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href: "\/LabelConvert\/docs\/supportconversions\/coco_to_labelimg\/",
title: "COCO → labelImg yolo",
description: "One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelimg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.",
content: " One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelimg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.jpg │ ├── 000000000001.txt │ |-- 000000000002.jpg │ └── classes.txt └── val ├── 000000000001.jpg ├── 000000000001.txt └── classes.txt For the converted directory, you can directly use the labelImg library to open it directly and change the label. The specific commands are as follows: $ cd dataset/COCO_labelImg_format $ labelImg train train/classes.txt # or $ labelImg val val/classes.txt "
description: "One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelImg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.",
content: " One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelImg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.jpg │ ├── 000000000001.txt │ |-- 000000000002.jpg │ └── classes.txt └── val ├── 000000000001.jpg ├── 000000000001.txt └── classes.txt For the converted directory, you can directly use the labelImg library to open it directly and change the label. The specific commands are as follows: $ cd dataset/COCO_labelImg_format $ labelImg train train/classes.txt # or $ labelImg val val/classes.txt "
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@@ -756,8 +765,8 @@ <h1 class="content-title mb-0">
id: 3 ,
href: "\/LabelConvert\/docs\/supportconversions\/coco_to_labelimg\/",
title: "COCO → labelImg yolo",
description: "One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelimg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.",
content: " One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelimg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.jpg │ ├── 000000000001.txt │ |-- 000000000002.jpg │ └── classes.txt └── val ├── 000000000001.jpg ├── 000000000001.txt └── classes.txt For the converted directory, you can directly use the labelImg library to open it directly and change the label. The specific commands are as follows: $ cd dataset/COCO_labelImg_format $ labelImg train train/classes.txt # or $ labelImg val val/classes.txt "
description: "One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelImg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.",
content: " One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelImg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.jpg │ ├── 000000000001.txt │ |-- 000000000002.jpg │ └── classes.txt └── val ├── 000000000001.jpg ├── 000000000001.txt └── classes.txt For the converted directory, you can directly use the labelImg library to open it directly and change the label. The specific commands are as follows: $ cd dataset/COCO_labelImg_format $ labelImg train train/classes.txt # or $ labelImg val val/classes.txt "
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@@ -894,5 +903,6 @@ <h1 class="content-title mb-0">
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2 changes: 1 addition & 1 deletion docs/supportconversions/index.xml
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@@ -37,7 +37,7 @@ labelImg_to_publaynet --data_dir dataset/labelImg_dataset \ --val_ratio 0.2 \ --
<pubDate>Fri, 30 Sep 2022 05:33:22 +0100</pubDate>

<guid>https://rapidai.github.io/LabelConvert/docs/supportconversions/coco_to_labelimg/</guid>
<description>One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelimg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.</description>
<description>One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelImg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.</description>
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<item>
116 changes: 67 additions & 49 deletions docs/supportconversions/labelimg_to_publaynet/index.html

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116 changes: 67 additions & 49 deletions docs/supportconversions/labelimg_to_yolov5/index.html

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116 changes: 67 additions & 49 deletions docs/supportconversions/yolov5_to_coco/index.html

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2 changes: 1 addition & 1 deletion index.xml
Original file line number Diff line number Diff line change
@@ -37,7 +37,7 @@ labelImg_to_publaynet --data_dir dataset/labelImg_dataset \ --val_ratio 0.2 \ --
<pubDate>Fri, 30 Sep 2022 05:33:22 +0100</pubDate>

<guid>https://rapidai.github.io/LabelConvert/docs/supportconversions/coco_to_labelimg/</guid>
<description>One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelimg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.</description>
<description>One-click conversion of COCO format data to labelImg labeled yolo format data. COCO format directory structure(see dataset/YOLOV5_COCO_format for details): YOLOV5_COCO_format ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── val2017 └── 000000000001.jpg Convert coco_to_labelImg --data_dir dataset/YOLOV5_COCO_format --data_dir: the directory where the COCO format dataset is located. Default is dataset/YOLOV5_COCO_format. Converted directory structure (see dataset/COCO_labelImg_format for details): COCO_labelImg_format ├── train │ ├── 000000000001.</description>
</item>

<item>
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26 changes: 13 additions & 13 deletions scss/style.css.map
4 changes: 2 additions & 2 deletions sitemap.xml
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@@ -12,7 +12,7 @@
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<lastmod>2023-09-20T08:23:06+08:00</lastmod>
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<loc>https://rapidai.github.io/LabelConvert/categories/</loc>
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