mirror of https://github.com/open-mmlab/mmyolo.git
521 lines
22 KiB
Markdown
521 lines
22 KiB
Markdown
# 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](https://github.com/open-mmlab/mmdetection/blob/3.x/tools/misc/print_config.py), you can directly use the following commands without copying the source code to the MMYOLO library.
|
||
|
||
```shell
|
||
mim run mmdet print_config ${CONFIG}
|
||
```
|
||
|
||
## 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.
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_coco_json.py [--data-root ${DATA_ROOT}] \
|
||
[--img-dir ${IMG_DIR}] \
|
||
[--ann-file ${ANN_FILE}] \
|
||
[--wait-time ${WAIT_TIME}] \
|
||
[--disp-all] [--category-names CATEGORY_NAMES [CATEGORY_NAMES ...]] \
|
||
[--shuffle]
|
||
```
|
||
|
||
If images and labels are in the same folder, you can specify `--data-root` to the folder, and then `--img-dir` and `--ann-file` to specify the relative path of the folder. The code will be automatically spliced.
|
||
If the image and label files are not in the same folder, you do not need to specify `--data-root`, but directly specify `--img-dir` and `--ann-file` of the absolute path.
|
||
|
||
E.g:
|
||
|
||
1. Visualize all categories of `COCO` and display all types of annotations such as `bbox` and `mask`:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_coco_json.py --data-root './data/coco' \
|
||
--img-dir 'train2017' \
|
||
--ann-file 'annotations/instances_train2017.json' \
|
||
--disp-all
|
||
```
|
||
|
||
If images and labels are not in the same folder, you can use a absolutely path:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_coco_json.py --img-dir '/dataset/image/coco/train2017' \
|
||
--ann-file '/label/instances_train2017.json' \
|
||
--disp-all
|
||
```
|
||
|
||
2. Visualize all categories of `COCO`, and display only the `bbox` type labels, and shuffle the image to show:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_coco_json.py --data-root './data/coco' \
|
||
--img-dir 'train2017' \
|
||
--ann-file 'annotations/instances_train2017.json' \
|
||
--shuffle
|
||
```
|
||
|
||
3. Only visualize the `bicycle` and `person` categories of `COCO` and only the `bbox` type labels are displayed:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_coco_json.py --data-root './data/coco' \
|
||
--img-dir 'train2017' \
|
||
--ann-file 'annotations/instances_train2017.json' \
|
||
--category-names 'bicycle' 'person'
|
||
```
|
||
|
||
4. Visualize all categories of `COCO`, and display all types of label such as `bbox`, `mask`, and shuffle the image to show:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_coco_json.py --data-root './data/coco' \
|
||
--img-dir 'train2017' \
|
||
--ann-file 'annotations/instances_train2017.json' \
|
||
--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.
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_dataset.py ${CONFIG} \
|
||
[--out-dir ${OUT_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_dirs/browse_ dataset` at the same time:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
|
||
--out-dir 'work_dirs/browse_dataset'
|
||
```
|
||
|
||
2. 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_dirs/browse_ dataset`:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
|
||
--out-dir 'work_dirs/browse_dataset' \
|
||
--show-interval 10
|
||
```
|
||
|
||
3. 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:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
|
||
--show-interval 10
|
||
```
|
||
|
||
4. 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_dirs/browse_ dataset`:
|
||
|
||
```shell
|
||
python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py' \
|
||
--out-dir 'work_dirs/browse_dataset' \
|
||
--not-show
|
||
```
|
||
|
||
### Visualize dataset analysis
|
||
|
||
`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.
|
||
|
||
Description of the script's functions:
|
||
|
||
The data required by each sub function is obtained through the data preparation of `main()`.
|
||
|
||
Function 1: Generated by the sub function `show_bbox_num` to display the distribution of categories and bbox instances.
|
||
|
||
<img src="https://user-images.githubusercontent.com/90811472/200314770-4fb21626-72f2-4a4c-be5d-bf860ad830ec.jpg"/>
|
||
|
||
Function 2: Generated by the sub function `show_bbox_wh` to display the width and height distribution of categories and bbox instances.
|
||
|
||
<img src="https://user-images.githubusercontent.com/90811472/200315007-96e8e795-992a-4c72-90fa-f6bc00b3f2c7.jpg"/>
|
||
|
||
Function 3: Generated by the sub function `show_bbox_wh_ratio` to display the width to height ratio distribution of categories and bbox instances.
|
||
|
||
<img src="https://user-images.githubusercontent.com/90811472/200315044-4bdedcf6-087a-418e-8fe8-c2d3240ceba8.jpg"/>
|
||
|
||
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.
