We published a paper on arXiv.
We uploaded the pretrained models described in this paper including ResNet-50 pretrained on the combined dataset with Kinetics-700 and Moments in Time.
We significantly updated our scripts. If you want to use older versions to reproduce our CVPR2018 paper, you should use the scripts in the CVPR2018 branch.
This update includes as follows:
- Refactoring whole project
- Supporting the newer PyTorch versions
- Supporting distributed training
- Supporting training and testing on the Moments in Time dataset.
- Adding R(2+1)D models
- Uploading 3D ResNet models trained on the Kinetics-700, Moments in Time, and STAIR-Actions datasets
This is the PyTorch code for the following papers:
This code includes training, fine-tuning and testing on Kinetics, Moments in Time, ActivityNet, UCF-101, and HMDB-51.
If you use this code or pre-trained models, please cite the following:
@inproceedings{hara3dcnns,
author={Kensho Hara and Hirokatsu Kataoka and Yutaka Satoh},
title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={6546--6555},
year={2018},
}Pre-trained models are available here.
All models are trained on Kinetics-700 (K), Moments in Time (M), STAIR-Actions (S), or merged datasets of them (KM, KS, MS, KMS).
If you want to finetune the models on your dataset, you should specify the following options.
r3d18_K_200ep.pth: --model resnet --model_depth 18 --n_pretrain_classes 700
r3d18_KM_200ep.pth: --model resnet --model_depth 18 --n_pretrain_classes 1039
r3d34_K_200ep.pth: --model resnet --model_depth 34 --n_pretrain_classes 700
r3d34_KM_200ep.pth: --model resnet --model_depth 34 --n_pretrain_classes 1039
r3d50_K_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 700
r3d50_KM_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 1039
r3d50_KMS_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 1139
r3d50_KS_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 800
r3d50_M_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 339
r3d50_MS_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 439
r3d50_S_200ep.pth: --model resnet --model_depth 50 --n_pretrain_classes 100
r3d101_K_200ep.pth: --model resnet --model_depth 101 --n_pretrain_classes 700
r3d101_KM_200ep.pth: --model resnet --model_depth 101 --n_pretrain_classes 1039
r3d152_K_200ep.pth: --model resnet --model_depth 152 --n_pretrain_classes 700
r3d152_KM_200ep.pth: --model resnet --model_depth 152 --n_pretrain_classes 1039
r3d200_K_200ep.pth: --model resnet --model_depth 200 --n_pretrain_classes 700
r3d200_KM_200ep.pth: --model resnet --model_depth 200 --n_pretrain_classes 1039
Old pretrained models are still available here.
However, some modifications are required to use the old pretrained models in the current scripts.
- PyTorch (ver. 0.4+ required)
conda install pytorch torchvision cudatoolkit=10.1 -c soumith-
FFmpeg, FFprobe
-
Python 3
- Download videos using the official crawler.
- Convert from avi to jpg files using
util_scripts/generate_video_jpgs.py
python -m util_scripts.generate_video_jpgs mp4_video_dir_path jpg_video_dir_path activitynet- Add fps infomartion into the json file
util_scripts/add_fps_into_activitynet_json.py
python -m util_scripts.add_fps_into_activitynet_json mp4_video_dir_path json_file_path- Download videos using the official crawler.
- Locate test set in
video_directory/test.
- Locate test set in
- Convert from avi to jpg files using
util_scripts/generate_video_jpgs.py
python -m util_scripts.generate_video_jpgs mp4_video_dir_path jpg_video_dir_path kinetics- Generate annotation file in json format similar to ActivityNet using
util_scripts/kinetics_json.py- The CSV files (kinetics_{train, val, test}.csv) are included in the crawler.
python -m util_scripts.kinetics_json csv_dir_path 700 jpg_video_dir_path jpg dst_json_path- Download videos and train/test splits here.
- Convert from avi to jpg files using
util_scripts/generate_video_jpgs.py
python -m util_scripts.generate_video_jpgs avi_video_dir_path jpg_video_dir_path ucf101- Generate annotation file in json format similar to ActivityNet using
util_scripts/ucf101_json.pyannotation_dir_pathincludes classInd.txt, trainlist0{1, 2, 3}.txt, testlist0{1, 2, 3}.txt
python -m util_scripts.ucf101_json annotation_dir_path jpg_video_dir_path dst_json_path- Download videos and train/test splits here.
- Convert from avi to jpg files using
util_scripts/generate_video_jpgs.py
python -m util_scripts.generate_video_jpgs avi_video_dir_path jpg_video_dir_path hmdb51- Generate annotation file in json format similar to ActivityNet using
util_scripts/hmdb51_json.pyannotation_dir_pathincludes brush_hair_test_split1.txt, ...
python -m util_scripts.hmdb51_json annotation_dir_path jpg_video_dir_path dst_json_pathAssume the structure of data directories is the following:
~/
data/
kinetics_videos/
jpg/
.../ (directories of class names)
.../ (directories of video names)
... (jpg files)
results/
save_100.pth
kinetics.json
Confirm all options.
python main.py -hTrain ResNets-50 on the Kinetics-700 dataset (700 classes) with 4 CPU threads (for data loading).
Batch size is 128.
Save models at every 5 epochs.
All GPUs is used for the training.
If you want a part of GPUs, use CUDA_VISIBLE_DEVICES=....
python main.py --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --dataset kinetics --model resnet \
--model_depth 50 --n_classes 700 --batch_size 128 --n_threads 4 --checkpoint 5Continue Training from epoch 101. (~/data/results/save_100.pth is loaded.)
python main.py --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --dataset kinetics --resume_path results/save_100.pth \
--model_depth 50 --n_classes 700 --batch_size 128 --n_threads 4 --checkpoint 5Calculate top-5 class probabilities of each video using a trained model (~/data/results/save_200.pth.)
Note that inference_batch_size should be small because actual batch size is calculated by inference_batch_size * (n_video_frames / inference_stride).
python main.py --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --dataset kinetics --resume_path results/save_200.pth \
--model_depth 50 --n_classes 700 --n_threads 4 --no_train --no_val --inference --output_topk 5 --inference_batch_size 1Evaluate top-1 video accuracy of a recognition result (~/data/results/val.json).
python -m util_scripts.eval_accuracy ~/data/kinetics.json ~/data/results/val.json --subset val -k 1 --ignoreFine-tune fc layers of a pretrained model (~/data/models/resnet-50-kinetics.pth) on UCF-101.
python main.py --root_path ~/data --video_path ucf101_videos/jpg --annotation_path ucf101_01.json \
--result_path results --dataset ucf101 --n_classes 101 --n_pretrain_classes 700 \
--pretrain_path models/resnet-50-kinetics.pth --ft_begin_module fc \
--model resnet --model_depth 50 --batch_size 128 --n_threads 4 --checkpoint 5
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