Update app.py

app.py

@@ -21,13 +21,13 @@ train_tfms = tfms.A.Adapter(
 valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size), tfms.A.Normalize()])
 
 
-backbone = faster_rcnn.backbones.resnet_fpn.resnet18(pretrained=True)
-model1 = faster_rcnn.model(backbone=backbone, num_classes=len(class_map))
-model1.load_state_dict(torch.load('fasterRCNNFruits.pth', map_location=torch.device('cpu')))
-# model1 = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True), num_classes=len(class_map))
-# model1 = faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True), num_classes=len(class_map))
-# state_dict = torch.load('fasterRCNNFruits.pth', map_location=torch.device('cpu'))
-# model1.load_state_dict(state_dict)
+# backbone = faster_rcnn.backbones.resnet_fpn.resnet18(pretrained=True)
+# model1 = faster_rcnn.model(backbone=backbone, num_classes=len(class_map))
+# model1.load_state_dict(torch.load('fasterRCNNFruits.pth', map_location=torch.device('cpu')))
+model1 = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True), num_classes=len(class_map))
+model1 = faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True), num_classes=len(class_map))
+state_dict = torch.load('fasterRCNNFruits.pth', map_location=torch.device('cpu'))
+model1.load_state_dict(state_dict)
 
 def show_preds(input_image, display_label, display_bbox, detection_threshold):