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  1. README.md +15 -3
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  # ClimateGAN: Raising Awareness about Climate Change by Generating Images of Floods
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  This repository contains the code used to train the model presented in our **[paper](https://arxiv.org/abs/2110.02871)**.
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  ## Using pre-trained weights
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- In the paper, we present ClimateGAN as a solution to produce images of floods. It can actually do **more**:
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  * reusing the segmentation map, we are able to isolate the sky, turn it red and in a few more steps create an image resembling the consequences of a wildfire on a neighboring area, similarly to the [California wildfires](https://www.google.com/search?q=california+wildfires+red+sky&source=lnms&tbm=isch&sa=X&ved=2ahUKEwisws-hx7zxAhXxyYUKHQyKBUwQ_AUoAXoECAEQBA&biw=1680&bih=917&dpr=2).
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  * reusing the depth map, we can simulate the consequences of a smog event on an image, scaling the intensity of the filter by the distance of an object to the camera, as per [HazeRD](http://www2.ece.rochester.edu/~gsharma/papers/Zhang_ICIP2017_HazeRD.pdf)
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  $ cd config
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  $ gdown https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K
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  $ unzip release-github-v1.zip
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- $ cd ..
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  ```
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  2. Run from the repo's root:
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-
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  1. With `comet`:
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  ```bash
 
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - Climate Change
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+ - GAN
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+ - Domain Adaptation
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+ license: gpl-3.0
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+ # datasets:
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+ # -
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+ ---
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+
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  # ClimateGAN: Raising Awareness about Climate Change by Generating Images of Floods
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  This repository contains the code used to train the model presented in our **[paper](https://arxiv.org/abs/2110.02871)**.
 
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  ## Using pre-trained weights
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+ In the paper, we present ClimateGAN as a solution to produce images of floods. It can actually do **more**:
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  * reusing the segmentation map, we are able to isolate the sky, turn it red and in a few more steps create an image resembling the consequences of a wildfire on a neighboring area, similarly to the [California wildfires](https://www.google.com/search?q=california+wildfires+red+sky&source=lnms&tbm=isch&sa=X&ved=2ahUKEwisws-hx7zxAhXxyYUKHQyKBUwQ_AUoAXoECAEQBA&biw=1680&bih=917&dpr=2).
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  * reusing the depth map, we can simulate the consequences of a smog event on an image, scaling the intensity of the filter by the distance of an object to the camera, as per [HazeRD](http://www2.ece.rochester.edu/~gsharma/papers/Zhang_ICIP2017_HazeRD.pdf)
 
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  $ cd config
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  $ gdown https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K
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  $ unzip release-github-v1.zip
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+ $ cd ..
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  ```
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  2. Run from the repo's root:
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+
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  1. With `comet`:
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  ```bash