SqueezeNet implementation with Keras Framework
SqueezeNet v1.1 Implementation using Keras Functional Framework 2.0
This network model has AlexNet accuracy with small footprint (5.1 MB) Pretrained models are converted from original Caffe network.
# Most Recent One
pip install git+https://github.com/rcmalli/keras-squeezenet.git
# Release Version
pip install keras_squeezenet
Project is now up-to-date with the new Keras version (2.0).
Old Implementation is still available at 'keras1' branch but not updated.
import numpy as np
from keras_squeezenet import SqueezeNet
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.preprocessing import image
model = SqueezeNet()
img = image.load_img('../images/cat.jpeg', target_size=(227, 227))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds))
MIT License
Note: If you find this project useful, please include reference link in your work.