javier/Photovoltaic_Fault_Detector
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update Table Weights

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Daniel Saavedra
2020-04-26 21:07:24 -04:00
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@@ -31,6 +31,11 @@ The models used for detection are SSD [SSD: Single Shot MultiBox Detector](https
Grab the pretrained weights of SSD and YOLO3 from [Drive_Weights](https://drive.google.com/drive/folders/1LSc9FkAwJrAAT8pAUWz8aax_biFAMMXS?usp=sharing)
| Model | Pretrained Weights |
|:-----------:|:-------------------:|
| SSD7/SSD300 | [Weight VGG16](https://drive.google.com/open?id=1VHTx28tGI94yFqwT_WHp-xkx_8Hh_A31)|
| YOLO3 | [Weight Full Yolo3](https://drive.google.com/open?id=1cnCQHl-TnOrwb-leug1I0O9vMBaSwJLt)|
## Type of Data
The images used for the design of this model were extracted by air analysis, specifically: FLIR aerial radiometric thermal infrared pictures, taken by UAV (R-JPEG format). Which were converted into .jpg images for the training of these detection models.
Example FLIR image:
@@ -177,6 +182,15 @@ Compute the mAP performance of the model defined in `saved_weights_name` on the
# Result
All of weights of this trained model grab from [Drive_Weights](https://drive.google.com/drive/folders/1LSc9FkAwJrAAT8pAUWz8aax_biFAMMXS?usp=sharing)
| Model | Pretrained Weights | Config |
|:--------------:|:------------------:|:--------:|
| SSD7 Panel | [weight](https://drive.google.com/open?id=1qNjfAp9sW1VJh8ewnb3NKuafhZockTqV) | [config](Result_ssd7_panel/config_7_panel.json) |
| SSD300 Soiling | [weight](https://drive.google.com/open?id=1IiOyYW8yPAh4IALbM_ZVqRhLdxV-ZSPw) | [config](config_300_fault_1.json) |
| YOLO3 Panel | [weight](https://drive.google.com/open?id=14zgtgDJv3KTvhRC-VOz6sqsGPC_bdrL1) | [config](config_full_yolo_panel_infer.json) |
| YOLO3 Soiling | [weight](https://drive.google.com/open?id=1YLgkn1wL5xAGOpwd2gzdfsJVGYPzszn-) | [config](config_full_yolo_fault_1_infer.json) |
| YOLO3 Diode | [weight](https://drive.google.com/open?id=1VUtrK9JVTbzBw5dX7_dgLTMToFHbAJl1) | [config](config_full_yolo_fault_4_infer.json) |
## Panel Detector
### SDD7
On folder [Result ssd7 panel](Result_ssd7_panel/) show code (jupyter notebook), weight and result of this model (mAP 89.8%).