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简体中文 | English

ActivityNet data preparation

Introduction

ActivityNet is a dataset for large-scale video understanding tasks, which can be used for tasks such as action localization, action recognition, etc.

Download

  1. The BMN model uses the processed ActivityNet 1.3 dataset. There are two ways to use it:

    • Using our processed ActivityNet 1.3 dataset (compressed package is about 5.5G), each video has corresponding action labels, duration intervals, duration frames, duration seconds and other information Download with the following command:

      wget https://paddlemodels.bj.bcebos.com/video_detection/bmn_feat.tar.gz # Download the processed video feature data
wget https://paddlemodels.bj.bcebos.com/video_detection/activitynet_1.3_annotations.json # Download the processed label data

      Or click the following hyperlinks to download:

      Video feature data Video feature data

      then decompression bmn_feat.tar.gz

      tar -xf bmn_feat.tar.gz

    • Extract features by yourself

      First refer to Download Instructions to download the original dataset. When training this model, you need to use TSN to extract features from the source files first. You can self-extract video frame and optical flow information, and the pre-trained TSN model can be downloaded from [here](https://github.com/ yjxiong/anet2016-cuhk) download.

    The information in the activitynet_1.3_annotations.json tag file is as follows:

    {
    "v_QOlSCBRmfWY": {
        "duration_second": 82.73,
        "subset": "training",
        "duration_frame": 2067,
        "annotations": [{
            "segment": [6.195294851794072, 77.73085420904837],
            "label": "Ballet"
        }],
        "feature_frame": 2064
    },
    "v_ehGHCYKzyZ8": {
        "duration_second": 61.7189999999999994,
        "subset": "training",
        "duration_frame": 1822,
        "annotations": [{
            "segment": [43.95990729267573, 45.401932082395355],
            "label": "Doing crunches"
        }],
        "feature_frame": 1808
    },
    ...,
    ...
}

    In the end, 19228 video feature npy files are obtained, corresponding to the 19228 label information in the activitynet_1.3_annotations.json file.

  2. Create a new data/bmn_data folder, and then unzip the video feature data after downloading and put it in this folder, and finally it should be organized into the following form:

    PaddleVideo
├── data
│   ├── bmn_data
│   │   ├── fix_feat_100
│   │   │   ├── v___c8enCfzqw.npy
│   │   │   ├── v___dXUJsj3yo.npy
│   │   │   ├── ...
│   │   │
│   │   └── activitynet_1.3_annotations.json

  3. Finally, modify the feat_path field in the configuration file configs/localization/bmn.yaml to specify the feature directory path, and the file_path field to specify the label file path.