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Tammy Yang 616cb8db09 | 6 years ago | |
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LICENSE.md | 8 years ago | |
README.md | 6 years ago | |
model.png | 8 years ago | |
model.py | 6 years ago | |
squeezenet_demo.py | 7 years ago |
This is the Keras implementation of SqueezeNet using functional API (arXiv 1602.07360).
SqueezeNet is a small model of AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.
The original model was implemented in caffe.
Differences:
This repository contains only the Keras implementation of the model, for other parameters used, please see the demo script, squeezenet_demo.py in the simdat package.
The training process uses a total of 2,600 images with 1,300 images per class (so, total two classes only).
There are a total 130 images used for validation. After 20 epochs, the model achieves the following:
loss: 0.6563 - acc: 0.7065 - val_loss: 0.6247 - val_acc: 0.8750
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