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SpringHHH committed Mar 22, 2019
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## Overview

This is a Tensorflow implementation of the Residual Encoder Network based on [Automatic Colorization](http://tinyclouds.org/colorize/) and the pre-trained VGG16 model from [https://github.com/machrisaa/tensorflow-vgg](https://github.com/machrisaa/tensorflow-vgg)
This is a TensorFlow implementation of the Residual Encoder Network based on [Automatic Colorization](http://tinyclouds.org/colorize/) and the pre-trained VGG16 model from [https://github.com/machrisaa/tensorflow-vgg](https://github.com/machrisaa/tensorflow-vgg)

**For latest tensorflow with [estimator](https://www.tensorflow.org/guide/estimators) support, please check [tf-1.12](https://github.com/Armour/Automatic-Image-Colorization/tree/tf-1.12) branch. (still under development, the training code is working now)**
**For latest TensorFlow with [estimator](https://www.tensorflow.org/guide/estimators) support, please check [tf-1.12](https://github.com/Armour/Automatic-Image-Colorization/tree/tf-1.12) branch. (still under development, the training code is working now)**

## Structure

@@ -17,10 +17,10 @@ This is a Tensorflow implementation of the Residual Encoder Network based on [Au
* `read_input.py`: all functions related to input
* `residual_encoder.py`: the residual encoder model
* `common.py`: the common part for training and testing, which is mainly the workflow for this model
* `train.py`: train the residual encoder model using Tensorflow built-in AdamOptimizer
* `train.py`: train the residual encoder model using TensorFlow built-in AdamOptimizer
* `test.py`: test your own images and save the output images

## Tensorflow graph
## TensorFlow graph

![residual_encoder](images/residual_encoder.png)

@@ -29,16 +29,16 @@ This is a Tensorflow implementation of the Residual Encoder Network based on [Au
* First please download pre-trained VGG16 model [vgg16.npy](https://mega.nz/#!YU1FWJrA!O1ywiCS2IiOlUCtCpI6HTJOMrneN-Qdv3ywQP5poecM) to vgg folder

* Option 1: Use pre-trained residual encoder model
* Model can be downloaded [here](https://github.com/Armour/Automatic-Image-Colorization/releases/tag/2.0)
* Download model [here](https://github.com/Armour/Automatic-Image-Colorization/releases/tag/2.0)
* Unzip all files to `summary_path` (you can change this path in `config.py`)

* Option 2: Train your own model!
1. Change the `batch_size` and `training_iters` if you want.
2. Change `training_dir` to your directory that contains all your training jpg images
2. Change `training_dir` to your directory that has all your training jpg images
3. Run `python train.py`

* Test
1. Change `testing_dir` to your directory that contains all your testing jpg images
1. Change `testing_dir` to your directory that has all your testing jpg images
2. Run `python test.py`

## Examples

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