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keras-squeezenet [![Build Status](https://travis-ci.org/rcmalli/keras-squeezenet.svg?branch=master)](https://travis-ci.org/rcmalli/keras-squeezenet)\nSqueezeNet v1.1 Implementation using Keras Functional Framework 2.0\n\nThis [network model](https://github.com/rcmalli/keras-squeezenet/blob/master/images/SqueezeNet.png) has AlexNet accuracy with small footprint (5.1 MB)\nPretrained models are converted from original Caffe network.\n\n\n\n~~~bash\n# Most Recent One\npip install git+https://github.com/rcmalli/keras-squeezenet.git\n# Release Version\npip install keras_squeezenet\n~~~\n\n### News\n\n- Project is now up-to-date with the new Keras version (2.0).\n\n- Old Implementation is still available at 'keras1' branch but not updated.\n\n### Library Versions\n\n- Keras v2.1.1\n- Tensorflow v1.4\n\n### Example Usage\n\n~~~python\nimport numpy as np\nfrom keras_squeezenet import SqueezeNet\nfrom keras.applications.imagenet_utils import preprocess_input, decode_predictions\nfrom keras.preprocessing import image\n\nmodel = SqueezeNet()\n\nimg = image.load_img('../images/cat.jpeg', target_size=(227, 227))\nx = image.img_to_array(img)\nx = np.expand_dims(x, axis=0)\nx = preprocess_input(x)\n\npreds = model.predict(x)\nprint('Predicted:', decode_predictions(preds))\n\n~~~\n\n\n### References\n\n1) [Keras Framework](www.keras.io)\n\n2) [SqueezeNet Official Github Repo](https://github.com/DeepScale/SqueezeNet)\n\n3) [SqueezeNet Paper](http://arxiv.org/abs/1602.07360)\n\n\n### Licence \n\nMIT License \n\nNote: If you find this project useful, please include reference link in your work.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frcmalli%2Fkeras-squeezenet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frcmalli%2Fkeras-squeezenet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frcmalli%2Fkeras-squeezenet/lists"}