diff --git a/deepface/basemodels/ArcFace.py b/deepface/basemodels/ArcFace.py
index 2ba0b77..a5e99cc 100644
--- a/deepface/basemodels/ArcFace.py
+++ b/deepface/basemodels/ArcFace.py
@@ -10,7 +10,9 @@ import os
 from pathlib import Path
 import gdown
 
-def loadModel():
+#url = "https://drive.google.com/uc?id=1LVB3CdVejpmGHM28BpqqkbZP5hDEcdZY"
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5'):
 	base_model = ResNet34()
 	inputs = base_model.inputs[0]
 	arcface_model = base_model.outputs[0]
@@ -20,41 +22,36 @@ def loadModel():
 	arcface_model = keras.layers.Dense(512, activation=None, use_bias=True, kernel_initializer="glorot_normal")(arcface_model)
 	embedding = keras.layers.BatchNormalization(momentum=0.9, epsilon=2e-5, name="embedding", scale=True)(arcface_model)
 	model = keras.models.Model(inputs, embedding, name=base_model.name)
-	
+
 	#---------------------------------------
 	#check the availability of pre-trained weights
-	
+
 	home = str(Path.home())
-	
-	url = "https://drive.google.com/uc?id=1LVB3CdVejpmGHM28BpqqkbZP5hDEcdZY"
+
 	file_name = "arcface_weights.h5"
 	output = home+'/.deepface/weights/'+file_name
-	
+
 	if os.path.isfile(output) != True:
 
 		print(file_name," will be downloaded to ",output)
-		gdown.download(url, output, quiet=False)	
-	
+		gdown.download(url, output, quiet=False)
+
 	#---------------------------------------
 	
-	try:
-		model.load_weights(output)
-	except:
-		print("pre-trained weights could not be loaded.")
-		print("You might try to download it from the url ", url," and copy to ",output," manually")
-	
+	model.load_weights(output)
+
 	return model
-	
+
 def ResNet34():
-	
+
 	img_input = tensorflow.keras.layers.Input(shape=(112, 112, 3))
-	
+
 	x = tensorflow.keras.layers.ZeroPadding2D(padding=1, name='conv1_pad')(img_input)
 	x = tensorflow.keras.layers.Conv2D(64, 3, strides=1, use_bias=False, kernel_initializer='glorot_normal', name='conv1_conv')(x)
 	x = tensorflow.keras.layers.BatchNormalization(axis=3, epsilon=2e-5, momentum=0.9, name='conv1_bn')(x)
 	x = tensorflow.keras.layers.PReLU(shared_axes=[1, 2], name='conv1_prelu')(x)
 	x = stack_fn(x)
-		
+
 	model = training.Model(img_input, x, name='ResNet34')
 
 	return model
@@ -91,4 +88,4 @@ def stack_fn(x):
 	x = stack1(x, 64, 3, name='conv2')
 	x = stack1(x, 128, 4, name='conv3')
 	x = stack1(x, 256, 6, name='conv4')
-	return stack1(x, 512, 3, name='conv5')
\ No newline at end of file
+	return stack1(x, 512, 3, name='conv5')
diff --git a/deepface/basemodels/DeepID.py b/deepface/basemodels/DeepID.py
index 08c7923..8fbfa6a 100644
--- a/deepface/basemodels/DeepID.py
+++ b/deepface/basemodels/DeepID.py
@@ -9,44 +9,46 @@ from tensorflow.keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D
 
