https://github.com/elite-sheep/vgg16

A vgg16 implementation with tensorflow-python
https://github.com/elite-sheep/vgg16

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A vgg16 implementation with tensorflow-python

• Host: GitHub
• URL: https://github.com/elite-sheep/vgg16
• Owner: elite-sheep
• Created: 2018-04-04T08:42:13.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-04-20T08:09:23.000Z (almost 7 years ago)
• Last Synced: 2024-11-30T01:26:29.986Z (2 months ago)
• Language: Python
• Size: 13.7 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# VGG16

This a implementation of vgg16 with tensorflow and python, study-oriented.

## Overview of vgg16

**vgg16** is an important convolutional neural network posted by Karen Simonyan. The link of their essay is here below:  

**https://arxiv.org/abs/1409.1556**  

## Usage

### Train

To train vgg16 with this project, you only need to provide a path. The path should contain a file named "model.json" and this JSON

file contains all information to train a vgg16 network with this project.  

A sample **model.json** is shown below:  

```

{

	"learning_rate":0.01,

	"momentum":0.9,

	"batchsize":8,

	"batches":70,

	"channel":3,

	"classes":2,

	"classnamelist":"labels.txt",

	"trainlist":"train.txt",

	"labellist":"labellist.txt"

}

```

The **classnamelist** is a file that contains all names of classes. The number of classes should be equal to the number of classes that

provides in "model.json".  

The **trainlist** file contains the absolute path to all pictures in the training set.  

The **labellist** file contains the index of class of a picture, corresponding to the index of pictures in the **trainlist**.  

To train a vgg16 using this project:  

```python

#!coding=utf-8

from src.vgg16 import Vgg16

def main():

		#init a new Vgg16 instance

    net = Vgg16()

		#load json file

    net.loadWithUntrainedJson(srcDir="./example")

		#train CNN, the model will be deployed in tgtDir

    net.train(tgtDir='./example')

if __name__ == '__main__':

    main()

```

### Predict

We provide a predict API to predict from a image list. The API is shown below:  

```python

def predict(self, tgtDir, imageListFile)

```

Before you use the model to predict, the model should be loaded with the following API:  

```python

def loadWithTrainedJson(self, modelDir)

```

The **modelDir** contains the model trained with this project or created 

by the users themselves.  

The **imageListFile** is the list of images to be predicted. Each line of the file contains an 

address of an image(we encourage absolute address).  

At last, the result will dump to **result.txt** to **tgtDir**.  

### More

Further question, please contact **[email protected]**