Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
SqueezeNet implementation with Keras Framework
| Date | Stars |
|---|---|
| 2026-07-24 | 405 |
| 2026-07-25 | 405 |
| 2026-07-28 | 405 |
| 2026-07-30 | 405 |
| 2026-08-06 | 405 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# keras-squeezenet [](https://travis-ci.org/rcmalli/keras-squeezenet)
SqueezeNet v1.1 Implementation using Keras Functional Framework 2.0
This [network model](https://github.com/rcmalli/keras-squeezenet/blob/master/images/SqueezeNet.png) has AlexNet accuracy with small footprint (5.1 MB)
Pretrained models are converted from original Caffe network.
~~~bash
# Most Recent One
pip install git+https://github.com/rcmalli/keras-squeezenet.git
# Release Version
pip install keras_squeezenet
~~~
### News
- Project is now up-to-date with the new Keras version (2.0).
- Old Implementation is still available at 'keras1' branch but not updated.
### Library Versions
- Keras v2.1.1
- Tensorflow v1.4
### Example Usage
~~~python
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))
~~~
### References
1) [Keras Framework](www.keras.io)
2) [SqueezeNet Official Github Repo](https://github.com/DeepScale/SqueezeNet)
3) [SqueezeNet Paper](http://arxiv.org/abs/1602.07360)
### Licence
MIT License
Note: If you find this project useful, please include reference link in your work.
Excerpt of 1,543 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:51083b31e035785e, topic:tensorflow