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+# Preparing AVA
+
+## Introduction
+
+<!-- [DATASET] -->
+
+```BibTeX
+@inproceedings{gu2018ava,
+  title={Ava: A video dataset of spatio-temporally localized atomic visual actions},
+  author={Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={6047--6056},
+  year={2018}
+}
+```
+
+For basic dataset information, please refer to the official [website](https://research.google.com/ava/index.html).
+Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/ava/`.
+
+## Step 1. Prepare Annotations
+
+First of all, you can run the following script to prepare annotations.
+
+```shell
+bash download_annotations.sh
+```
+
+This command will download `ava_v2.1.zip` for AVA `v2.1` annotation. If you need the AVA `v2.2` annotation, you can try the following script.
+
+```shell
+VERSION=2.2 bash download_annotations.sh
+```
+
+## Step 2. Prepare Videos
+
+Then, use the following script to prepare videos. The codes are adapted from the [official crawler](https://github.com/cvdfoundation/ava-dataset).
+Note that this might take a long time.
+
+```shell
+bash download_videos.sh
+```
+
+Or you can use the following command to downloading AVA videos in parallel using a python script.
+
+```shell
+bash download_videos_parallel.sh
+```
+
+Note that if you happen to have sudoer or have [GNU parallel](https://www.gnu.org/software/parallel/) on your machine,
+you can speed up the procedure by downloading in parallel.
+
+```shell
+# sudo apt-get install parallel
+bash download_videos_gnu_parallel.sh
+```
+
+## Step 3. Cut Videos
+
+Cut each video from its 15th to 30th minute and make them at 30 fps.
+
+```shell
+bash cut_videos.sh
+```
+
+## Step 4. Extract RGB and Flow
+
+Before extracting, please refer to [install.md](/docs/install.md) for installing [denseflow](https://github.com/open-mmlab/denseflow).
+
+If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance. And you can run the following script to soft link the extracted frames.
+
+```shell
+# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
+mkdir /mnt/SSD/ava_extracted/
+ln -s /mnt/SSD/ava_extracted/ ../data/ava/rawframes/
+```
+
+If you only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames using denseflow.
+
+```shell
+bash extract_rgb_frames.sh
+```
+
+If you didn't install denseflow, you can still extract RGB frames using ffmpeg by the following script.
+
+```shell
+bash extract_rgb_frames_ffmpeg.sh
+```
+
+If both are required, run the following script to extract frames.
+
+```shell
+bash extract_frames.sh
+```
+
+## Step 5. Fetch Proposal Files
+
+The scripts are adapted from FAIR's [Long-Term Feature Banks](https://github.com/facebookresearch/video-long-term-feature-banks).
+
+Run the following scripts to fetch the pre-computed proposal list.
+
+```shell
+bash fetch_ava_proposals.sh
+```
+
+## Step 6. Folder Structure
+
+After the whole data pipeline for AVA preparation.
+you can get the rawframes (RGB + Flow), videos and annotation files for AVA.
+
+In the context of the whole project (for AVA only), the *minimal* folder structure will look like:
+(*minimal* means that some data are not necessary: for example, you may want to evaluate AVA using the original video format.)
+
+```
+mmaction2
+├── mmaction
+├── tools
+├── configs
+├── data
+│   ├── ava
+│   │   ├── annotations
+│   │   |   ├── ava_dense_proposals_train.FAIR.recall_93.9.pkl
+│   │   |   ├── ava_dense_proposals_val.FAIR.recall_93.9.pkl
+│   │   |   ├── ava_dense_proposals_test.FAIR.recall_93.9.pkl
+│   │   |   ├── ava_train_v2.1.csv
+│   │   |   ├── ava_val_v2.1.csv
+│   │   |   ├── ava_train_excluded_timestamps_v2.1.csv
+│   │   |   ├── ava_val_excluded_timestamps_v2.1.csv
+│   │   |   ├── ava_action_list_v2.1_for_activitynet_2018.pbtxt
+│   │   ├── videos
+│   │   │   ├── 053oq2xB3oU.mkv
+│   │   │   ├── 0f39OWEqJ24.mp4
+│   │   │   ├── ...
+│   │   ├── videos_15min
+│   │   │   ├── 053oq2xB3oU.mkv
+│   │   │   ├── 0f39OWEqJ24.mp4
+│   │   │   ├── ...
+│   │   ├── rawframes
+│   │   │   ├── 053oq2xB3oU
+|   │   │   │   ├── img_00001.jpg
+|   │   │   │   ├── img_00002.jpg
+|   │   │   │   ├── ...
+```
+
+For training and evaluating on AVA, please refer to [getting_started](/docs/getting_started.md).
+
+## Reference
+
+1. O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014