|
||
|
||
<img src="https://user-images.githubusercontent.com/90811472/200315075-71680fe2-db6f-4981-963e-a035c1281fc1.jpg"/>
|
||
|
||
Print List: Generated by the sub function `show_class_list` and `show_data_list`.
|
||
|
||
<img src="https://user-images.githubusercontent.com/90811472/200315152-9d6df91c-f2d2-4bba-9f95-b790fac37b62.jpg"/>
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
|
||
[--type ${TYPE}] \
|
||
[--class-name ${CLASS_NAME}] \
|
||
[--area-rule ${AREA_RULE}] \
|
||
[--func ${FUNC}] \
|
||
[--out-dir ${OUT_DIR}]
|
||
```
|
||
|
||
E,g：
|
||
|
||
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:
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py
|
||
```
|
||
|
||
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:
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
|
||
--val-dataset
|
||
```
|
||
|
||
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:
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
|
||
--class-name person
|
||
```
|
||
|
||
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]`：
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
|
||
--area-rule 30 70 125
|
||
```
|
||
|
||
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:
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
|
||
--func show_bbox_num
|
||
```
|
||
|
||
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`:
|
||
|
||
```shell
|
||
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
|
||
--out-dir work_dirs/dataset_analysis
|
||
```
|
||
|
||
### Hyper-parameter Scheduler Visualization
|
||
|
||
`tools/analysis_tools/vis_scheduler` aims to help the user to check the hyper-parameter scheduler of the optimizer(without training), which support the "learning rate", "momentum", and "weight_decay".
|
||
|
||
```bash
|
||
python tools/analysis_tools/vis_scheduler.py \
|
||
${CONFIG_FILE} \
|
||
[-p, --parameter ${PARAMETER_NAME}] \
|
||
[-d, --dataset-size ${DATASET_SIZE}] \
|
||
[-n, --ngpus ${NUM_GPUs}] \
|
||
[-o, --out-dir ${OUT_DIR}] \
|
||
[--title ${TITLE}] \
|
||
[--style ${STYLE}] \
|
||
[--window-size ${WINDOW_SIZE}] \
|
||
[--cfg-options]
|
||
```
|
||
|
||
**Description of all arguments**：
|
||
|
||
- `config`: The path of a model config file.
|
||
- **`-p, --parameter`**: The param to visualize its change curve, choose from "lr", "momentum" or "wd". Default to use "lr".
|
||
- **`-d, --dataset-size`**: The size of the datasets. If set，`DATASETS.build` will be skipped and `${DATASET_SIZE}` will be used as the size. Default to use the function `DATASETS.build`.
|
||
- **`-n, --ngpus`**: The number of GPUs used in training, default to be 1.
|
||
- **`-o, --out-dir`**: The output path of the curve plot, default not to output.
|
||
- `--title`: Title of figure. If not set, default to be config file name.
|
||
- `--style`: Style of plt. If not set, default to be `whitegrid`.
|
||
- `--window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`.
|
||
- `--cfg-options`: Modifications to the configuration file, refer to [Learn about Configs](../user_guides/config.md).
|
||
|
||
```{note}
|
||
Loading annotations maybe consume much time, you can directly specify the size of the dataset with `-d, dataset-size` to save time.
|
||
```
|
||
|
||
You can use the following command to plot the step learning rate schedule used in the config `configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py`:
|
||
|
||
```shell
|
||
python tools/analysis_tools/vis_scheduler.py \
|
||
configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py \
|
||
--dataset-size 118287 \
|
||
--ngpus 8 \
|
||
--out-dir ./output
|
||
```
|
||
|
||
<div align=center><img src="https://user-images.githubusercontent.com/27466624/213091635-d322d2b3-6e28-4755-b871-ef0a89a67a6b.jpg" style=" width: auto; height: 40%; "></div>
|
||
|
||
## Dataset Conversion
|
||
|
||
The folder `tools/data_converters` currently contains `ballon2coco.py`, `yolo2coco.py`, and `labelme2coco.py` - three dataset conversion tools.
|
||
|
||
- `ballon2coco.py` converts the `balloon` dataset (this small dataset is for starters only) to COCO format.
|
||
|
||
For a detailed description of this script, please see the `Dataset Preparation` section in [From getting started to deployment with YOLOv5](./yolov5_tutorial.md).
