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https://github.com/kentsommer/keras-inceptionV4
Keras Implementation of Google's Inception-V4 Architecture (includes Keras compatible pre-trained weights)
https://github.com/kentsommer/keras-inceptionV4
Last synced: about 1 month ago
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Keras Implementation of Google's Inception-V4 Architecture (includes Keras compatible pre-trained weights)
- Host: GitHub
- URL: https://github.com/kentsommer/keras-inceptionV4
- Owner: kentsommer
- License: apache-2.0
- Created: 2017-01-19T07:25:31.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-09-10T05:47:38.000Z (over 7 years ago)
- Last Synced: 2024-05-10T09:32:03.647Z (7 months ago)
- Language: Python
- Homepage:
- Size: 1.63 MB
- Stars: 466
- Watchers: 21
- Forks: 176
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-keras : https://github.com/kentsommer/keras-inceptionV4
- awesome-image-classification - unofficial-keras : https://github.com/kentsommer/keras-inceptionV4
README
# Keras Inception-V4
Keras implementation of Google's inception v4 model with ported weights!As described in:
[Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi)](https://arxiv.org/abs/1602.07261)Note this Keras implementation tries to follow the [tf.slim definition](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v4.py) as closely as possible.
Pre-Trained weights for this Keras model can be found here (ported from the tf.slim ckpt): https://github.com/kentsommer/keras-inceptionV4/releases
You can evaluate a sample image by performing the following (weights are downloaded automatically):
* ```$ python evaluate_image.py```
```
Loaded Model Weights!
Class is: African elephant, Loxodonta africana
Certainty is: 0.868498
```# News
5/23/2017:* Enabled support for both Theano and Tensorflow (again... :neckbeard:)
* Added useful training parameters
* l2 regularization added to conv layers
* Variance Scaling initialization added to conv layers
* Momentum value updated for batch_norm layers
* Updated pre-processing to match paper (subtracts 0.5 instead of 1.0 :fire:)
* Minor code changes and cleanup is also included in the recent changes# Performance Metrics (@Top5, @Top1)
Error rate on non-blacklisted subset of ILSVRC2012 Validation Dataset (Single Crop):
* Top@1 Error: 19.54%
* Top@5 Error: 4.88%These error rates are actually slightly lower than the listed error rates in the paper:
* Top@1 Error: 20.0%
* Top@5 Error: 5.0%