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+# Neural Beacon Placement
+
+This code accompanies the paper *Jointly Optimizing Placement and Inference for Beacon-based Localization*.
+
+Arxiv Link: (Arxiv link here)
+
+Dependencies: Numpy, Tensorflow, and Matplotlib (for visualizations)
+
+
+### Important Files:
+
+**src/experiments** - This directory contains files defining the parameters for each experiment. Newly created experiment files should be placed here.
+
+**src/config.py** - This file defines the paths used for saving data, model weights, and results.
+
+
+
+### Evaluate a pretrained model:
+
+We provide 6 pretrained models you can use to reproduce our results. Download the models [here](https://github.com/ayanc/NSP/releases/download/untagged-b778691ee67fe075aa38/pretrained_models.zip).
+
+To evaluate a model, run the following commands:
+
+
+```bash
+unzip path_to_pretrained_models.zip
+cd src
+python gen_test_data.py maps/map1.txt #~200MB for each map
+python eval_model.py anneal_map1
+python gen_viz.py anneal_map1
+```
+Replace "anneal_map1" with another experiment name to evaluate other models.
+
+Since the propagation model is noisy, your numbers may differ slightly from ours.
+
+
+### Train a new model:
+
+To train a new model, create a new experiment file in the src/experiments directory. Then, run the following commands:
+
+```bash
+cd src
+python gen_train_data.py maps/map1.txt #~3GB for each map
+python run.py exp_name #Replace "exp_name" with the name of your experiment
+```
+
+### Generate a new map:
+
+To use a new map, convert the map to a .svg file. Then, run:
+```bash
+cd maps
+python svg2txt.py path_to_svg 25 25 > path_to_map.txt # creates an evenly spaced grid of 25 x 25 beacon locations
+```
+To use the map, first generate train and test data:
+```bash
+python gen_train_data.py path_to_map.txt
+python gen_test_data.py path_to_map.txt
+```
+
+Then, in an experiment file, set:
+```python
+MAPFILE = "path_to_map.txt"
+```