|
||
|
||
```shell
|
||
python tools/dataset_converters/balloon2coco.py
|
||
```
|
||
|
||
- `yolo2coco.py` converts a dataset from `yolo-style` **.txt** format to COCO format, please use it as follows:
|
||
|
||
```shell
|
||
python tools/dataset_converters/yolo2coco.py /path/to/the/root/dir/of/your_dataset
|
||
```
|
||
|
||
Instructions:
|
||
|
||
1. `image_dir` is the root directory of the yolo-style dataset you need to pass to the script, which should contain `images`, `labels`, and `classes.txt`. `classes.txt` is the class declaration corresponding to the current dataset. One class a line. The structure of the root directory should be formatted as this example shows:
|
||
|
||
```bash
|
||
.
|
||
└── $ROOT_PATH
|
||
├── classes.txt
|
||
├── labels
|
||
│ ├── a.txt
|
||
│ ├── b.txt
|
||
│ └── ...
|
||
├── images
|
||
│ ├── a.jpg
|
||
│ ├── b.png
|
||
│ └── ...
|
||
└── ...
|
||
```
|
||
|
||
2. The script will automatically check if `train.txt`, `val.txt`, and `test.txt` have already existed under `image_dir`. If these files are located, the script will organize the dataset accordingly. Otherwise, the script will convert the dataset into one file. The image paths in these files must be **ABSOLUTE** paths.
|
||
3. By default, the script will create a folder called `annotations` in the `image_dir` directory which stores the converted JSON file. If `train.txt`, `val.txt`, and `test.txt` are not found, the output file is `result.json`. Otherwise, the corresponding JSON file will be generated, named as `train.json`, `val.json`, and `test.json`. The `annotations` folder may look similar to this:
|
||
|
||
```bash
|
||
.
|
||
└── $ROOT_PATH
|
||
├── annotations
|
||
│ ├── result.json
|
||
│ └── ...
|
||
├── classes.txt
|
||
├── labels
|
||
│ ├── a.txt
|
||
│ ├── b.txt
|
||
│ └── ...
|
||
├── images
|
||
│ ├── a.jpg
|
||
│ ├── b.png
|
||
│ └── ...
|
||
└── ...
|
||
```
|
||
|
||
## Download Dataset
|
||
|
||
`tools/misc/download_dataset.py` supports downloading datasets such as `COCO`, `VOC`, `LVIS` and `Balloon`.
|
||
|
||
```shell
|
||
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 voc2012
|
||
python tools/misc/download_dataset.py --dataset-name lvis
|
||
python tools/misc/download_dataset.py --dataset-name balloon [--save-dir ${SAVE_DIR}] [--unzip]
|
||
```
|
||
|
||
## Convert Model
|
||
|
||
The six scripts under the `tools/model_converters` 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`):
|
||
|
||
```shell
|
||
git clone -b v6.1 https://github.com/ultralytics/yolov5.git
|
||
cd yolov5
|
||
```
|
||
|
||
2. Download official weight file:
|
||
|
||
```shell
|
||
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
|
||
```
|
||
|
||
3. Copy file `tools/model_converters/yolov5_to_mmyolo.py` to the path of YOLOv5 official code clone:
|
||
|
||
```shell
|
||
cp ${MMDET_YOLO_PATH}/tools/model_converters/yolov5_to_mmyolo.py yolov5_to_mmyolo.py
|
||
```
|
||
|
||
4. Conversion
|
||
|
||
```shell
|
||
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:
|
||
|
||
```shell
|
||
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth
|
||
```
|
||
|
||
2. Conversion
|
||
|
||
```shell
|
||
python tools/model_converters/yolox_to_mmyolo.py --src yolox_s.pth --dst mmyolox.pt
|
||
```
|
||
|
||
The converted `mmyolox.pt` can be used by MMYOLO.
|
||
|
||
## Optimize anchors size
|
||
|
||
Script `tools/analysis_tools/optimize_anchors.py` supports three methods to optimize YOLO anchors including `k-means`
|
||
anchor cluster, `Differential Evolution` and `v5-k-means`.