 #-------------------------------------
 
-def loadModel(url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'):
-	
+#url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5'):
+
 	myInput = Input(shape=(55, 47, 3))
-	
+
 	x = Conv2D(20, (4, 4), name='Conv1', activation='relu', input_shape=(55, 47, 3))(myInput)
 	x = MaxPooling2D(pool_size=2, strides=2, name='Pool1')(x)
 	x = Dropout(rate=0.99, name='D1')(x)
-	
+
 	x = Conv2D(40, (3, 3), name='Conv2', activation='relu')(x)
 	x = MaxPooling2D(pool_size=2, strides=2, name='Pool2')(x)
 	x = Dropout(rate=0.99, name='D2')(x)
-	
+
 	x = Conv2D(60, (3, 3), name='Conv3', activation='relu')(x)
 	x = MaxPooling2D(pool_size=2, strides=2, name='Pool3')(x)
 	x = Dropout(rate=0.99, name='D3')(x)
-	
+
 	x1 = Flatten()(x)
 	fc11 = Dense(160, name = 'fc11')(x1)
-	
+
 	x2 = Conv2D(80, (2, 2), name='Conv4', activation='relu')(x)
 	x2 = Flatten()(x2)
 	fc12 = Dense(160, name = 'fc12')(x2)
-	
+
 	y = Add()([fc11, fc12])
 	y = Activation('relu', name = 'deepid')(y)
-	
+
 	model = Model(inputs=[myInput], outputs=y)
 
 	#---------------------------------
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/deepid_keras_weights.h5') != True:
 		print("deepid_keras_weights.h5 will be downloaded...")
-		
+
 		output = home+'/.deepface/weights/deepid_keras_weights.h5'
 		gdown.download(url, output, quiet=False)
-		
-	model.load_weights(home+'/.deepface/weights/deepid_keras_weights.h5')	
-	
-	return model
\ No newline at end of file
+
+	model.load_weights(home+'/.deepface/weights/deepid_keras_weights.h5')
+
+	return model
diff --git a/deepface/basemodels/Facenet.py b/deepface/basemodels/Facenet.py
index 1f11325..2b7c6cc 100644
--- a/deepface/basemodels/Facenet.py
+++ b/deepface/basemodels/Facenet.py
@@ -21,7 +21,7 @@ def scaling(x, scale):
 	return x * scale
 
 def InceptionResNetV2():
-	
+
 	inputs = Input(shape=(160, 160, 3))
 	x = Conv2D(32, 3, strides=2, padding='valid', use_bias=False, name= 'Conv2d_1a_3x3') (inputs)
 	x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_1a_3x3_BatchNorm')(x)
@@ -42,7 +42,7 @@ def InceptionResNetV2():
 	x = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Conv2d_4b_3x3') (x)
 	x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_4b_3x3_BatchNorm')(x)
 	x = Activation('relu', name='Conv2d_4b_3x3_Activation')(x)
-	
+
 	# 5x Block35 (Inception-ResNet-A block):
 	branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
@@ -68,7 +68,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block35_1_Activation')(x)
-	
+
 	branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block35_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -93,7 +93,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block35_2_Activation')(x)
-	
+
 	branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block35_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -118,7 +118,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block35_3_Activation')(x)
-	
+
 	branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block35_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -143,7 +143,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block35_4_Activation')(x)
-	
+
 	branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block35_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -205,7 +205,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_1_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -224,7 +224,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_2_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -243,7 +243,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_3_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -262,7 +262,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_4_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -281,7 +281,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_5_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_6_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -299,8 +299,8 @@ def InceptionResNetV2():
 	up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_6_Conv2d_1x1') (mixed)
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
-	x = Activation('relu', name='Block17_6_Activation')(x)	
-	
+	x = Activation('relu', name='Block17_6_Activation')(x)
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_7_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -319,7 +319,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_7_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_8_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -338,7 +338,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_8_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_9_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -357,7 +357,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block17_9_Activation')(x)
-	
+
 	branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block17_10_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -377,7 +377,7 @@ def InceptionResNetV2():
 	x = add([x, up])
 	x = Activation('relu', name='Block17_10_Activation')(x)
 
-	# Mixed 7a (Reduction-B block): 8 x 8 x 2080	
+	# Mixed 7a (Reduction-B block): 8 x 8 x 2080
 	branch_0 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_0_Conv2d_0a_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Mixed_7a_Branch_0_Conv2d_0a_1x1_Activation')(branch_0)
@@ -404,7 +404,7 @@ def InceptionResNetV2():
 	x = Concatenate(axis=3, name='Mixed_7a')(branches)
 
