javier/Photovoltaic_Fault_Detector
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Merge branch 'master' into yolo3_tensorflow2

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Daniel Saavedra
2020-12-01 22:52:34 -03:00
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parent 6643b8501f 68b1249776
commit 5e93799680
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.coveralls.yml Normal file
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service_name: travis-pro
repo_token: dPa5VOUwCyUgWdKawMjs0my7p23JSLBqy

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GPS_Panel/README.md
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```
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.
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| 0-Normal | 0.95 | 0.97 | 0.96 | 1688 | |
| 1-Falla | 0.97 | 0.96 | 0.96 | 2084 | 0.96 |

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README.md
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![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.
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## 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