update model state

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+faceViT4.pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -24,7 +24,7 @@ model_name = "google/vit-base-patch16-224"
 processor = ViTImageProcessor.from_pretrained(model_name)   
 base_model = ViTModel.from_pretrained("WinKawaks/vit-small-patch16-224")
 model = ViT(base_model)
-model.load_state_dict(torch.load('faceViT6.pth'))
+model.load_state_dict(torch.load('faceViT4.pth'))
 
 # Set the model to evaluation mode
 model.eval()
@@ -34,7 +34,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 model.to(device)
 
 # Initialize MTCNN for face detection
-mtcnn = MTCNN(keep_all=True, min_face_size=20, thresholds=[0.6, 0.7, 0.7], device=device)
+mtcnn = MTCNN(keep_all=True, device=device)
 
 # Define the transformation
 transform = transforms.Compose([
@@ -43,7 +43,7 @@ transform = transforms.Compose([
 ])
 
 def cosine_similarity(embedding1, embedding2):
-    similarity = torch.nn.functional.cosine_similarity(embedding1.flatten().unsqueeze(0), embedding2.flatten().unsqueeze(0))
+    similarity = torch.nn.functional.cosine_similarity(embedding1.unsqueeze(0), embedding2.unsqueeze(0))
     return similarity.item()
 
 def align_face(frame):