 	# 5x Block8 (Inception-ResNet-C block):
-	
+
 	branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block8_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -423,7 +423,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block8_1_Activation')(x)
-	
+
 	branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block8_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -442,7 +442,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block8_2_Activation')(x)
-	
+
 	branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block8_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -461,7 +461,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block8_3_Activation')(x)
-	
+
 	branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block8_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -480,7 +480,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block8_4_Activation')(x)
-	
+
 	branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block8_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -499,7 +499,7 @@ def InceptionResNetV2():
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
 	x = add([x, up])
 	x = Activation('relu', name='Block8_5_Activation')(x)
-	
+
 	branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_0_Conv2d_1x1') (x)
 	branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
 	branch_0 = Activation('relu', name='Block8_6_Branch_0_Conv2d_1x1_Activation')(branch_0)
@@ -517,7 +517,7 @@ def InceptionResNetV2():
 	up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_6_Conv2d_1x1') (mixed)
 	up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 1})(up)
 	x = add([x, up])
-	
+
 	# Classification block
 	x = GlobalAveragePooling2D(name='AvgPool')(x)
 	x = Dropout(1.0 - 0.8, name='Dropout')(x)
@@ -530,23 +530,25 @@ def InceptionResNetV2():
 
 	return model
 
-def loadModel(url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'):
+#url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5'):
 	model = InceptionResNetV2()
-	
+
 	#-----------------------------------
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/facenet_weights.h5') != True:
 		print("facenet_weights.h5 will be downloaded...")
-		
+
 		output = home+'/.deepface/weights/facenet_weights.h5'
 		gdown.download(url, output, quiet=False)
-	
+
 	#-----------------------------------
-	
+
 	model.load_weights(home+'/.deepface/weights/facenet_weights.h5')
-	
+
 	#-----------------------------------
-	
-	return model
\ No newline at end of file
+
+	return model
diff --git a/deepface/basemodels/OpenFace.py b/deepface/basemodels/OpenFace.py
index eb63812..afd8bbe 100644
--- a/deepface/basemodels/OpenFace.py
+++ b/deepface/basemodels/OpenFace.py
@@ -13,7 +13,9 @@ from tensorflow.keras import backend as K
 
 #---------------------------------------
 
-def loadModel(url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'):
+#url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5'):
 	myInput = Input(shape=(96, 96, 3))
 
 	x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
@@ -229,19 +231,19 @@ def loadModel(url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9
 	model = Model(inputs=[myInput], outputs=norm_layer)
 	
 	#-----------------------------------
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/openface_weights.h5') != True:
 		print("openface_weights.h5 will be downloaded...")
-		
+
 		output = home+'/.deepface/weights/openface_weights.h5'
 		gdown.download(url, output, quiet=False)
-	
+
 	#-----------------------------------
-	
+
 	model.load_weights(home+'/.deepface/weights/openface_weights.h5')
-	
+
 	#-----------------------------------
-	
-	return model
\ No newline at end of file
+
+	return model
diff --git a/deepface/basemodels/VGGFace.py b/deepface/basemodels/VGGFace.py
index 1e13aa9..720fed1 100644
--- a/deepface/basemodels/VGGFace.py
+++ b/deepface/basemodels/VGGFace.py
@@ -12,7 +12,7 @@ else:
 	from tensorflow import keras
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
-	
+
 #---------------------------------------
 
 def baseModel():
@@ -60,34 +60,31 @@ def baseModel():
 	model.add(Convolution2D(2622, (1, 1)))
 	model.add(Flatten())
 	model.add(Activation('softmax'))
-	
+
 	return model
 
-def loadModel(url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'):
-	
+#url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/vgg_face_weights.h5'):
+
 	model = baseModel()
-	
+
 	#-----------------------------------
-	
+
 	home = str(Path.home())
 	output = home+'/.deepface/weights/vgg_face_weights.h5'
-	
+
 	if os.path.isfile(output) != True:
-		print("vgg_face_weights.h5 will be downloaded...")		
+		print("vgg_face_weights.h5 will be downloaded...")
 		gdown.download(url, output, quiet=False)
-	
+
 	#-----------------------------------
 	
