The notebook shows how to train, custom object detection model in pytorch. The model is fasterrcnn with resnet50 backbone(backbone pertrained on imagenet). map@0.3:0.1:0.9 is used as evaluation metric. Wgisd dataset of grapes is used.
The notebook shows how to train, custom object detection model in pytorch. The model is fasterrcnn with resnet50 backbone(backbone pertrained on imagenet). map@0.3:0.1:0.9 is used as evaluation metric. Wgisd dataset of grapes is used.