/SPJ

CS231N/SC230 Final Course Project

Primary LanguageJupyter Notebook

SPJ (Songze, Parker, Julio)

CS231N/CS230 Final Course Project

Google Cloud Instance/Image

Follow this exactly! With one exeception, storage size. Keep in mind the 'Data/' folder alone (without videos, just preprocessed features) requires about 90GB of data. http://cs231n.github.io/gce-tutorial/

Download Instructions

Make Data directory

mkdir Data
cd Data

Download IDs and Labels for All Splits

curl -O https://cs.stanford.edu/people/ranjaykrishna/densevid/captions.zip
unzip captions.zip

Download CD3 Features

curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/activitynet_v1-3.part-00
curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/activitynet_v1-3.part-01
curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/activitynet_v1-3.part-02
curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/activitynet_v1-3.part-03
curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/activitynet_v1-3.part-04
curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/activitynet_v1-3.part-05
curl -O http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/data/challenge16/features/c3d/PCA_activitynet_v1-3.hdf5

Group unzipped splitted files

cat activitynet_v1-3.part-* > temp.zip 

Unzip files

unzip temp.zip

Submission Format

{
version: "VERSION 1.0",
results: {
  v_5n7NCViB5TU: [
      {
      sentence: "One player moves all around the net holding the ball", # String description of an event. 
      timestamp: [1.23,4.53] # The start and end times of the event (in seconds).
      },
      {
      sentence: "A small group of men are seen running around a basketball court playing a game".
      timestamp: [5.24, 18.23]
      }
  ]
}
external_data: {
  used: true, # Boolean flag. True indicates the use of external data.
  details: "First fully-connected layer from VGG-16 pre-trained on ILSVRC-2012 training set", # This string details what kind of external data you used and how you used it.
}
}

Detach Using Screen

Start Screen Session

screen -S jupyter

start jupyter notebook

jupyter notebook

detach Press CTRL-A, D

re-attach to this screen session

screen -r jupyter

Adding to PYTHONPATH

export PYTHONPATH=$(pwd)

Deep Action Proposals (DAPs Repo)

https://github.com/escorciav/daps

NetVLAD (LOUPE Repo)

https://github.com/antoine77340/LOUPE

Evaluation Code

https://github.com/ranjaykrishna/densevid_eval

Quick References

Paper and Benchmarks https://arxiv.org/abs/1705.00754

Dataset http://cs.stanford.edu/people/ranjaykrishna/densevid/

ActivityNet Home Page http://activity-net.org/

Dense-Captioning Task Page http://activity-net.org/challenges/2018/tasks/anet_captioning.html