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VGG-19 pre-trained model for Keras
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readme.md

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
arXiv:1409.1556

In the paper, the VGG-19 model is denoted as configuration E. It achieves 7.5% top-5 error on ILSVRC-2012-val, 7.3% top-5 error on ILSVRC-2012-test.

Please cite the paper if you use the models.

###Contents:

model and usage demo: see vgg-19_keras.py

weights: vgg19_weights.h5

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vgg-19_keras.py
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import cv2, numpy as np
def VGG_19(weights_path=None):
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='softmax'))
if weights_path:
model.load_weights(weights_path)
return model
if __name__ == "__main__":
im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32)
im[:,:,0] -= 103.939
im[:,:,1] -= 116.779
im[:,:,2] -= 123.68
im = im.transpose((2,0,1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
model = VGG_19('vgg19_weights.h5')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
out = model.predict(im)
print np.argmax(out)
@DanlanChen
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How can I used it for grey scaled image?

@AdityaSoni19031997
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AdityaSoni19031997 commented Aug 30, 2017 •
edited
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For gray scale we need to change the channel to 1 ...
For colored its 3...
We need to play around with the input parameter of the very first layer...(3->1)

@manimtechrs
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I have 4 classes to predict . can i use VGG-16 model to predict classes .

@flyingduck92
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My tensorflow version is 1.8.0

I cannot load the vgg-19 weight file.
This is the error i got:
You are trying to load a weight file containing 0 layers into a model with 19 layers.

@NEUUCAS
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NEUUCAS commented Mar 17, 2019

My tensorflow version is 1.8.0

I cannot load the vgg-19 weight file.
This is the error i got:
You are trying to load a weight file containing 0 layers into a model with 19 layers.

I have the same error. How to fix it?

@NimmiGhetia
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NimmiGhetia commented Apr 15, 2019 •
edited
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I am getting this error, can you help me resolve the error. I haven't change the code

Using TensorFlow backend.
vgg-19_keras.py:11: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(64, (3, 3), activation="relu")
model.add(Convolution2D(64, 3, 3, activation='relu'))
WARNING:tensorflow:From /opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
vgg-19_keras.py:13: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(64, (3, 3), activation="relu")
model.add(Convolution2D(64, 3, 3, activation='relu'))
vgg-19_keras.py:17: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(128, (3, 3), activation="relu")
model.add(Convolution2D(128, 3, 3, activation='relu'))
vgg-19_keras.py:19: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(128, (3, 3), activation="relu")
model.add(Convolution2D(128, 3, 3, activation='relu'))
Traceback (most recent call last):
File "vgg-19_keras.py", line 73, in
model = VGG_19('vgg19_weights.h5')
File "vgg-19_keras.py", line 20, in VGG_19
model.add(MaxPooling2D((2,2), strides=(2,2)))
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/engine/sequential.py", line 181, in add
output_tensor = layer(self.outputs[0])
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/engine/base_layer.py", line 457, in call
output = self.call(inputs, **kwargs)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/layers/pooling.py", line 205, in call
data_format=self.data_format)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/layers/pooling.py", line 268, in _pooling_function
pool_mode='max')
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 3978, in pool2d
data_format=tf_data_format)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 2748, in max_pool
name=name)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 5137, in max_pool
data_format=data_format, name=name)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
op_def=op_def)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1823, in init
control_input_ops)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1662, in _create_c_op
raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_2/MaxPool' (op: 'MaxPool') with input shapes: [?,1,112,128].

@b-hakim
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b-hakim commented Apr 20, 2019

I got the same issue:

ValueError: You are trying to load a weight file containing 0 layers into a model with 19 layers.

@b-hakim
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b-hakim commented Apr 20, 2019 •
edited
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@NimmiGhetia your problem is in the keras,
edit the
~/.keras/keras.json and change the "image_data_format" to "image_data_format": "channels_first"

@Rouxkein
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***this my model run complete , i using vgg19 to train data cifar 10 ,you can refer it

from keras.datasets import cifar10
import cv2
import random
import numpy as np
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import cv2, numpy as np
import tensorflow as tf
#load data
(x_train,y_train),(x_test,y_test)=cifar10.load_data()

train data load 0 to 2000

ind_train = random.sample(list(range(x_train.shape[0])), 2000)
x_train = x_train[ind_train]
y_train = y_train[ind_train]

test data load 0 to 2000

ind_test = random.sample(list(range(x_test.shape[0])), 2000)
x_test = x_test[ind_test]
y_test = y_test[ind_test]
#resize image
def resize_data(data):
data_upscale=np.zeros((data.shape[0],224,224,3))
for i,img in enumerate(data):
l_img=cv2.resize(img,dsize=(224,224),interpolation=cv2.INTER_CUBIC)
data_upscale[i]=l_img
return data_upscale

resize train and test data

x_train_r = resize_data(x_train)
x_test_r = resize_data(x_test)

make explained variable hot-encoded

train_y= to_categorical(y_train)
test_y = to_categorical(y_test)

def VGG_19(weights=None, include_top=True, classes=10,input_shape=(224,224,3)):
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224,3)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
return model

model = VGG_19(input_shape = (224, 224, 3), classes = 10)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history=model.fit(x_train_r , train_y, epochs=5, batch_size=20, validation_data=(x_test_r , test_y ))
model.summary()

@mounirlazid
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I am looking for a simple example of a vgg16 model to classify 3D medical images. thank you for your help

@zhouhongyuthu
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HI，the h5 link is no longer valid. Could you fix it?

@ThatIsMyUsername
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HI，the h5 link is no longer valid. Could you fix it?

@petzlover
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vinitha “Great implementation of VGG19 in Keras, the pretrained weights make it very easy to try out.”

@lusiaj
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lusiaj commented Sep 25, 2025

lusia

Model Architecture: The Gist includes a Keras implementation of the VGG-19 architecture, which consists of 19 layers: 16 convolutional layers and 3 fully connected layers.

Weights: The pre-trained weights for the model are provided in the vgg19_weights.h5 file. These weights were obtained by directly converting the original Caffe model provided by the authors.

Usage: A usage demo is provided in the vgg-19_keras.py script, which demonstrates how to load the model and use it for predictions.

Citation: The original paper describing the VGG-19 architecture is:

Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556.

This indicates that the Gist is recognized as a valuable resource for obtaining a pre-trained VGG-19 model for Keras.

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