3D ResNets for Action Recognition
This is the PyTorch code for the following papers:
This code includes only training and testing on the ActivityNet and Kinetics datasets.
If you want to classify your videos using our pretrained models,
use this code.
The PyTorch (python) version of this code is available here.
The PyTorch version includes additional models, such as pre-activation ResNet, Wide ResNet, ResNeXt, and DenseNet.
Citation
If you use this code or pre-trained models, please cite the following:
@article{hara3dcnns,
author={Kensho Hara and Hirokatsu Kataoka and Yutaka Satoh},
title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?},
journal={arXiv preprint},
volume={arXiv:1711.09577},
year={2017},
}
Pre-trained models
Pre-trained models are available at releases.
Requirements
git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; bash install-deps;
./install.sh
luarocks install json
- FFmpeg, FFprobe
wget http://johnvansickle.com/ffmpeg/releases/ffmpeg-release-64bit-static.tar.xz
tar xvf ffmpeg-release-64bit-static.tar.xz
cd ./ffmpeg-3.3.3-64bit-static/; sudo cp ffmpeg ffprobe /usr/local/bin;
- Python 3
Preparation
ActivityNet
- Download datasets using official crawler codes
- Convert from avi to jpg files using
utils/video_jpg.py
python utils/video_jpg.py avi_video_directory jpg_video_directory
- Generate fps files using
utils/fps.py
python utils/fps.py avi_video_directory jpg_video_directory
Kinetics
- Download datasets using official crawler codes
- Locate test set in
video_directory/test.
- Locate test set in
- Convert from avi to jpg files using
utils/video_jpg_kinetics.py
python utils/video_jpg_kinetics.py avi_video_directory jpg_video_directory
- Generate n_frames files using
utils/n_frames_kinetics.py
python utils/n_frames_kinetics.py jpg_video_directory
- Generate annotation file in json format similar to ActivityNet using
utils/kinetics_json.py
python utils/kinetics_json.py train_csv_path val_csv_path test_csv_path json_path
Running the code
Assume the structure of data directories is the following:
~/
data/
activitynet_videos/
jpg/
.../ (directories of video names)
... (jpg files)
kinetics_videos/
jpg/
.../ (directories of class names)
.../ (directories of video names)
... (jpg files)
models/
resnet.t7
results/
model_100.t7
LR/
ActivityNet/
lr.lua
Kinetics/
lr.lua
kinetics.json
activitynet.json
Confirm all options.
th main.lua -h
Train ResNets-34 on the Kinetics dataset (400 classes) with 4 CPU threads (for data loading) and 2 GPUs.
Batch size is 128.
Save models at every 5 epochs.
th main.lua --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --lr_path LR/Kinetics/lr.lua --dataset kinetics --model resnet \
--resnet_depth 34 --n_classes 400 --batch_size 128 --n_gpu 2 --n_threads 4 --checkpoint 5
Continue Training from epoch 101. (~/data/results/model_100.t7 is loaded.)
th main.lua --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --lr_path LR/Kinetics/lr.lua --dataset kinetics --begin_epoch 101 \
--batch_size 128 --n_gpu 2 --n_threads 4 --checkpoint 5
Perform recognition for each video of validation set using pretrained model. This operation outputs top-10 labels for each video.
th main.lua --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --premodel_path models/resnet.t7 --dataset kinetics \
--no_train --no_val --test_video --test_subset val --n_gpu 2 --n_threads 4