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EfficientSCI
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EfficientSCI
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# EfficientSCI This repo is the implementation of [EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging](https://openaccess.thecvf.com/content/CVPR2023/html/Wang_EfficientSCI_Densely_Connected_Network_With_Space-Time_Factorization_for_Large-Scale_Video_CVPR_2023_paper.html). ## Testing Result on Simulation Dataset <div align="center"> <img src="docs/psnr_time.png" width=60% /> Fig1. Comparison of reconstruction quality and testing time of several SOTA deep learning based algorithms. </div> ## Installation Please see the [Installation Manual](docs/install.md) for EfficientSCI Installation. ## Training Support multi GPUs and single GPU training efficiently. First download DAVIS 2017 dataset from [DAVIS website](https://davischallenge.org/), then modify *data_root* value in *configs/\_base_/davis.py* file, make sure *data_root* link to your training dataset path. Launch multi GPU training by the statement below: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=3278 tools/train.py configs/EfficientSCI/efficientsci_base.py --distributed=True ``` Launch single GPU training by the statement below. Default using GPU 0. One can also choosing GPUs by specify CUDA_VISIBLE_DEVICES ``` python tools/train.py configs/EfficientSCI/efficientsci_base.py ``` ## Testing EfficientSCI on Grayscale Simulation Dataset Specify the path of weight parameters, then launch 6 benchmark test in grayscale simulation dataset by executing the statement below. ``` python tools/test.py configs/EfficientSCI/efficientsci_base.py --weights=checkpoints/efficientsci_base.pth ``` ## Testing EfficientSCI in Color Simulation Dataset First, download the model weight file (checkpoints/efficientsci/efficientsci_base_mid_color.pth) and test data (datasets/middle_scale) from [Dropbox](https://www.dropbox.com/sh/ig08kyi2kdnjxm1/AAAjskial4ZEQ_9Qp31SEYeda?dl=0) or [BaiduNetdisk](https://pan.baidu.com/s/1wRMBsYoyVFFsEI5-lTPy6w?pwd=d2oi), and place them in the checkpoints folder and test_datasets folder respectively. Then, execute the statement below to launch EfficientSCI in 6 middle color simulation dataset. ``` python tools/test.py configs/EfficientSCI/efficientsci_base_mid_color.py --weights=checkpoints/efficientsci_base_mid_color.pth ``` ## Citation ``` @inproceedings{wang2023efficientsci, title={Efficientsci: Densely connected network with space-time factorization for large-scale video snapshot compressive imaging}, author={Wang, Lishun and Cao, Miao and Yuan, Xin}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={18477--18486}, year={2023} } ```