-	try:
-		model.load_weights(output)
-	except Exception as err:
-		print(str(err))
-		print("Pre-trained weight could not be loaded.")
-		print("You might try to download the pre-trained weights from the url ", url, " and copy it to the ", output)
-	
+	model.load_weights(output)
+
 	#-----------------------------------
-	
+
 	#TO-DO: why?
 	vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
-	
-	return vgg_face_descriptor
\ No newline at end of file
+
+	return vgg_face_descriptor
diff --git a/deepface/extendedmodels/Age.py b/deepface/extendedmodels/Age.py
index b8f0561..f2ee1e1 100644
--- a/deepface/extendedmodels/Age.py
+++ b/deepface/extendedmodels/Age.py
@@ -16,7 +16,9 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel(url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'):
+#url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5'):
 
 	model = VGGFace.baseModel()
 
diff --git a/deepface/extendedmodels/Emotion.py b/deepface/extendedmodels/Emotion.py
index 9baaa38..447be74 100644
--- a/deepface/extendedmodels/Emotion.py
+++ b/deepface/extendedmodels/Emotion.py
@@ -15,7 +15,9 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
 
-def loadModel(url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'):
+#url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5'):
 
 	num_classes = 7
 
@@ -52,15 +54,18 @@ def loadModel(url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdB
 	if os.path.isfile(home+'/.deepface/weights/facial_expression_model_weights.h5') != True:
 		print("facial_expression_model_weights.h5 will be downloaded...")
 
-		#TO-DO: upload weights to google drive
+		output = home+'/.deepface/weights/facial_expression_model_weights.h5'
+		gdown.download(url, output, quiet=False)
 
-		#zip
+		"""
+		#google drive source downloads zip
 		output = home+'/.deepface/weights/facial_expression_model_weights.zip'
 		gdown.download(url, output, quiet=False)
 
 		#unzip facial_expression_model_weights.zip
 		with zipfile.ZipFile(output, 'r') as zip_ref:
 			zip_ref.extractall(home+'/.deepface/weights/')
+		"""
 
 	model.load_weights(home+'/.deepface/weights/facial_expression_model_weights.h5')
 
diff --git a/deepface/extendedmodels/Gender.py b/deepface/extendedmodels/Gender.py
index dff11ff..80fb9f1 100644
--- a/deepface/extendedmodels/Gender.py
+++ b/deepface/extendedmodels/Gender.py
@@ -14,7 +14,9 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel(url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'):
+#url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5'):
 
 	model = VGGFace.baseModel()
 
diff --git a/deepface/extendedmodels/Race.py b/deepface/extendedmodels/Race.py
index 4a94332..20059b7 100644
--- a/deepface/extendedmodels/Race.py
+++ b/deepface/extendedmodels/Race.py
@@ -16,7 +16,9 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel(url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'):
+#url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
+
+def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5'):
 
 	model = VGGFace.baseModel()
 
@@ -41,13 +43,18 @@ def loadModel(url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMp
 	if os.path.isfile(home+'/.deepface/weights/race_model_single_batch.h5') != True:
 		print("race_model_single_batch.h5 will be downloaded...")
 
-		#zip
+		output = home+'/.deepface/weights/race_model_single_batch.h5'
+		gdown.download(url, output, quiet=False)
+
+		"""
+		#google drive source downloads zip
 		output = home+'/.deepface/weights/race_model_single_batch.zip'
 		gdown.download(url, output, quiet=False)
 
 		#unzip race_model_single_batch.zip
 		with zipfile.ZipFile(output, 'r') as zip_ref:
 			zip_ref.extractall(home+'/.deepface/weights/')
+		"""
 
 	race_model.load_weights(home+'/.deepface/weights/race_model_single_batch.h5')