update readme

bl0 · bl0 · commit 3e37b2de3726 · 2018-11-25T16:49:31.000+08:00
diff --git a/README.md b/README.md
@@ -1,53 +1,54 @@
 HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN
 =====================================
 
-Code for ["HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN"](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_HashGAN_Deep_Learning_CVPR_2018_paper.pdf).
+Code for CVPR 2018 Paper ["HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN"](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_HashGAN_Deep_Learning_CVPR_2018_paper.pdf).
 
 
 ## Prerequisites
 
-- Python3, NumPy, TensorFlow-gpu, SciPy, Matplotlib, easydict, yacs, tqdm
+- Python3, NumPy, TensorFlow-gpu, SciPy, Matplotlib, OpenCV, easydict, yacs, tqdm
 - A recent NVIDIA GPU
 
+We provide a `environment.yaml` for you and you can simplely use `conda env create -f environment.yml` to create the environment
+
+```bash
+conda create --no-default-packages -n HashGAN python=3.6 && source activate HashGAN
+conda install -y numpy scipy matplotlib  tensorflow-gpu opencv
+pip install easydict yacs tqdm pillow
+```
+
 ## Data Preparation
 In `data_list/` folder, we give three examples to show how to prepare image training data. If you want to add other datasets as the input, you need to prepare `train.txt`, `test.txt`, `database.txt` and `database_nolabel.txt` as CIFAR-10 dataset.
 
-You can download the whole cifar10 dataset including the images and data list from [here](https://github.com/thulab/DeepHash/releases/download/v0.1/cifar10.zip), and unzip it to data/cifar10 folder.
+You can download the whole cifar10 dataset including the images and data list from [here](https://github.com/thulab/DeepHash/releases/download/v0.1/cifar10.zip), and unzip it to `data/cifar10` folder.
 
-Make sure the tree of `/path/to/project/data/cifar10` looks like this:
+If you need run on NUSWIDE_81 and COCO, we recommend you to follow [here](https://github.com/thuml/HashNet/tree/master/pytorch#datasets) to prepare NUSWIDE_81 and COCO images.
 
-```
-.
-|-- database.txt
-|-- database_nolabel.txt
-|-- test
-|-- test.txt
-|-- train
-`-- train.txt
-```
+## Pretrained Models
+You can download the pretrained models in the [release page](https://github.com/thuml/HashGAN/releases) and modify config file to use the pretrained models.
+
+## Training
 
-If you need run on NUSWIDE_81 and COCO, we recommend you to follow https://github.com/thuml/HashNet/tree/master/pytorch#datasets to prepare NUSWIDE_81 and COCO images.
+The training process can be divided into two step:
+1. Training a image generator.
+2. Fintune Alexnet using original labeled images and generated images.
 
-## Models
-### TODO
+In `config` folder, we provide some examples to prepare yaml configuration.
 
-- [ ] code refactor
-  - [ ] wgan scale == 0
-  - [ ] G, D architecture
-  - [ ] evaluate mode
-  - [ ] resume training
-  - [ ] tensorboard
-- [ ] experiment
-  - [ ] training longger
-- [ ] code release
-  - [ ] pretrained G model  
-  - [ ] Pretrain model of Alexnet
-  - [ ] rerun all process on a fresh machine
+```
+config
+├── cifar_evaluation.yaml
+├── cifar_step_1.yaml
+├── cifar_step_2.yaml
+└── nuswide_step_1.yaml
+```
+
+You can run the model using command like the following:
 
-Configuration for th models is specified in a list of constants at the top of
-the file, you can use the following command to run it:
+- `python main.py --cfg config/cifar_step_1.yaml --gpus 0`
+- `python main.py --cfg config/cifar_step_2.yaml --gpus 0`
 
-- `python main.py`
+You can use tensorboard to monitor the training process such as losses and Mean Average Precision.
 
 ## Citation
 If you use this code for your research, please consider citing:
@@ -63,7 +64,7 @@ If you use this code for your research, please consider citing:
 
 ## Contact
 If you have any problem about our code, feel free to contact 
-- caoyue10@gmail.com
 - liubinthss@gmail.com
-
+- caoyue10@gmail.com
+  
 or describe your problem in Issues.
