diff --git a/README.md b/README.md
index 4f79a11..d61b0e8 100644
--- a/README.md
+++ b/README.md
@@ -91,19 +91,14 @@ The pretrained model is saved at `./checkpoints/[type]_pretrained`. Check [here]
 
 2. Generate images using the pretrained model.
 ```bash
-python test.py --dataroot [path_to_dataset] --name [type]_pretrained --model selectiongan --which_model_netG unet_256 --which_direction AtoB --dataset_mode aligned --norm batch --gpu_ids 0 --batchSize [BS] --loadSize [LS] --fineSize [FS] --no_flip --eval
+python test.py --dataroot [path_to_dataset] --name [type]_pretrained --model [gesturegan_model] --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm batch --gpu_ids 0 --batchSize [BS] --loadSize [LS] --fineSize [FS] --no_flip;
 ```
-`[path_to_dataset]`, is the path to the dataset. Dataset can be one of `dayton`, `cvusa`, and `ego2top`. `[type]_pretrained` is the directory name of the checkpoint file downloaded in Step 1, which should be one of `dayton_a2g_64_pretrained`, `dayton_g2a_64_pretrained`, `dayton_a2g_256_pretrained`, `dayton_g2a_256_pretrained`, `cvusa_pretrained`,and `ego2top_pretrained`. If you are running on CPU mode, change `--gpu_ids 0` to `--gpu_ids -1`. For [`BS`, `LS`, `FS`],
 
-- `dayton_a2g_64_pretrained`: [16,72,64]
-- `dayton_g2a_64_pretrained`: [16,72,64]
-- `dayton_g2a_256_pretrained`: [4,286,256]
-- `dayton_g2a_256_pretrained`: [4,286,256]
-- `cvusa_pretrained`: [4,286,256]
-- `ego2top_pretrained`: [8,286,256]
-
-Note that testing require large amount of disk space, because the model will generate 10 intermedia image results and 10 attention maps on disk. If you don't have enough space, append `--saveDisk` on the command line.
+`[path_to_dataset]` is the path to the dataset. Dataset can be one of `ntu`, `senz3d`, `dayton_a2g`, `dayton_g2a` and `cvusa`. `[type]_pretrained` is the directory name of the checkpoint file downloaded in Step 1, which should be one of `ntu_gesturegan_twocycle_pretrained`, `senz3d_gesturegan_twocycle_pretrained`, `dayton_a2g_64_gesturegan_onecycle_pretrained`, `dayton_g2a_64_gesturegan_onecycle_pretrained`, `dayton_a2g_gesturegan_onecycle_pretrained`, `dayton_g2a_gesturegan_onecycle_pretrained` and `cvusa_gesturegan_onecycle_pretrained`. 
+`[gesturegan_model]` is the directory name of the model of GestureGAN, which should be one of `gesturegan_twocycle` or `gesturegan_onecycle`.
+If you are running on CPU mode, change `--gpu_ids 0` to `--gpu_ids -1`. For [`BS`, `LS`, `FS`], please see `Training` and `Testing` sections.
 
+Note that testing require large amount of disk space. If you don't have enough space, append `--saveDisk` on the command line.
     
 3. The outputs images are stored at `./results/[type]_pretrained/` by default. You can view them using the autogenerated HTML file in the directory.
 
@@ -115,21 +110,49 @@ New models can be trained with the following commands.
 
