not-show] \
                                              [--show-interval ${SHOW_INTERVAL}]

E,g：

  1. Use config file configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py to visualize the picture. The picture will pop up directly and be saved to the directory work dir/browse_ dataset at the same time:
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
                                               --output-dir 'work-dir/browse_dataset'
  1. Use config file configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py to visualize the picture. The picture will pop up and display directly. Each picture lasts for 10 seconds. At the same time, it will be saved to the directory work dir/browse_ dataset:
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
                                               --output-dir 'work-dir/browse_dataset' \
                                               --show-interval 10
  1. Use config file configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py to visualize the picture. The picture will pop up and display directly. Each picture lasts for 10 seconds and the picture will not be saved:
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
                                               --show-interval 10
  1. Use config file configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py to visualize the picture. The picture will not pop up directly, but only saved to the directory work dir/browse_ dataset:
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
                                               --output-dir 'work-dir/browse_dataset' \
                                               --not-show

Dataset Conversion

the tools/ directory also contains script to convert the balloon dataset (A small dataset is only for beginner use) into COCO format.

For a detailed description of this script, please refer to the "Dataset Preparation" section in From getting started to deployment with YOLOv5.

python tools/dataset_converters/balloon2coco.py

Dataset Download

tools/misc/download_dataset.py supports downloading datasets such as COCO, VOC, LVIS and Balloon.

python tools/misc/download_dataset.py --dataset-name coco2017
python tools/misc/download_dataset.py --dataset-name voc2007
python tools/misc/download_dataset.py --dataset-name lvis
python tools/misc/download_dataset.py --dataset-name balloon [--save-dir ${SAVE_DIR}] [--unzip]

Model Conversion

The three scripts under the tools/ directory can help users convert the keys in the official pre-trained model of YOLO to the format of MMYOLO, and use MMYOLO to fine tune the model.

YOLOv5

Take conversion yolov5s.pt as an example:

  1. Clone the official YOLOv5 code to the local (currently the maximum supported version is v6.1):
git clone -b v6.1 https://github.com/ultralytics/yolov5.git
cd yolov5
  1. Download official weight file:
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
  1. Copy file tools/model_converters/yolov5_to_mmyolo.py to the path of YOLOv5 official code clone:
cp ${MMDET_YOLO_PATH}/tools/model_converters/yolov5_to_mmyolo.py yolov5_to_mmyolo.py
  1. Conversion
python yolov5_to_mmyolo.py --src ${WEIGHT_FILE_PATH} --dst mmyolov5.pt

The converted mmyolov5.pt can be used by MMYOLO. The official weight conversion of YOLOv6 is also used in the same way.

YOLOX

The conversion of YOLOX model does not need to download the official YOLOX code, just download the weight.

Take conversion yolox_s.pth as an example:

  1. Download official weight file:
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth
  1. Conversion
python tools/model_converters/yolox_to_mmyolo.py --src yolox_s.pth --dst mmyolox.pt

The converted mmyolox.pt can be used by MMYOLO.