|
||
|
||
### k-means
|
||
|
||
In k-means method, the distance criteria is based IoU, python shell as follow:
|
||
|
||
```shell
|
||
python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
|
||
--algorithm k-means \
|
||
--input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} \
|
||
--out-dir ${OUT_DIR}
|
||
```
|
||
|
||
### Differential Evolution
|
||
|
||
In differential_evolution method, based differential evolution algorithm, use `avg_iou_cost` as minimum target function, python shell as follow:
|
||
|
||
```shell
|
||
python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
|
||
--algorithm DE \
|
||
--input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} \
|
||
--out-dir ${OUT_DIR}
|
||
```
|
||
|
||
### v5-k-means
|
||
|
||
In v5-k-means method, clustering standard as same with YOLOv5 which use shape-match, python shell as follow:
|
||
|
||
```shell
|
||
python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
|
||
--algorithm v5-k-means \
|
||
--input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} \
|
||
--prior_match_thr ${PRIOR_MATCH_THR} \
|
||
--out-dir ${OUT_DIR}
|
||
```
|
||
|
||
## Perform inference on large images
|
||
|
||
First install [`sahi`](https://github.com/obss/sahi) with:
|
||
|
||
```shell
|
||
pip install -U sahi>=0.11.4
|
||
```
|
||
|
||
Perform MMYOLO inference on large images (as satellite imagery) as:
|
||
|
||
```shell
|
||
wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth
|
||
|
||
python demo/large_image_demo.py \
|
||
demo/large_image.jpg \
|
||
configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
|
||
checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
|
||
```
|
||
|
||
Arrange slicing parameters as:
|
||
|
||
```shell
|
||
python demo/large_image_demo.py \
|
||
demo/large_image.jpg \
|
||
configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
|
||
checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
|
||
--patch-size 512
|
||
--patch-overlap-ratio 0.25
|
||
```
|
||
|
||
Export debug visuals while performing inference on large images as:
|
||
|
||
```shell
|
||
python demo/large_image_demo.py \
|
||
demo/large_image.jpg \
|
||
configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
|
||
checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
|
||
--debug
|
||
```
|
||
|
||
[`sahi`](https://github.com/obss/sahi) citation:
|
||
|
||
```
|
||
@article{akyon2022sahi,
|
||
title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
|
||
author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
|
||
journal={2022 IEEE International Conference on Image Processing (ICIP)},
|
||
doi={10.1109/ICIP46576.2022.9897990},
|
||
pages={966-970},
|
||
year={2022}
|
||
}
|
||
```
|
||
|
||
## Extracts a subset of COCO
|
||
|
||
The training dataset of the COCO2017 dataset includes 118K images, and the validation set includes 5K images, which is a relatively large dataset. Loading JSON in debugging or quick verification scenarios will consume more resources and bring slower startup speed.
|
||
|
||
The `extract_subcoco.py` script provides the ability to extract a specified number/classes/area-size of images. The user can use the `--num-img`, `--classes`, `--area-size` parameter to get a COCO subset of the specified condition of images.
|
||
|
||
For example, extract images use scripts as follows:
|
||
|
||
```shell
|
||
python tools/misc/extract_subcoco.py \
|
||
${ROOT} \
|
||
${OUT_DIR} \
|
||
--num-img 20 \
|
||
--classes cat dog person \
|
||
--area-size small
|
||
```
|
||
|
||
It gone be extract 20 images, and only includes annotations which belongs to cat(or dog/person) and bbox area size is small, after filter by class and area size, the empty annotation images won't be chosen, guarantee the images be extracted definitely has annotation info.
|
||
|
||
Currently, only support COCO2017. In the future will support user-defined datasets of standard coco JSON format.
|
||
|
||
The root path folder format is as follows:
|
||
|
||
```text
|
||
├── root
|
||
│ ├── annotations
|
||
│ ├── train2017
|
||
│ ├── val2017
|
||
│ ├── test2017
|
||
```
|
||
|
||
1. Extract 10 training images and 10 validation images using only 5K validation sets.
|
||
|
||
```shell
|
||
python tools/misc/extract_subcoco.py ${ROOT} ${OUT_DIR} --num-img 10
|
||
```
|
||
|
||
2. Extract 20 training images using the training set and 20 validation images using the validation set.
|
||
|
||
```shell
|
||
python tools/misc/extract_subcoco.py ${ROOT} ${OUT_DIR} --num-img 20 --use-training-set
|
||
```
|
||
|
||
3. Set the global seed to 1. The default is no setting.
|
||
|
||
```shell
|
||
python tools/misc/extract_subcoco.py ${ROOT} ${OUT_DIR} --num-img 20 --use-training-set --seed 1
|
||
```
|
||
|
||
4. Extract images by specify classes
|
||
|
||
```shell
|
||
python tools/misc/extract_subcoco.py ${ROOT} ${OUT_DIR} --classes cat dog person
|
||
```
|
||
|
||
5. Extract images by specify anchor size
|
||
|
||
```shell
|
||
python tools/misc/extract_subcoco.py ${ROOT} ${OUT_DIR} --area-size small
|
||
```
|