" ECO: Efficient Convolutional Network for Online Video Understanding, European Conference on Computer Vision (ECCV), 2018." By Mohammadreza Zolfaghari, Kamaljeet Singh, Thomas Brox
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Trained models on Kinetics dataset for ECO Lite and C3D are provided.
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Keep watching for updates
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Pre-trained model for 2D-Net is provided by tsn-pytorch.
- Python 3.5.2
- PyTorch 0.4.1
- TorchVison: 0.2.1
git clone https://github.com/mzolfaghari/ECO-pytorch
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>
- Download the initialization and trained models:
sh models/download_models.sh
- If you can not access Google Drive, please download the pretrained models from BaiduYun, and put them in "models" folder.
- Command for training ECO Lite model:
./scripts/run_ECOLite_kinetics.sh local
- For training C3D network use the following command:
./scripts/run_c3dres_kinetics.sh local
- For finetuning on UCF101 use the following command:
./scripts/run_ECOLite_finetune_UCF101.sh local
- If you want to train your model from scratch change the config as following:
--pretrained_parts scratch
- configurations explained in "opts.py"
- ECO Full
- Trained models on other datasets
If you use this code or ideas from the paper for your research, please cite our paper:
@inproceedings{ECO_eccv18,
author={Mohammadreza Zolfaghari and
Kamaljeet Singh and
Thomas Brox},
title={{ECO:} Efficient Convolutional Network for Online Video Understanding},
booktitle={ECCV},
year={2018}
}
Mohammadreza Zolfaghari, Can Zhang
Questions can also be left as issues in the repository. We will be happy to answer them.