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+# Result Photovoltaic fault detector
+
+## Panel Detector
+### SDD7
+On folder [Result ssd7 panel](Result_ssd7_panel/) show code (jupyter notebook), weight and result of this model (mAP 89.8%).
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+### YOLO3
+On folder [Result yolo3 panel](Result_yolo3_panel/) weight and result of this model (mAP 86.3%).
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+## Soiling Fault Detector
+### SSD300
+On folder [Result ssd300 fault 1](Result_ssd300_fault_1/) show code (jupyter notebook), weight and result of this model (mAP 79.5%).
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+### YOLO3
+On folder [Result yolo3 fault 1](Result_yolo3_fault_1/) show [history train](Result_yolo3_fault_1/yolo3_full_yolo.output), weight and result of this model (mAP 73.02%).
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+## Affected Cell Detector
+### YOLO3
+On folder [Result yolo3 fault 2](Result_yolo3_fault_2/) show [history train](Result_yolo3_fault_2/yolo3_full_yolo.output), weight and result of this model (mAP 71.93%).
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+## Diode Fault Detector
+### YOLO3
+On folder [Result yolo3 fault 4](Result_yolo3_fault_4/) show [history train](Result_yolo3_fault_4/yolo3_full_yolo.output), weight and result of this model (mAP 66.22%).
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+## Panel Disconnect Detector
+### YOLO3
+To use the detector we must only use 'panel_yolo3_disconnect.py' with the previously established form, that is:
+`python predict_yolo3_disconnect.py -c config_full_yolo_panel_infer.json -i /path/to/image/ -o /path/output/result`
+To use this model, only the yolo3_panel detector model is needed.
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+The idea to detect the disconnection is by calculating the luminosity of each panel, to then normalize this data and highlight the panels with a luminosity out of normality.