Project: SlowFast (GitHub Link)

SlowFast-master
- demo
  - Kinetics
    - SLOWFAST_8x8_R50.yaml
    - kinetics_400_labels.csv
  - AVA
    - ava.names
    - SLOWFAST_32x2_R101_50_50.yaml
- slowfast
  - datasets
    - build.py
    - DATASET.md
    - decoder.py
    - ssv2.py
    - charades.py
    - transform.py
    - kinetics.py
    - ava_helper.py
    - cv2_transform.py
    - multigrid_helper.py
    - video_container.py
    - __init__.py
    - utils.py
    - loader.py
    - ava_dataset.py
  - utils
    - bn_helper.py
    - env.py
    - misc.py
    - meters.py
    - c2_model_loading.py
    - tensorboard_vis.py
    - ava_evaluation
      - np_mask_ops.py
      - np_box_mask_list_ops.py
      - metrics.py
      - label_map_util.py
      - np_box_list_ops.py
      - np_box_mask_list.py
      - np_box_ops.py
      - ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt
      - standard_fields.py
      - __init__.py
      - README.md
      - per_image_evaluation.py
      - np_box_list.py
      - object_detection_evaluation.py
    - distributed.py
    - metrics.py
    - multiprocessing.py
    - visualization_utils.py
    - checkpoint.py
    - lr_policy.py
    - __init__.py
    - parser.py
    - ava_eval_helper.py
    - logging.py
    - benchmark.py
    - multigrid.py
    - weight_init_helper.py
  - models
    - build.py
    - optimizer.py
    - batchnorm_helper.py
    - stem_helper.py
    - head_helper.py
    - losses.py
    - video_model_builder.py
    - __init__.py
    - resnet_helper.py
    - nonlocal_helper.py
    - custom_video_model_builder.py
  - config
    - custom_config.py
    - __init__.py
    - defaults.py
  - __init__.py
- LICENSE
- CONTRIBUTING.md
- projects
  - multigrid
    - README.md
- setup.py
- GETTING_STARTED.md
- configs
  - Kinetics
    - SLOWFAST_8x8_R50.yaml
    - SLOW_4x16_R50.yaml
    - SLOWFAST_4x16_R50.yaml
    - C2D_NLN_8x8_R50_IN1K.yaml
    - SLOWFAST_NLN_4x16_R50.yaml
    - SLOWFAST_8x8_R50_stepwise_multigrid.yaml
    - SLOW_8x8_R50.yaml
    - I3D_8x8_R50_IN1K.yaml
    - SLOWFAST_8x8_R50_stepwise.yaml
    - I3D_8x8_R50.yaml
    - SLOW_NLN_4x16_R50.yaml
    - c2
      - SLOWFAST_8x8_R50.yaml
      - SLOW_4x16_R50.yaml
      - SLOWFAST_4x16_R50.yaml
      - SLOWFAST_8x8_R101_50_101.yaml
      - SLOW_8x8_R50.yaml
      - I3D_8x8_R50.yaml
      - C2D_NOPOOL_8x8_R50.yaml
      - SLOWFAST_8x8_R101_50_50.yaml
      - I3D_NLN_8x8_R50.yaml
      - SLOWFAST_8x8_R101_101_101.yaml
      - SLOWFAST_16x8_R101_50_50.yaml
      - SLOWFAST_NLN_16x8_R101_50_50.yaml
    - SLOW_NLN_8x8_R50.yaml
    - I3D_NLN_8x8_R50_IN1K.yaml
    - C2D_NLN_8x8_R50.yaml
    - I3D_NLN_8x8_R50.yaml
    - SLOWFAST_NLN_8x8_R50.yaml
    - I3D_8x8_R101.yaml
    - C2D_8x8_R50.yaml
    - I3D_NLN_8x8_R101.yaml
    - C2D_8x8_R50_IN1K.yaml
  - AVA
    - SLOWFAST_32x2_R50_SHORT.yaml
    - c2
      - SLOW_8x8_R50.yaml
      - SLOWFAST_64x2_R101_50_50.yaml
      - SLOWFAST_32x2_R50.yaml
      - SLOWFAST_32x2_R101_50_50_v2.1.yaml
      - SLOWFAST_32x2_R101_50_50.yaml
    - SLOW_8x8_R50_SHORT.yaml
  - Charades
    - SLOWFAST_16x8_R50_multigrid.yaml
    - SLOWFAST_16x8_R50.yaml
  - SSv2
    - SLOWFAST_16x8_R50_multigrid.yaml
    - SLOWFAST_16x8_R50.yaml
- setup.cfg
- README.md
- MODEL_ZOO.md
- CODE_OF_CONDUCT.md
- tools
  - demo_net.py
  - test_net.py
  - train_net.py
  - run_net.py
  - benchmark.py
- linter.sh
- INSTALL.md

PySlowFast

PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficient training. This repository includes implementations of the following methods:

Introduction

The goal of PySlowFast is to provide a high-performance, light-weight pytorch codebase provides state-of-the-art video backbones for video understanding research on different tasks (classification, detection, and etc). It is designed in order to support rapid implementation and evaluation of novel video research ideas. PySlowFast includes implementations of the following backbone network architectures:

SlowFast
Slow
C2D
I3D
Non-local Network

Updates

We now support Multigrid Training for efficiently training video models. See projects/multigrid for more information.
PySlowFast is released in conjunction with our ICCV 2019 Tutorial.

License

PySlowFast is released under the Apache 2.0 license.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the PySlowFast Model Zoo.

Installation

Please find installation instructions for PyTorch and PySlowFast in INSTALL.md. You may follow the instructions in DATASET.md to prepare the datasets.

Quick Start

Follow the example in GETTING_STARTED.md to start playing video models with PySlowFast.

Contributors

PySlowFast is written and maintained by Haoqi Fan, Yanghao Li, Bo Xiong, Wan-Yen Lo, Christoph Feichtenhofer.