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Daniel Saavedra
2020-02-25 22:18:56 -03:00
parent 67eb6a3fa9
commit 338e0e9ae9
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README.md
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@@ -25,7 +25,7 @@ The models used for detection are SSD [SSD: Single Shot MultiBox Detector](https
* [SSD_Keras](https://github.com/pierluigiferrari/ssd_keras#how-to-fine-tune-one-of-the-trained-models-on-your-own-dataset)
* [YOLOv3_Keras](https://github.com/experiencor/keras-yolo3)
Grab the pretrained weights of SSD and YOLO3 from https://drive.google.com/drive/folders/1LSc9FkAwJrAAT8pAUWz8aax_biFAMMXS?usp=sharing
Grab the pretrained weights of SSD and YOLO3 from [Drive_Weights](https://drive.google.com/drive/folders/1LSc9FkAwJrAAT8pAUWz8aax_biFAMMXS?usp=sharing)
## 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.
@@ -172,10 +172,10 @@ The evaluation is integrated into the training process, if you want to do the in
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`.
# Result
All of weights of this trained model grab from https://drive.google.com/drive/folders/1LSc9FkAwJrAAT8pAUWz8aax_biFAMMXS?usp=sharing
All of weights of this trained model grab from [Drive_Weights](https://drive.google.com/drive/folders/1LSc9FkAwJrAAT8pAUWz8aax_biFAMMXS?usp=sharing)
## Panel Detector
### SDD7
On folder Result_ssd7_panel show code (jupyter notebook), weight and result of this model (mAP 89.8%).
On folder [Result ssd7 panel](Result_ssd7_panel/) show code (jupyter notebook), weight and result of this model (mAP 89.8%).
![](Result_ssd7_panel/result_ssd7_panel/DJI_0020.jpg)
@@ -183,14 +183,14 @@ On folder Result_ssd7_panel show code (jupyter notebook), weight and result of t
## Soiling Fault Detector
### SSD300
On folder Result_ssd300_fault_1 show code (jupyter notebook), weight and result of this model (mAP 79.5%).
On folder [Result ssd300 fault 1](Result_ssd300_fault_1/) show code (jupyter notebook), weight and result of this model (mAP 79.5%).
![](Result_ssd300_fault_1/result_ssd300_fault_1/Mision_11_DJI_0011.jpg)
### YOLO3
On folder Result_yolo3_fault_1 show history train (yolo3_full_yolo.output), weight and result of this model (mAP 73.02%).
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%).
![](Result_yolo3_fault_1/result_yolo3_fault_1/Mision_11_DJI_0011.jpg)
@@ -198,7 +198,7 @@ On folder Result_yolo3_fault_1 show history train (yolo3_full_yolo.output), weig
## Diode Fault Detector
### YOLO3
On folder Result_yolo3_fault_4 show history train (yolo3_full_yolo.output), weight and result of this model (mAP 73.02%).
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 73.02%).
![](Result_yolo3_fault_4/result_yolo3_fault_4/Mision%2041_DJI_0044.jpg)
@@ -221,4 +221,4 @@ Before sending your pull requests, make sure you followed this list.
# Example to use trained model
In ['Example_Prediction'](ex_prediction.ipynb)
In ['Example_Prediction'](Example_prediction.ipynb)