new MLP classifier

app/Hackathon_setup/MLP_Image_Classifier.t7

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:873e9da54f68f1909be43707112432b65b7115b3e24c14e0052c00085f6145cc
-size 25251228
+oid sha256:32624d294f5e724d1fb112aa67aec2e502597422cae30c99d1eb4ccfb216ebde
+size 25247132

app/Hackathon_setup/face_recognition.py

@@ -118,13 +118,13 @@ def get_similarity(img1, img2):
 ##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
 def get_face_class(img1):
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    classes = ['person1','person2','person6','person7', 'person3']
+    classes = ['person1','person2','person6','person7']
     det_img1 = detected_face(img1)
     if det_img1 == 0:
         det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
     img1 = trnscm(det_img1).unsqueeze(0)
     feature_net = Siamese()  #                                           ##
-    feature_classifier = MLPClassifier(input_size=5, hidden_size=2048, num_classes=5)
+    feature_classifier = MLPClassifier(input_size=5, hidden_size=2048, num_classes=4)
     model = torch.load(current_path + "/siamese_model.t7", map_location="cpu")  ##
     feature_net.load_state_dict(model["net_dict"])  ##
     #classifier

app/Hackathon_setup/face_recognition_model.py

@@ -64,7 +64,7 @@ class Siamese(torch.nn.Module):
 ##########################################################################################################
 
 # YOUR CODE HERE for pytorch classifier
-classes = ['person1','person2','person6','person7', 'person3']
+classes = ['person1','person2','person6','person7']
 
 num_of_classes = len(classes)
 
@@ -75,9 +75,6 @@ classifier = nn.Sequential(nn.Linear(256, 64), nn.BatchNorm1d(64), nn.ReLU(),
 #classifier = classifier.to(device) -error thrown
 print(classifier)
 
-# Definition of classes as dictionary
-#classes = ['person1','person2','person3','person4','person5','person6','person7']
-
 class MLPClassifier(nn.Module):
     def __init__(self, input_size, hidden_size, num_classes):
         super(MLPClassifier, self).__init__()