Contains code and pretrained models to get you started with a baseline on version 2 of "something-something" dataset
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

something-something-v2-baseline

Contains code and pretrained models to get you started with a baseline on version 2 of "something-something" dataset

Performance of pre-trained model on validation set:

Model top-1 top-5
model3D_1 49.88% 78.82%
model3D_1_224 47.67% 77.35%
model3D_1 with left-right augmentation and fps jitter 51.33% 80.46%

Prerequisites

  • Conda - manages Python environment and dependencies
  • Run conda update conda to ensure the package is up-to-date

Setting up

Installation

  • Clone this repo and https://github.com/jacobgil/pytorch-grad-cam (for obtaining saliency maps see section for more details

  • Move into this repo's root directory

  • Setup python environment - conda env update

    • This will setup an environment named - smth
  • Activate python environment - source activate smth

Download the dataset

The dataset is provided in the form of videos in webm format using VP9 encoding, occupying a total size of 19.4 GB. The videos are in landscape format with height (the shorter side) of 240px at 12 frames/sec.

  • Follow instructions on the data page
  • Download the json files to fetch annotations of the data

Modify config file to include the above paths

In any configuration file (e.g. configs/config_example.json), modify the

  • path to data: data_folder
  • path to JSONs: json_data_train, json_data_val, json_data_test

How to train from scratch?

Run: CUDA_VISIBLE_DEVICES=0,1 python train.py -c configs/config_example.json -g 0,1 --use_cuda

where,

  • CUDA_VISIBLE_DEVICES: environment variable to specify GPU ids to use. (Note: uses all gpus if not specified)

Hyperparameters

Please refer to config file at: configs/config_example.json

  • batch_size: 30 - change this to fill your GPU memory (Note: should be a multiple of number of gpus used)
  • num_workers: 5: number of parallel processes to fetch and pre-process data (increase to max possible CPU cores you have to get better GPU utilisation)
  • lr: 0.008 - increase it if you happen to increase the batch size
  • clip_size: 72 - number of frames in a video sample as input to the model (which at default 12 fps covers 6 secs)
  • step_size_train: 1 - factor by which FPS is reduced (so a step size of 2 would mean an fps of 6)
  • input_spatial_size: 84 - dimension of each frame in input is scaled and cropped to 84x84, but you can use the ubiquitous frame size of 224x224, since the data is provided with height of 240px in landscape format
  • column_units: 512: desired number of units in feature space for each sample

How to use a pre-trained model?

  • Pre-trained models are available in directory: trained_models/pretrained/ With their respective config files here: configs/pretrained/

  • We provide a vanilla implementation of consisting of 11 layers of 3D convolutions. Please refer here: model3D_1.py

  • Use the notebook to get predictions from these models

Test model and get submission file on test data

Modify path to model file in checkpoint variable of config file

CUDA_VISIBLE_DEVICES=0,1 python train.py -c configs/pretrained/config_model1.json -g 0,1 -r -e --use_cuda

The options used here are:

  • -r: to resume an already trained model
  • -e: to evaluate the model on test data

Grad-CAM

Use the notebook to visualize saliency maps of any example from validation set

e.g.

alt text

Id of the video sample = 56620
True label --> 12 (Dropping something onto something)

Top-5 Predictions:
Top 1 :== Dropping something next to something. Prob := 41.51%
Top 2 :== Throwing something. Prob := 13.26%
Top 3 :== Throwing something onto a surface. Prob := 8.92%
Top 4 :== Something falling like a rock. Prob := 8.68%
Top 5 :== Dropping something onto something. Prob := 4.55%

Predicted index chosen = 11 (Dropping something next to something)

Commonsense score

Use the notebook to fetch commonsense score using contrastive groups list in directory assets/

For more details, please refer: https://openreview.net/pdf?id=rkX9Z_kwf

LICENSE

See the file LICENSE for details. Some code snippets have been taken from Keras (see LICENSE_keras) and the PyTorch (see LICENSE_pytorch). See comments in the source code for details.

Reference

If you use our code, dataset or pre-trained models, please cite our paper:

@inproceedings{goyal2017something,
  title={The” something something” video database for learning and evaluating visual common sense},
  author={Goyal, Raghav and Kahou, Samira Ebrahimi and Michalski, Vincent and Materzynska, Joanna and Westphal, Susanne and Kim, Heuna and Haenel, Valentin and Fruend, Ingo and Yianilos, Peter and Mueller-Freitag, Moritz and others}
}