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.
Learning to See in the Dark. CVPR 2018
| Date | Stars |
|---|---|
| 2026-07-24 | 5564 |
| 2026-07-25 | 5564 |
| 2026-07-28 | 5564 |
| 2026-07-30 | 5564 |
| 2026-08-06 | 5564 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Learning-to-See-in-the-Dark
This is a Tensorflow implementation of Learning to See in the Dark in CVPR 2018, by [Chen Chen](http://cchen156.github.io/), [Qifeng Chen](http://cqf.io/), [Jia Xu](http://pages.cs.wisc.edu/~jiaxu/), and [Vladlen Koltun](http://vladlen.info/).
[Project Website](http://cchen156.github.io/SID.html)<br/>
[Paper](http://cchen156.github.io/paper/18CVPR_SID.pdf)<br/>
This code includes the default model for training and testing on the See-in-the-Dark (SID) dataset.
## Demo Video
https://youtu.be/qWKUFK7MWvg
## Setup
### Requirement
Required python (version 2.7) libraries: Tensorflow (>=1.1) + Scipy + Numpy + Rawpy.
Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes but not tested.
### Dataset
**Update Sep, 2025:** We got signicant bills from Google storage. Please download the data manually and save a copy locally. Avoid re-downloading the data per experiment run. The download script is removed. Thank you!
**Update Aug, 2018:** We found some misalignment with the ground-truth for image 10034, 10045, 10172. Please remove those images for quantitative results, but they still can be used for qualitative evaluations.
You can download it directly from Google drive for the [Sony](https://storage.googleapis.com/isl-datasets/SID/Sony2025.zip) (25 GB) and [Fuji](https://storage.googleapis.com/isl-datasets/SID/Fuji2025.zip) (52 GB) sets.
There is download limit by Google drive in a fixed period of time. If you cannot download because of this, try these links: [Sony](https://drive.google.com/open?id=1G6VruemZtpOyHjOC5N8Ww3ftVXOydSXx) (25 GB) and [Fuji](https://drive.google.com/open?id=1C7GeZ3Y23k1B8reRL79SqnZbRBc4uizH) (52 GB).
New: we provide file parts in [Baidu Drive](https://pan.baidu.com/s/1fk8EibhBe_M1qG0ax9LQZA) now. After you download all the parts, you can combine them together by running: "cat SonyPart* > Sony.zip" and "cat FujiPart* > Fuji.zip".
The file lists are provided. In each row, there are a short-exposed image path, the corresponding long-exposed image path, camera ISO and F number. Note that multiple short-exposed images may correspond to the same long-exposed image.
The file name contains the image information. For example, in "10019_00_0.033s.RAF", the first digit "1" means it is from the test set ("0" for training set and "2" for validation set); "0019" is the image ID; the following "00" is the number in the sequence/burst; "0.033s" is the exposure time 1/30 seconds.
### Testing
1. Clone this repository.
2. Download the pretrained models by running
```Shell
python download_models.py
```
3. Run "python test_Sony.py". This will generate results on the Sony test set.
4. Run "python test_Fuji.py". This will generate results on the Fuji test set.
By default, the code takes the data in the "./dataset/Sony/" folder and "./dataset/Fuji/". If you save the dataset in other folders, please change the "input_dir" and "gt_dir" at the beginning of the code.
### Training new models
1. To train the Sony model, run "python train_Sony.py". The result and model will be save in "result_Sony" folder by default.
2. To train the Fuji model, run "python train_Fuji.py". The result and model will be save in "result_Fuji" folder by default.
By default, the code takes the data in the "./dataset/Sony/" folder and "./dataset/Fuji/". If you save the dataset in other folders, please change the "input_dir" and "gt_dir" at the beginning of the code.
Loading the raw data and processing by Rawpy takes significant more time than the backpropagation. By default, the code will load all the groundtruth data processed by Rawpy into memory without 8-bit or 16-bit quantization. This requires at least 64 GB RAM for training the Sony model and 128 GB RAM for the Fuji model. If you need to train it on a machine with less RAM, you may needExcerpt of 5,610 characters
Read on GitHub
48
Jason Cooke · Turing Complete Limited · New Zealand
1
Tim Gates · IRESS · Australia
1
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8743564f8a47f1df, topic:tensorflow