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Image classification models. Keras.

Project description

PyPI version Build Status

Classification models Zoo

Trained on ImageNet classification models. Keras.

Architectures:

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
Note

[SE-]ResNeXt and SENet models build with GroupConvolution which is not implemented in Keras/TensorFlow. For correct work of load_model function custom object is used. To be able to load one of these models from file, please, import classification_models before.

Weights

Name Classes Models
'imagenet' 1000 all models
'imagenet11k-place365ch' 11586 resnet50
'imagenet11k' 11221 resnet152

Installation

Requirements:

  • python >= 3.5
  • keras >= 2.1.0
  • tensorflow >= 1.9
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 package:

$ pip install image-classifiers

Latest version:

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

Examples

Loading model with imagenet weights:
  1. Direct way (keras-applications like)
from classification_models.resnet import ResNet18, preprocess_input

model = ResNet18((224, 224, 3), weights='imagenet')
  1. Using Classifiers container
from classification_models import Classifiers

classifier, preprocess_input = Classifiers.get('resnet18')
model = classifier((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.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.resnet import ResNet18, preprocess_input

# 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.resnet import ResNet18, preprocess_input

# 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)

Project details


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