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Daniel Saavedra
2020-02-10 00:30:30 -03:00
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## Detection
Grab the pretrained weights of SSD and YOLO3 from https://drive.google.com/drive/folders/1FuhIJFxuzB9CLuRNwbKWFFsM6Nyweorf?usp=sharing
Grab the pretrained weights of SSD and YOLO3 from 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.
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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/1FuhIJFxuzB9CLuRNwbKWFFsM6Nyweorf?usp=sharing
All of weights of this trained model grab from 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%).