added model trained with full food101 dataset for 10 epochs

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth filter=lfs diff=lfs merge=lfs -text
 examples/pizza_dad.jpeg filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_food101_FULL.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_food101_FULL.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c940eb26e29abc1d07e29a67cbb0ca69cb915fce3e63b24dd473e3dd8b025aa
+size 31853626

app.py

@@ -22,7 +22,7 @@ with open("class_names.txt","r") as f:
 effnetb2_model, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
 
 # Load save weights
-effnetb2_model.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+effnetb2_model.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_FULL.pth",
                                           map_location=torch.device("cpu"))) # map location to cpu is a must, as we have trained our model in the GPU
 
 ### 3. Predict function