Image classification models. Keras.
Project description
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)
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