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Description:

imageclassifiers 1.0.0

Classification models Zoo - Keras (and TensorFlow Keras)
Trained on ImageNet classification models.
The library is designed to work both with Keras and TensorFlow Keras. See example below.
Important!
There was a huge library update 05 of August. Now classification-models works with both frameworks: keras and tensorflow.keras.
If you have models, trained before that date, to load them, please, use image-classifiers (PyPI package name) of 0.2.2 version. You can roll back using pip install -U image-classifiers==0.2.2.
Architectures:

VGG [16, 19]
ResNet [18, 34, 50, 101, 152]
ResNeXt [50, 101]
SE-ResNet [18, 34, 50, 101, 152]
SE-ResNeXt [50, 101]
SE-Net [154]
DenseNet [121, 169, 201]
Inception ResNet V2
Inception V3
Xception
NASNet [large, mobile]
MobileNet
MobileNet v2

Specification
The top-k accuracy were obtained using center single crop on the
2012 ILSVRC ImageNet validation set and may differ from the original ones.
The input size used was 224x224 (min size 256) for all models except:

NASNetLarge 331x331 (352)
InceptionV3 299x299 (324)
InceptionResNetV2 299x299 (324)
Xception 299x299 (324)

The inference *Time was evaluated on 500 batches of size 16.
All models have been tested using same hardware and software.
Time is listed just for comparison of performance.

Model
Acc@1
Acc@5
Time*
Source

vgg16
70.79
89.74
24.95
keras

vgg19
70.89
89.69
24.95
keras

resnet18
68.24
88.49
16.07
mxnet

resnet34
72.17
90.74
17.37
mxnet

resnet50
74.81
92.38
22.62
mxnet

resnet101
76.58
93.10
33.03
mxnet

resnet152
76.66
93.08
42.37
mxnet

resnet50v2
69.73
89.31
19.56
keras

resnet101v2
71.93
90.41
28.80
keras

resnet152v2
72.29
90.61
41.09
keras

resnext50
77.36
93.48
37.57
keras

resnext101
78.48
94.00
60.07
keras

densenet121
74.67
92.04
27.66
keras

densenet169
75.85
92.93
33.71
keras

densenet201
77.13
93.43
42.40
keras

inceptionv3
77.55
93.48
38.94
keras

xception
78.87
94.20
42.18
keras

inceptionresnetv2
80.03
94.89
54.77
keras

seresnet18
69.41
88.84
20.19
pytorch

seresnet34
72.60
90.91
22.20
pytorch

seresnet50
76.44
93.02
23.64
pytorch

seresnet101
77.92
94.00
32.55
pytorch

seresnet152
78.34
94.08
47.88
pytorch

seresnext50
78.74
94.30
38.29
pytorch

seresnext101
79.88
94.87
62.80
pytorch

senet154
81.06
95.24
137.36
pytorch

nasnetlarge
82.12
95.72
116.53
keras

nasnetmobile
74.04
91.54
27.73
keras

mobilenet
70.36
89.39
15.50
keras

mobilenetv2
71.63
90.35
18.31
keras

Weights

Name
Classes
Models

'imagenet'
1000
all models

'imagenet11k-place365ch'
11586
resnet50

'imagenet11k'
11221
resnet152

Installation
Requirements:

Keras >= 2.2.0 / TensorFlow >= 1.12
keras_applications >= 1.0.7

Note
This library does not have TensorFlow in a requirements for installation.
Please, choose suitable version (‘cpu’/’gpu’) and install it manually using
official Guide (https://www.tensorflow.org/install/).

PyPI stable package:
$ pip install image-classifiers==0.2.2

PyPI latest package:
$ pip install image-classifiers==1.0.0b1

Latest version:
$ pip install git+https://github.com/qubvel/classification_models.git

Examples
Loading model with imagenet weights:
# for keras
from classification_models.keras import Classifiers

# for tensorflow.keras
# from classification_models.tfkeras import Classifiers

ResNet18, preprocess_input = Classifiers.get('resnet18')
model = ResNet18((224, 224, 3), weights='imagenet')

This way take one additional line of code, however if you would
like to train several models you do not need to import them directly,
just access everything through Classifiers.
You can get all model names using Classifiers.models_names() method.
Inference example:
import numpy as np
from skimage.io import imread
from skimage.transform import resize
from keras.applications.imagenet_utils import decode_predictions
from classification_models.keras import Classifiers

ResNet18, preprocess_input = Classifiers.get('resnet18')

# read and prepare image
x = imread('./imgs/tests/seagull.jpg')
x = resize(x, (224, 224)) * 255 # cast back to 0-255 range
x = preprocess_input(x)
x = np.expand_dims(x, 0)

# load model
model = ResNet18(input_shape=(224,224,3), weights='imagenet', classes=1000)

# processing image
y = model.predict(x)

# result
print(decode_predictions(y))

Model fine-tuning example:
import keras
from classification_models.keras import Classifiers

ResNet18, preprocess_input = Classifiers.get('resnet18')

# prepare your data
X = ...
y = ...

X = preprocess_input(X)

n_classes = 10

# build model
base_model = ResNet18(input_shape=(224,224,3), weights='imagenet', include_top=False)
x = keras.layers.GlobalAveragePooling2D()(base_model.output)
output = keras.layers.Dense(n_classes, activation='softmax')(x)
model = keras.models.Model(inputs=[base_model.input], outputs=[output])

# train
model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X, y)