Add files using upload-large-folder tool

readme.txt

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+# EPIC KITCHENS Dataset - Video Release
+Release Date: April 2018
+
+## Authors
+Dima Damen (1)
+Hazel Doughty (1)
+Sanja Fidler (2)
+Antonino Furnari (3)
+Evangelos Kazakos (1)
+Giovanni Maria Farinella (3)
+Davide Moltisanti (1)
+Jonathan Munro (1)
+Toby Perrett (1)
+Will Price (1)
+Michael Wray (1)
+
+* (1 University of Bristol)
+* (2 University of Toronto)
+* (3 University of Catania)
+
+
+## Citing
+When using the dataset, kindly reference:
+
+Dima Damen, Hazel Doughty, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Giovanni Maria Farinella, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray (2018). Scaling Egocentric Vision: The EPIC-KITCHENS Dataset. check publication [here](http://epic-kitchens.github.io)
+
+## Dataset Details
+This readme contains information about video files.  Please see the 
+[github](https://github.com/epic-kitchens/annotations) for the latest annotations 
+and challenges for the dataset.
+
+## Folder Details
+We have the following top level folders:
+
+* **videos:** contains the raw uncut videos.
+* **object_detection_images:** contains the frames used as input for object
+  detection.
+* **rgb_flow:** contains the RGB and flow frames used as input for action
+  recognition.
+
+We use `P##/P##_**` with `##` denoting the participant number and `**` to denote
+the video number.
+
+### Video Folder Structure
+Videos is further broken down into two subdirectories, train and test containing
+the files in the train and test sets respectively.
+
+Each video, labelled as `P##_**.mp4` can be found inside the participant
+directory, i.e. `P01_01.mp4`  can be found at `Videos/train/P01/P01_01.mp4`.
+
+
+### Object Images Folder Structure
+Object Detection Images is further broken down into two subdirectories, train
+and test containing the files in the train and test sets respectively.
+
+Image frames, labelled as `img_xxxxxxxxxx.jpg` can be found inside the video
+directory, i.e. The first frame from `P01_01.mp4` can be found at
+`ObjectDetectionImages/train/P01/P01_01/img_0000000001.jpg`.
+
+
+### RGB Flow Folder Structure
+RGB Flow is further split into two subdirectories, `rgb` and `flow` containing
+files for the RGB frames and flow Frames.  Both folders contain the same
+directory structure and are further broken down into train and test directories
+containing the train and test sets respectively.
+
+Frames from a video have been grouped into a tar file, labelled as `P##_**.tar`
+and can be found inside the participant directory, i.e. The RGB frames from,
+`P01_01` can be found in `rgb_flow/train/P01_01.tar`. Similarly the flow frames
+can be found in `flow/train/P01_01.tar`. Each tar file contains a flat directory
+of frames named `frame_xxxxxxxxxx.jpg`.
+
+
+## Video Information
+
+Videos are recorded in 1080p at 59.94 FPS on a GoPro Hero 5 with linear field of
+view. There are few videos which were shot at different resolutions,
+field of views, or FPS due to participant error or camera. These videos
+identified using `ffprobe` are:
+
+* 1280x720: `P12_01`, `P12_02`, `P12_03`, `P12_04`.
+* 2560x1440: `P12_05`, `P12_06` 
+* 29.97 FPS: `P09_07`, `P09_08`, `P10_01`, `P10_04`, `P11_01`, `P18_02`,
+    `P18_03`
+* 48 FPS: `P17_01`, `P17_02`, `P17_03`, `P17_04`
+* 90 FPS: `P18_09`
+
+The GoPro Hero 5 was also set to drop the framerate in low light conditions to
+preserve exposure leading to variable FPS in some videos.  If you wish to
+extract frames we suggest you resample at 60 FPS to mitigate issues with
+variable FPS, this can be achieved in a single step with FFmpeg: 
+
+```
+ffmpeg -i 'P##_**.MP4' -vf 'scale=-2:256' -q:v 4 -r 60 'P##_**/frame_%010d.jpg'
+```
+
+Optical flow was extracted using a fork of
+[`gpu_flow`](https://github.com/feichtenhofer/gpu_flow) made 
+[available on github](https://github.com/dl-container-registry/furnari-flow).
+ We set the parameters: stride = 2, dilation = 3, bound = 25 and size = 256.
+
+
+## License
+All files in this dataset are copyright by us and published under the 
+Creative Commons Attribution-NonCommerial 4.0 International License, found 
+[here](https://creativecommons.org/licenses/by-nc/4.0/).
+This means that you must give appropriate credit, provide a link to the license,
+and indicate if changes were made. You may do so in any reasonable manner,
+but not in any way that suggests the licensor endorses you or your use. You
+may not use the material for commercial purposes.