https://github.com/hollance/vggnet-metal
iPhone version of the VGGNet convolutional neural network for image recognition
https://github.com/hollance/vggnet-metal
Last synced: about 1 month ago
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iPhone version of the VGGNet convolutional neural network for image recognition
- Host: GitHub
- URL: https://github.com/hollance/vggnet-metal
- Owner: hollance
- Created: 2016-08-29T11:36:19.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-04-10T10:19:20.000Z (about 8 years ago)
- Last Synced: 2023-10-25T23:19:14.884Z (over 1 year ago)
- Language: Swift
- Size: 781 KB
- Stars: 182
- Watchers: 10
- Forks: 38
- Open Issues: 3
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Metadata Files:
- Readme: README.markdown
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README
# VGGNet+Metal
This is the source code that accompanies my blog post [Convolutional neural networks on the iPhone with VGGNet](http://matthijshollemans.com/2016/08/30/vggnet-convolutional-neural-network-iphone/).
This project shows how to implement the 16-layer VGGNet convolutional neural network for basic image recognition on the iPhone.
VGGNet was a competitor in the [ImageNet ILSVRC-2014](http://image-net.org/challenges/LSVRC/2014/results) image classification competition and scored second place. For more details about VGGNet, see the [project page](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) and the [paper](http://arxiv.org/pdf/1409.1556):
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
arXiv:1409.1556
The iPhone app uses the VGGNet version from the [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo).
### How to run the app
You need an iPhone or iPad that supports Metal, running iOS 10 or better. (I have only tested the app on an iPhone 6s.)
**NOTE:** The source code won't run as-is. You need to do the following before you can build the Xcode project:
0 (optional) - If you don't want to set up a local environment for all of below, you can download the coverted file from [here](https://mega.nz/#!fcFyGJBJ!5Zy47jS3xhHP-0CjEVg5CRzx1wF1itnf9AHVdeRBHYk).
1 - Download the [prototxt](https://gist.githubusercontent.com/ksimonyan/211839e770f7b538e2d8/raw/0067c9b32f60362c74f4c445a080beed06b07eb3/VGG_ILSVRC_16_layers_deploy.prototxt) file.
2 - Download the [caffemodel](http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel) file.
3 - Run the conversion script from Terminal (requires Python 3 and the numpy and google.protobuf packages):
```
$ python3 convert_vggnet.py VGG_ILSVRC_16_layers_deploy.prototxt VGG_ILSVRC_16_layers.caffemodel ./output
```
This generates the file `./output/parameters.data`. It will take a few minutes! The reason you need to download the caffemodel file and convert it yourself is that `parameters.data` is a 500+ MB file and you can't put those on GitHub.
4 - Copy `parameters.data` into the `VGGNet-iOS/VGGNet` folder.
5 - Now you can build the app in Xcode (version 8.0 or better). You can only build for the device, the simulator isn't supported (gives compiler errors).
The VGGNet+Metal source code is licensed under the terms of the MIT license.