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+# Deep Learning for Decentralized Parking Lot Occupancy Detection
+
+This repo contains code to reproduce the experiments presented in [Deep Learning for Decentralized Parking Lot Occupancy Detection](https://www.sciencedirect.com/science/article/pii/S095741741630598X).
+
+Visit the [project website](http://cnrpark.it/) for more info and resources (dataset, pre-trained models).
+
+## Requirements
+
+ - Caffe with Python interface (PyCaffe)
+
+## Steps to reproduce experiments
+
+ 1. Clone this repo together with its submodules:
+
+    ```bash
+    git clone --recursive https://github.com/fabiocarrara/deep-parking.git
+    ```
+
+ 2. Download the datasets using the following links and extract them somewhere.
+
+    | Dataset | Link | Size | 
+    | ------- | ---- | ---: |
+    | CNRPark | http://cnrpark.it/dataset/CNRPark-Patches-150x150.zip | 36.6 MB |
+    | CNR-EXT | http://cnrpark.it/dataset/CNR-EXT-Patches-150x150.zip | 449.5 MB |
+    | PKLot   | visit [PKLot webpage](https://web.inf.ufpr.br/vri/databases/parking-lot-database/) | 4.6 GB |
+
+
+ 3. Get the dataset splits and extract them in the repo folder
+    ```bash
+    # Listfile containing dataset splits
+    wget http://cnrpark.it/dataset/splits.zip
+    unzip splits.zip
+    ```
+
+ 4. Add a `config.py` files inside each folder in `splits/` to tell `pyffe` where the images are.
+    The content of the files should be like this (adjust the `root_dir` attribute to the absolute path of the extracted datasets):
+    ```python
+    config = dict(root_folder = '/path/to/dataset/dir/')
+    ```
+    This path will be prepended to each line in the list files defining the various splits.
+
+
+ 5. Train and evaluate all the models by running:
+    ```bash
+    python main.py
+    ```
+    Modify `main.py` to select the experiments you want to reproduce.
+    Run `pklot.py` if you want to train and evaluate our architecture on the PKLot splits only.
+    
+## Citation
+
+```
+@article{amato2017deep,
+  title={Deep learning for decentralized parking lot occupancy detection},
+  author={Amato, Giuseppe and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Meghini, Carlo and Vairo, Claudio},
+  journal={Expert Systems with Applications},
+  volume={72},
+  pages={327--334},
+  year={2017},
+  publisher={Pergamon}
+}
+```
+