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: 4 days 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 (about 7 years ago)
• Last Synced: 2024-05-10T09:32:03.647Z (6 months ago)
• Language: Python
• Homepage:
• Size: 1.63 MB
• Stars: 466
• Watchers: 21
• Forks: 176
• Open Issues: 2
• 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%