preparing_activitynet.md

Preparing ActivityNet

For basic dataset information, please refer to the official website. Here, we use the ActivityNet rescaled feature provided in this repo. Before we start, please make sure that current working directory is $MMACTION/tools/data/activitynet/.

Step 1. Download Annotations

First of all, you can run the following script to download annotation files.

bash download_annotations.sh

Step 2. Prepare Videos Features

Then, you can run the following script to download activitynet features.

bash download_features.sh

Step 3. Process Annotation Files

Next, you can run the following script to process the downloaded annotation files for training and testing. It first merges the two annotation files together and then seperates the annoations by train, val and test.

python process_annotations.py

Step 4. Check Directory Structure

After the whole data pipeline for ActivityNet preparation, you will get the features and annotation files.

In the context of the whole project (for ActivityNet only), the folder structure will look like:

mmaction
├── mmaction
├── tools
├── configs
├── data
│   ├── ActivityNet
│   │   ├── anet_anno_{train,val,test,full}.json
│   │   ├── anet_anno_action.json
│   │   ├── video_info_new.csv
│   │   ├── activitynet_feature_cuhk
│   │   │   ├── csv_mean_100
│   │   │   │   ├── v___c8enCfzqw.csv
│   │   │   │   ├── v___dXUJsj3yo.csv
│   │   │   |   ├── ..

For training and evaluating on ActivityNet, please refer to getting_started.md.