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vcoco's Introduction

hico-det

主要特性

转换为 COCO 格式的 VCOCO 数据集标注,可用pycocotools.coco.COCO加载。

本仓库仅包含标注,图片下载参考文末引用

ann_id遵循原本标注,但在label_info.json中为其配置了valid_id,按照以下规则重新排列:

  1. obj_name
  2. verb_name

仅为了优化打印成表格后的视觉效果。

数据特性

  • hoi: 260 (triplett)
  • rel: 24 (排除 no-role-action 和 point-inter)
  • obj: 80 (coco-ins)

标注格式

image

{
    'info': [...], 
    'licenses': [...], 
    'images': [
        {
            'file_name': 'HICO_test2015_00000001.jpg',
            'height'   : 427,
            'width'    : 640,
            'id'       : 1
        },
        ...
    ]
}

instance

{
    'info': [...], 
    'licenses': [...], 
    'images': [...],
    'annotations': [
        {
            'id'         : 1,
            'bbox'       : [320, 306, 39, 43],
            'area'       : 1677,
            'category_id': 1,
            'image_id'   : 1
        },
        ...
    ], 
    'categories': [
        {'supercategory': 'person', 'id': 1, 'name': 'person'},
        ...
    ]
}

relation

{
    'info': [...], 
    'licenses': [...], 
    'images': [...],
    'annotations': [
        {'id': 1, 'subject_id': 1, 'object_id': 3, 'category_id': 88, 'image_id': 1},
        ...
    ], 
    'categories': [
        {'id': 1, 'name': 'adjust'},
        ...
    ]
}

文件夹作用

annotations

存放标注文件(coco格式)。

其中根目录的标注转换自v-coco提供的标注文件。 而qpic子目录存放转换自QPIC提供的标注。

tools

base

建立标注和配置的过程,以及简易可视化(jupyter)。

proposal

instance/person_keypoints预测的转化。

text_embed

提取文本嵌入的脚本。

引用

论文: Visual Semantic Role Labeling

code: https://github.com/s-gupta/v-coco

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