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+# Preparing JHMDB
+
+## Introduction
+
+<!-- [DATASET] -->
+
+```BibTeX
+@inproceedings{Jhuang:ICCV:2013,
+    title = {Towards understanding action recognition},
+    author = {H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black},
+    booktitle = {International Conf. on Computer Vision (ICCV)},
+    month = Dec,
+    pages = {3192-3199},
+    year = {2013}
+}
+```
+
+For basic dataset information, you can refer to the dataset [website](http://jhmdb.is.tue.mpg.de/).
+Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/jhmdb/`.
+
+## Download and Extract
+
+You can download the RGB frames, optical flow and ground truth annotations from [google drive](https://drive.google.com/drive/folders/1BvGywlAGrACEqRyfYbz3wzlVV3cDFkct).
+The data are provided from [MOC](https://github.com/MCG-NJU/MOC-Detector/blob/master/readme/Dataset.md), which is adapted from [act-detector](https://github.com/vkalogeiton/caffe/tree/act-detector).
+
+After downloading the `JHMDB.tar.gz` file and put it in `$MMACTION2/tools/data/jhmdb/`, you can run the following command to extract.
+
+```shell
+tar -zxvf JHMDB.tar.gz
+```
+
+If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance.
+
+You can run the following script to soft link SSD.
+
+```shell
+# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
+mkdir /mnt/SSD/JHMDB/
+ln -s /mnt/SSD/JHMDB/ ../../../data/jhmdb
+```
+
+## Check Directory Structure
+
+After extracting, you will get the `FlowBrox04` directory, `Frames` directory and `JHMDB-GT.pkl` for JHMDB.
+
+In the context of the whole project (for JHMDB only), the folder structure will look like:
+
+```
+mmaction2
+├── mmaction
+├── tools
+├── configs
+├── data
+│   ├── jhmdb
+│   |   ├── FlowBrox04
+│   |   |   ├── brush_hair
+│   |   |   |   ├── April_09_brush_hair_u_nm_np1_ba_goo_0
+│   |   |   |   |   ├── 00001.jpg
+│   |   |   |   |   ├── 00002.jpg
+│   |   |   |   |   ├── ...
+│   |   |   |   |   ├── 00039.jpg
+│   |   |   |   |   ├── 00040.jpg
+│   |   |   |   ├── ...
+│   |   |   |   ├── Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_2
+│   |   |   ├── ...
+│   |   |   ├── wave
+│   |   |   |   ├── 21_wave_u_nm_np1_fr_goo_5
+│   |   |   |   ├── ...
+│   |   |   |   ├── Wie_man_winkt!!_wave_u_cm_np1_fr_med_0
+│   |   ├── Frames
+│   |   |   ├── brush_hair
+│   |   |   |   ├── April_09_brush_hair_u_nm_np1_ba_goo_0
+│   |   |   |   |   ├── 00001.png
+│   |   |   |   |   ├── 00002.png
+│   |   |   |   |   ├── ...
+│   |   |   |   |   ├── 00039.png
+│   |   |   |   |   ├── 00040.png
+│   |   |   |   ├── ...
+│   |   |   |   ├── Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_2
+│   |   |   ├── ...
+│   |   |   ├── wave
+│   |   |   |   ├── 21_wave_u_nm_np1_fr_goo_5
+│   |   |   |   ├── ...
+│   |   |   |   ├── Wie_man_winkt!!_wave_u_cm_np1_fr_med_0
+│   |   ├── JHMDB-GT.pkl
+
+```
+
+:::{note}
+The `JHMDB-GT.pkl` exists as a cache, it contains 6 items as follows:
+
+1. `labels` (list): List of the 21 labels.
+2. `gttubes` (dict): Dictionary that contains the ground truth tubes for each video.
+  A **gttube** is dictionary that associates with each index of label and a list of tubes.
+  A **tube** is a numpy array with `nframes` rows and 5 columns, each col is in format like `<frame index> <x1> <y1> <x2> <y2>`.
+3. `nframes` (dict): Dictionary that contains the number of frames for each video, like `'walk/Panic_in_the_Streets_walk_u_cm_np1_ba_med_5': 16`.
+4. `train_videos` (list): A list with `nsplits=1` elements, each one containing the list of training videos.
+5. `test_videos` (list): A list with `nsplits=1` elements, each one containing the list of testing videos.
+6. `resolution` (dict): Dictionary that outputs a tuple (h,w) of the resolution for each video, like `'pour/Bartender_School_Students_Practice_pour_u_cm_np1_fr_med_1': (240, 320)`.
+
+:::