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https://github.com/serengil/deepface.git
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google drive to github release
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@ -10,7 +10,9 @@ import os
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from pathlib import Path
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from pathlib import Path
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import gdown
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import gdown
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def loadModel():
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#url = "https://drive.google.com/uc?id=1LVB3CdVejpmGHM28BpqqkbZP5hDEcdZY"
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5'):
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base_model = ResNet34()
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base_model = ResNet34()
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inputs = base_model.inputs[0]
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inputs = base_model.inputs[0]
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arcface_model = base_model.outputs[0]
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arcface_model = base_model.outputs[0]
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@ -26,7 +28,6 @@ def loadModel():
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home = str(Path.home())
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home = str(Path.home())
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url = "https://drive.google.com/uc?id=1LVB3CdVejpmGHM28BpqqkbZP5hDEcdZY"
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file_name = "arcface_weights.h5"
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file_name = "arcface_weights.h5"
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output = home+'/.deepface/weights/'+file_name
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output = home+'/.deepface/weights/'+file_name
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@ -37,11 +38,7 @@ def loadModel():
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#---------------------------------------
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#---------------------------------------
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try:
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model.load_weights(output)
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model.load_weights(output)
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except:
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print("pre-trained weights could not be loaded.")
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print("You might try to download it from the url ", url," and copy to ",output," manually")
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return model
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return model
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@ -9,7 +9,9 @@ from tensorflow.keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D
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#-------------------------------------
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#-------------------------------------
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def loadModel(url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'):
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#url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5'):
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myInput = Input(shape=(55, 47, 3))
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myInput = Input(shape=(55, 47, 3))
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@ -530,7 +530,9 @@ def InceptionResNetV2():
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return model
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return model
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def loadModel(url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'):
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#url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5'):
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model = InceptionResNetV2()
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model = InceptionResNetV2()
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#-----------------------------------
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#-----------------------------------
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@ -13,7 +13,9 @@ from tensorflow.keras import backend as K
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#---------------------------------------
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#---------------------------------------
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def loadModel(url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'):
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#url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5'):
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myInput = Input(shape=(96, 96, 3))
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myInput = Input(shape=(96, 96, 3))
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x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
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x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
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@ -63,7 +63,9 @@ def baseModel():
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return model
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return model
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def loadModel(url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'):
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#url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/vgg_face_weights.h5'):
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model = baseModel()
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model = baseModel()
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@ -78,12 +80,7 @@ def loadModel(url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoe
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#-----------------------------------
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#-----------------------------------
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try:
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model.load_weights(output)
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model.load_weights(output)
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except Exception as err:
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print(str(err))
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print("Pre-trained weight could not be loaded.")
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print("You might try to download the pre-trained weights from the url ", url, " and copy it to the ", output)
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#-----------------------------------
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#-----------------------------------
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@ -16,7 +16,9 @@ elif tf_version == 2:
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation
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def loadModel(url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'):
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#url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5'):
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model = VGGFace.baseModel()
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model = VGGFace.baseModel()
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@ -15,7 +15,9 @@ elif tf_version == 2:
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
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def loadModel(url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'):
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#url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5'):
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num_classes = 7
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num_classes = 7
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@ -52,15 +54,18 @@ def loadModel(url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdB
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if os.path.isfile(home+'/.deepface/weights/facial_expression_model_weights.h5') != True:
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if os.path.isfile(home+'/.deepface/weights/facial_expression_model_weights.h5') != True:
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print("facial_expression_model_weights.h5 will be downloaded...")
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print("facial_expression_model_weights.h5 will be downloaded...")
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#TO-DO: upload weights to google drive
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output = home+'/.deepface/weights/facial_expression_model_weights.h5'
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gdown.download(url, output, quiet=False)
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#zip
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"""
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#google drive source downloads zip
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output = home+'/.deepface/weights/facial_expression_model_weights.zip'
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output = home+'/.deepface/weights/facial_expression_model_weights.zip'
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gdown.download(url, output, quiet=False)
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gdown.download(url, output, quiet=False)
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#unzip facial_expression_model_weights.zip
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#unzip facial_expression_model_weights.zip
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with zipfile.ZipFile(output, 'r') as zip_ref:
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with zipfile.ZipFile(output, 'r') as zip_ref:
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zip_ref.extractall(home+'/.deepface/weights/')
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zip_ref.extractall(home+'/.deepface/weights/')
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"""
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model.load_weights(home+'/.deepface/weights/facial_expression_model_weights.h5')
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model.load_weights(home+'/.deepface/weights/facial_expression_model_weights.h5')
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@ -14,7 +14,9 @@ elif tf_version == 2:
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation
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def loadModel(url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'):
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#url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5'):
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model = VGGFace.baseModel()
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model = VGGFace.baseModel()
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@ -16,7 +16,9 @@ elif tf_version == 2:
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation
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def loadModel(url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'):
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#url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5'):
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model = VGGFace.baseModel()
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model = VGGFace.baseModel()
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@ -41,13 +43,18 @@ def loadModel(url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMp
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if os.path.isfile(home+'/.deepface/weights/race_model_single_batch.h5') != True:
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if os.path.isfile(home+'/.deepface/weights/race_model_single_batch.h5') != True:
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print("race_model_single_batch.h5 will be downloaded...")
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print("race_model_single_batch.h5 will be downloaded...")
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#zip
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output = home+'/.deepface/weights/race_model_single_batch.h5'
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gdown.download(url, output, quiet=False)
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"""
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#google drive source downloads zip
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output = home+'/.deepface/weights/race_model_single_batch.zip'
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output = home+'/.deepface/weights/race_model_single_batch.zip'
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gdown.download(url, output, quiet=False)
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gdown.download(url, output, quiet=False)
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#unzip race_model_single_batch.zip
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#unzip race_model_single_batch.zip
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with zipfile.ZipFile(output, 'r') as zip_ref:
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with zipfile.ZipFile(output, 'r') as zip_ref:
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zip_ref.extractall(home+'/.deepface/weights/')
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zip_ref.extractall(home+'/.deepface/weights/')
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"""
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race_model.load_weights(home+'/.deepface/weights/race_model_single_batch.h5')
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race_model.load_weights(home+'/.deepface/weights/race_model_single_batch.h5')
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