Python files, Jupyter Notebooks and weights added.

CV-IP · Sep 10, 2020 · 94d91b4 · 94d91b4
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 # LLPackNet
 The project is the official implementation of our BMVC 2020 paper, "Towards Fast and Light-Weight Restoration of Dark Images"
 
-We show that we can enhance High Resolution,2848×4256, extremely dark single-image in the ballpark of 3 seconds even on a CPU. We achieve this with 2−7× fewer model parameters, 2−3× lower memory utilization, 5−20× speed up and yet maintain a competitive image reconstruction quality compared to the state-of-the-art algorithms. Watch the below video for results and overview.
+We show that we can enhance High Resolution,2848×4256, extremely dark single-image in the ballpark of 3 seconds even on a CPU. We achieve this with 2−7× fewer model parameters, 2−3× lower memory utilization, 5−20× speed up and yet maintain a competitive image reconstruction quality compared to the state-of-the-art algorithms. 
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+Watch the below video for results and overview of LLPackNet.
 
 [![Watch the project video](https://raw.githubusercontent.com/MohitLamba94/LLPackNet/master/pics/video.png)](https://www.youtube.com/watch?v=nO6pizVH_qM&feature=youtu.be)
 
+# How to use the code?
+The ```train.py``` and ```test.py``` files were used for training and testing. Relevant comments have been added to these files for better understanding. You however need to download the [SID dataset](https://github.com/cchen156/Learning-to-See-in-the-Dark) in your PC to execute them. 
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+The Jupyter Notebooks containing test code for the ablation studies can be also found in the ```ablation``` folder.
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+We used PyTorch version 1.3.1 with Python 3.7 to conduct the experiment. Along with the commonly used Python libraries such Numpy and Skimage, do install the [Rawpy](https://pypi.org/project/rawpy/) library required to read RAW images.
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 # Cite us
 If you find any information provided here useful please cite us,