diff --git a/.coveralls.yml b/.coveralls.yml
new file mode 100644
index 0000000..1e754e3
--- /dev/null
+++ b/.coveralls.yml
@@ -0,0 +1,2 @@
+service_name: travis-pro
+repo_token: dPa5VOUwCyUgWdKawMjs0my7p23JSLBqy
diff --git a/GPS_Panel/README.md b/GPS_Panel/README.md
index 71c7a5d..87eb02f 100755
--- a/GPS_Panel/README.md
+++ b/GPS_Panel/README.md
@@ -8,15 +8,15 @@ In the root project execute the following command to install all dependencies pr
 
 ```
 pip install -r requirements.txt
+```
 
 ## Example
 
 View example 
-
 ```
 Enumeration_KML.ipynb
-
 ```
+
 # Panel Classifier
 This algorithm uses the division of the panels to classify them individually if they are with any fault.
 
@@ -40,3 +40,6 @@ ClassifierPanel_KML.ipynb
 | 0-Normal | 0.95      | 0.97   | 0.96     | 1688    |          |
 | 1-Falla  | 0.97      | 0.96   | 0.96     | 2084    | 0.96     |
 
+
+
+
diff --git a/README.md b/README.md
index 9d3c3d5..0a1414e 100755
--- a/README.md
+++ b/README.md
@@ -4,9 +4,14 @@
 ![GitHub](https://img.shields.io/github/license/RentadroneCL/Photovoltaic_Fault_Detector)
 [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](CODE_OF_CONDUCT.md)
 [![Open Source Helpers](https://www.codetriage.com/rentadronecl/photovoltaic_fault_detector/badges/users.svg)](https://www.codetriage.com/rentadronecl/photovoltaic_fault_detector)
+[![Coverage Status](https://coveralls.io/repos/github/RentadroneCL/Photovoltaic_Fault_Detector/badge.svg)](https://coveralls.io/github/RentadroneCL/Photovoltaic_Fault_Detector)
 
 [Rentadrone.cl](https://rentadronecl.github.io)
 
+## Forum
+
+This project is part of the [UNICEF Innovation Fund Discourse community](https://unicef-if.discourse.group/c/projects/rentadrone/10). You can post comments or questions about each category of [Rentadrone Developers](https://rentadrone.cl/developers/) algorithms. We encourage users to participate in the forum and to engage with fellow users.
+
 ## Summary
 
 Model-definition is a deep learning application for fault detection in photovoltaic plants. In this repository you will find trained detection models that point out where the panel faults are by using radiometric thermal infrared pictures. In [Web-API](https://github.com/RentadroneCL/Web-API) contains a performant, production-ready reference implementation of this repository.
@@ -219,15 +224,17 @@ It carries out detection on the image and write the image with detected bounding
 ## Evaluation
 The evaluation is integrated into the training process, if you want to do the independent evaluation you must go to the folder ssd_keras-master or keras-yolo3-master and use the following code
 
-`python evaluate.py -c config.json`
+`python evaluate.py -c config.json` 
+Example:
+`python keras-yolo3-master/evaluate.py -c config_full_yolo_fault_1_infer.json` 
 
 Compute the mAP performance of the model defined in `saved_weights_name` on the validation dataset defined in `valid_image_folder` and `valid_annot_folder`.
 
-| Model |  mAP | Config |
-|:--------------:|:------------------:|
-| YOLO3 Soiling  | 0.7302 |[config](config_full_yolo_fault_1_infer.json) |
-|   YOLO3 Diode  | 0.6127 | [config](config_full_yolo_fault_4_infer.json) |
-|   YOLO3 Affected Cell  |  0.7230 | [config](config_full_yolo_fault_2_infer.json)|
+| Model 	 	|  mAP 		     | Config |
+|:--------------:	|:------------------:|:------------------:|
+| YOLO3 Soiling  	| 0.7302 	     |[config](config_full_yolo_fault_1_infer.json) |
+|   YOLO3 Diode  	| 0.6127             | [config](config_full_yolo_fault_4_infer.json)|
+|   YOLO3 Affected Cell |  0.7230            | [config](config_full_yolo_fault_2_infer.json)|
 
 
 # Weights of Trained Models