sergiuoprea / dpc

Video Representation Learning by Dense Predictive Coding. Tengda Han, Weidi Xie, Andrew Zisserman.

Video Representation Learning by Dense Predictive Coding

This repository contains the implementation of Dense Predictive Coding (DPC).

Links: [Arxiv] [Video] [Project page]

DPC Results

Original result from our paper:

Pretrain Dataset	Resolution	Backbone	Finetune Acc@1 (UCF101)	Finetune Acc@1 (HMDB51)
UCF101	128x128	2d3d-R18	60.6	-
Kinetics400	128x128	2d3d-R18	68.2	34.5
Kinetics400	224x224	2d3d-R34	75.7	35.7

Also re-implemented by other researchers:

Pretrain Dataset	Resolution	Backbone	Finetune Acc@1 (UCF101)	Finetune Acc@1 (HMDB51)
UCF101	128x128	2d3d-R18	61.35 @kayush95	45.31 @kayush95

News

• 2020/10/09: Upload 3D-ResNet18-UCF101-128x128 pretrained weights.
• 2020/06/05: Update the link for 3D-ResNet34-Kinetics400-224x224-runningStats, the old one didn't save BN running statistics, thus couldn't be used to evaluate linear probe. Now it saves (without changing any weights).

Installation

The implementation should work with python >= 3.6, pytorch >= 0.4, torchvision >= 0.2.2.

The repo also requires cv2 (conda install -c menpo opencv), tensorboardX >= 1.7 (pip install tensorboardX), joblib, tqdm, ipdb.

Prepare data

Follow the instructions here.

Self-supervised training (DPC)

Change directory cd DPC/dpc/

• example: train DPC-RNN using 2 GPUs, with 3D-ResNet18 backbone, on Kinetics400 dataset with 128x128 resolution, for 300 epochs

  python main.py --gpu 0,1 --net resnet18 --dataset k400 --batch_size 128 --img_dim 128 --epochs 300
  

• example: train DPC-RNN using 4 GPUs, with 3D-ResNet34 backbone, on Kinetics400 dataset with 224x224 resolution, for 150 epochs

  python main.py --gpu 0,1,2,3 --net resnet34 --dataset k400 --batch_size 44 --img_dim 224 --epochs 150
  

Evaluation: supervised action classification

Change directory cd DPC/eval/

• example: finetune pretrained DPC weights (replace {model.pth.tar} with pretrained DPC model)

  python test.py --gpu 0,1 --net resnet18 --dataset ucf101 --batch_size 128 --img_dim 128 --pretrain {model.pth.tar} --train_what ft --epochs 300
  

• example (continued): test the finetuned model (replace {finetune_model.pth.tar} with finetuned classifier model)

  python test.py --gpu 0,1 --net resnet18 --dataset ucf101 --batch_size 128 --img_dim 128 --test {finetune_model.pth.tar}
  

DPC-pretrained weights

It took us more than 1 week to train the 3D-ResNet18 DPC model on Kinetics-400 with 128x128 resolution, and it tooks about 6 weeks to train the 3D-ResNet34 DPC model on Kinetics-400 with 224x224 resolution (with 4 Nvidia P40 GPUs).

Download link:

• Kinetics400 pretrain:

  • 3D-ResNet18-Kinetics400-128x128,
  • 3D-ResNet34-Kinetics400-224x224,
  • 3D-ResNet34-Kinetics400-224x224-runningStats

• UCF101 pretrain:

  • 3D-ResNet18-UCF101-128x128

• example: finetune 3D-ResNet34-Kinetics400-224x224

  python test.py --gpu 0,1 --net resnet34 --dataset ucf101 --batch_size 44 --img_dim 224 --pretrain {model.pth.tar} --train_what ft --epochs 300
  

Citation

If you find the repo useful for your research, please consider citing our paper:

@InProceedings{Han19dpc,
  author       = "Tengda Han and Weidi Xie and Andrew Zisserman",
  title        = "Video Representation Learning by Dense Predictive Coding",
  booktitle    = "Workshop on Large Scale Holistic Video Understanding, ICCV",
  year         = "2019",
}

For any questions, welcome to create an issue or contact Tengda Han ([email protected]).