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ECO-pytorch

  • This unofficial repo implements the ECO network structure with PyTorch, official repo is here.
  • Pre-trained model for 2D-Net is provided by tsn-pytorch, and 3D-Net use the Kinetics-pretrained model of 3D-Resnet18 provided by 3D-ResNets-PyTorch.
  • Codes modified from tsn-pytorch.

PAPER INFO

"ECO: Efficient Convolutional Network for Online Video Understanding"
By Mohammadreza Zolfaghari, Kamaljeet Singh, Thomas Brox
paper link

NOTE

  • I have only tested the ECO-Lite-4F architecture, which achieves ~85% in accuracy(87.4% in paper).
  • Recently I'm busy with my exam, the repo will be updated after 15th, July.
  • Sorry for that! Please keep watching, any contribution is welcomed!

Environment:

  • Python 3.6.4
  • PyTorch 0.3.1

Clone this repo

git clone https://github.com/zhang-can/ECO-pytorch

Generate dataset lists

python gen_dataset_lists.py <ucf101/something> <dataset_frames_root_path>

e.g. python gen_dataset_lists.py something ~/dataset/20bn-something-something-v1/

The dataset should be organized as:
<dataset_frames_root_path>/<video_name>/<frame_images>

Training

[UCF101 - ECO - RGB] command:

python main.py ucf101 RGB <ucf101_rgb_train_list> <ucf101_rgb_val_list> \
        --arch ECO --num_segments 4 --gd 5 --lr 0.001 --lr_steps 30 60 --epochs 80 \
        -b 32 -i 4 -j 2 --dropout 0.5 --snapshot_pref ucf101_ECO --rgb_prefix img_ \
        --consensus_type identity --eval-freq 1