Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[IJCV][ECCV2020 Spotlight] Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring
| Date | Stars |
|---|---|
| 2026-07-31 | 332 |
| 2026-08-01 | 332 |
| 2026-08-02 | 332 |
| 2026-08-06 | 332 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# ESTRNN & BSD **[ECCV2020 Spotlight]** [Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510188.pdf) **[IJCV2022]** [Real-world Video Deblurring: A Benchmark Dataset and An Efficient Spatio-Temporal Recurrent Neural Network](https://arxiv.org/abs/2106.16028) ([Springer Link](https://link.springer.com/article/10.1007/s11263-022-01705-6)) by [Zhihang Zhong](https://zzh-tech.github.io/), Ye Gao, Yinqiang Zheng, Bo Zheng, Imari Sato This work presents an efficient RNN-based model and **the first real-world dataset for image/video deblurring** :-) ## Visual Results ### Results on REDS (Synthetic)  ### Results on GOPRO (Synthetic)  ### Results on BSD (Real-world)  ## Beam-Splitter Deblurring Dataset (BSD) We have collected a new real-world video deblurring dataset ([BSD](https://drive.google.com/file/d/1VJdyojIAriC5QZp2N_0umEqkIMk1_9HA/view?usp=sharing)) with more scenes and better setups (center-aligned), using the proposed beam-splitter acquisition system:   The configurations of the new BSD dataset are as below: <img src="https://drive.google.com/uc?export=view&id=1-jgrABLYLRr_A7I7YmpYOsuhW3bzFz_3" alt="bsd_config" width="450"/> Quantitative results on different setups of BSD: <img src="https://drive.google.com/uc?export=view&id=1CErjtpb5OkeLdeGmx4tA0fdsAx27ADHC" alt="bsd_config" width="800"/> ## Quick Start ### Prerequisites - Python 3.6 - PyTorch 1.6 with GPU - opencv-python - scikit-image - lmdb - thop - tqdm - tensorboard ### Downloading Datasets Please download and unzip the dataset file for each benchmark. - [**BSD**](https://drive.google.com/drive/folders/1LKLCE_RqPF5chqWgmh3pj7cg-t9KM2Hd?usp=sharing) ([**Full BSD with RAW**](https://drive.google.com/file/d/1VJdyojIAriC5QZp2N_0umEqkIMk1_9HA/view?usp=sharing)) - [GOPRO](https://drive.google.com/file/d/1dHJX-TIY-ZsSV6-PbPZzmockp1H3B_5w/view?usp=sharing) - [REDS](https://drive.google.com/file/d/1lFHndopTiAAOIEkjZdvrziA8p17y4rjD/view?usp=sharing) If you failed to download BSD from Google drive, please try the following BaiduCloudDisk version: [BSD 1ms8ms](https://pan.baidu.com/s/1i7iMOZVOvBWmNYi8zkQIpw), password: bsd1 [BSD 2ms16ms](https://pan.baidu.com/s/1ur-XHeNoSTPFQJwBVfbofQ), password: bsd2 [BSD 3ms24ms](https://pan.baidu.com/s/1QNJlxiduwbQzCypy-7Mlbw), password: bsd3 ### Training Specify *\<path\>* (e.g. "*./dataset/*") as where you put the dataset file. Modify the corresponding dataset configurations in the command, or change the default values in "*./para/paramter.py*". Training command is as below: ```bash python main.py --data_root <path> --dataset BSD --ds_config 2ms16ms ``` You can also tune the hyper-parameters such as batch size, learning rate, epoch number (P.S.: the actual batch size for ddp mode is num_gpus*batch_size): ```bash python main.py --lr 1e-4 --batch_size 4 --num_gpus 2 --trainer_mode ddp ``` If you want to train on your own dataset, please refer to "*/data/how_to_make_dataset_file.ipynb*". ### Inference Please download [checkpoints](https://drive.google.com/file/d/1w68kAw56tGCjG4M96_zYmls8fQaTH1RM/view?usp=sharing) of pretrained models for different setups and unzip them under the main directory. #### Dataset (Test Set) Inference Command to run a pre-trained model on BSD (3ms-24ms): ```bash python main.py --test_only --test_checkpoint ./checkpoints/ESTRNN_C80B15_BSD_3ms24ms.tar --dataset BSD --ds_config 3ms24ms --video ``` #### Blurry Video Inference
Excerpt of 5,428 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6102391e4e716282, llm:topics: dataset, deblurring, deep-learning, eccv2020, motion-blur, real-world-data, video-deblurring; description: Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring (ECCV2020).
matched fp:6102391e4e716282, llm:topics: dataset, deblurring, deep-learning, eccv2020, motion-blur, real-world-data, video-deblurring; description: Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring (ECCV2020).
matched fp:6102391e4e716282, llm:topics: dataset, deblurring, deep-learning, eccv2020, motion-blur, real-world-data, video-deblurring; description: Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring (ECCV2020).