https://github.com/titu1994/keras_mixnets
Keras Implementation of MixNets: Mixed Depthwise Convolutions
https://github.com/titu1994/keras_mixnets
Last synced: 7 months ago
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Keras Implementation of MixNets: Mixed Depthwise Convolutions
- Host: GitHub
- URL: https://github.com/titu1994/keras_mixnets
- Owner: titu1994
- License: mit
- Created: 2019-07-26T14:52:39.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-03-30T16:37:28.000Z (over 5 years ago)
- Last Synced: 2025-03-18T09:01:46.852Z (7 months ago)
- Language: Python
- Size: 204 KB
- Stars: 39
- Watchers: 5
- Forks: 9
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Keras MixNets: Mixed Depthwise Convolutional Kernels
Keras Implementation of MixNets from the paper [MixNets: : Mixed Depthwise Convolution Kernels](https://arxiv.org/abs/1907.09595).
Code ported from the official codebase [https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet](https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet)
# Mixed Depthwise Convolutional Kernel
From the above paper, a Mixed Convolution is a group of convolutions with varying filter sizes. The paper suggests that [3x3, 5x5, 7x7] can be used safely without any loss in performance (and possible increase in performance), while a 9x9 or 11x11 may degrade performance if used without proper architecture search.
# Installation
## From PyPI:
```$ pip install keras_mixnets```
## From Master branch:
```
pip install git+https://github.com/titu1994/keras_mixnets.git
OR
git clone https://github.com/titu1994/keras_mixnets.git
cd keras_mixnets
pip install .
```
# Usage
Due to the use of Model Subclassing, the keras model built **cannot* be deserialized using `load_model`. You must build the model each time. tf.keras supports writing Layers which have additional Layers within them, but as Keras itself does not support it yet, these models cannot be deserialized using `load_model`.
```python
from keras_mixnets import MixNetSmall # Medium and Large can also be used
model = MixNetSmall((224, 224, 3), include_top=True)
```
# Weights
Weights for these models have not been ported yet from Tensorflow.
# Requirements
- Tensorflow 1.14+ (Not 2.x)
- Keras 2.2.4+