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solarPV_identification/CNN/README.md
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# CNN for PV recognition in the urban environment
Authors: S. Roquette, M. Esguerra, A. Guerra and R. Castello (EPFL-LESOPB). Contacts: roberto.castello@epfl.ch
To submit a job to the cluster, the files in the cluster_files folder are used, `model_main.py` creates a new Unet model and it writes the weight and architecture in the `directory path`address given a set of parameters passed as arguments: rotations, lights, dice, weight1, weight2, epochs
-`rotations` is either a 1 or a 0 specifying if the input images contain or not rotations.
-`lights` is either a 1 or a 0 specifying if the input images contain or not different lightnings.
- `dice`is 1 if the model uses dice loss function or 0 if it uses weighted cross entropy.
- `weight1`is the weight given to non-PV pixels errors if weighted cross entropy is used
- `weight2`is the weight given to PV pixels errors if weighted cross entropy is used
- `epochs` is the number of epochs the model will execute.
## Images
For testing purposes, if you are running on the SCITAS cluster, you can simply access the folders SI_25_2015_1301-13, SI_25_2015_1301-32 and SI_25_2015_1261-33 located at:
```
/work/hyenergy/raw/SwissTopo/RGB_25cm/data_resized/
```
Please change the correspondent location in the data_loaders.py
Addition: the images originally used (50MB) for the students report can be downloaded using the following link:
https://enacshare.epfl.ch/fPbBRCfnDshpk3QAg6t7Kwz4VaEGyWN
In order to be able to run interactively, you want to download and save it in a `Numpy_images/` folder, and run the Main.ipynb using settings `memory = True`, depending on your computer's power you will also want to set `full_data = False`.
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