 2. Train.
 
+For NTU dataset:
+```bash
+export CUDA_VISIBLE_DEVICES=3,4;
+python train.py --dataroot ./datasets/ntu --name ntu_gesturegan_twocycle --model gesturegan_twocycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0,1 --batchSize 4 --loadSize 286 --fineSize 256 --no_flip --lambda_L1 800 --cyc_L1 0.1 --lambda_identity 0.01 --lambda_feat 1000 --display_id 0 --niter 10 --niter_decay 10;
+```
+
+For Senz3D dataset:
+```bash
+export CUDA_VISIBLE_DEVICES=5,7;
+python train.py --dataroot ./datasets/senz3d --name senz3d_gesturegan_twocycle --model gesturegan_twocycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0,1 --batchSize 4 --loadSize 286 --fineSize 256 --no_flip --lambda_L1 800 --cyc_L1 0.1 --lambda_identity 0.01 --lambda_feat 1000 --display_id 0 --niter 10 --niter_decay 10;
+```
+
+For CVUSA dataset:
+```bash
+export CUDA_VISIBLE_DEVICES=0;
+python train.py --dataroot ./dataset/cvusa --name cvusa_gesturegan_onecycle --model gesturegan_onecycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0 --batchSize 4 --loadSize 286 --fineSize 256 --no_flip --cyc_L1 0.1 --lambda_identity 100 --lambda_feat 100 --display_id 0 --niter 15 --niter_decay 15;
+```
+
+For Dayton (a2g direction, 256) dataset:
 ```bash
-# To train on the dayton dataset on 64*64 resolution,
+export CUDA_VISIBLE_DEVICES=0;
+python train.py --dataroot ./datasets/dayton_a2g --name dayton_a2g_gesturegan_onecycle --model gesturegan_onecycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0 --batchSize 4 --loadSize 286 --fineSize 256 --no_flip --cyc_L1 0.1 --lambda_identity 100 --lambda_feat 100 --display_id 0 --niter 20 --niter_decay 15;
+```
 
-python train.py --dataroot [path_to_dayton_dataset] --name [experiment_name] --model selectiongan --which_model_netG unet_256 --which_direction AtoB --dataset_mode aligned --norm batch --gpu_ids 0 --batchSize 16 --niter 50 --niter_decay 50 --loadSize 72 --fineSize 64 --no_flip --lambda_L1 100 --lambda_L1_seg 1 --display_winsize 64 --display_id 0
+For Dayton (g2a direction, 256) dataset:
+```bash
+export CUDA_VISIBLE_DEVICES=1;
+python train.py --dataroot ./datasets/dayton_g2a --name dayton_g2a_gesturegan_onecycle --model gesturegan_onecycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0 --batchSize 4 --loadSize 286 --fineSize 256 --no_flip --cyc_L1 0.1 --lambda_identity 100 --lambda_feat 100 --display_id 0 --niter 20 --niter_decay 15;
 ```
+
+For Dayton (a2g direction, 64) dataset:
 ```bash
-# To train on the datasets on 256*256 resolution,
+export CUDA_VISIBLE_DEVICES=0;
+python train.py --dataroot ./datasets/dayton_a2g --name dayton_a2g_64_gesturegan_onecycle --model gesturegan_onecycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0 --batchSize 16 --loadSize 72 --fineSize 64 --no_flip --cyc_L1 0.1 --lambda_identity 100 --lambda_feat 100 --display_id 0 --niter 50 --niter_decay 50;
+```
 
-python train.py --dataroot [path_to_dataset] --name [experiment_name] --model selectiongan --which_model_netG unet_256 --which_direction AtoB --dataset_mode aligned --norm batch --gpu_ids 0 --batchSize [BS] --loadSize [LS] --fineSize [FS] --no_flip --display_id 0 --lambda_L1 100 --lambda_L1_seg 1
+For Dayton (g2a direction, 64) dataset:
+```bash
+export CUDA_VISIBLE_DEVICES=1;
+python train.py --dataroot ./datasets/dayton_g2a --name dayton_g2a_64_gesturegan_onecycle --model gesturegan_onecycle --which_model_netG resnet_9blocks --which_direction AtoB --dataset_mode aligned --norm instance --gpu_ids 0 --batchSize 16 --loadSize 72 --fineSize 64 --no_flip --cyc_L1 0.1 --lambda_identity 100 --lambda_feat 100 --display_id 0 --niter 50 --niter_decay 50;
 ```
-- For dayton dataset, [`BS`,`LS`,`FS`]=[4,286,256], append `--niter 20 --niter_decay 15`.
-- For cvusa dataset, [`BS`,`LS`,`FS`]=[4,286,256], append `--niter 15 --niter_decay 15`.
-- For ego2top dataset, [`BS`,`LS`,`FS`]=[8,286,256], append `--niter 5 --niter_decay 5`.
 
-There are many options you can specify. Please use `python train.py --help`. The specified options are printed to the console. To specify the number of GPUs to utilize, use `export CUDA_VISIBLE_DEVICES=[GPU_ID]`. Training will cost about one week with the default `--batchSize` on one NVIDIA GeForce GTX 1080 Ti GPU. So we suggest you use a larger `--batchSize`, while performance is not tested using a larger `--batchSize`.
+There are many options you can specify. Please use `python train.py --help`. The specified options are printed to the console. To specify the number of GPUs to utilize, use `export CUDA_VISIBLE_DEVICES=[GPU_ID]`. Note that train `gesturegan_onecycle` only needs one GPU, while train `gesturegan_twocycle` needs two GPUs.
 
 To view training results and loss plots on local computers, set `--display_id` to a non-zero value and run `python -m visdom.server` on a new terminal and click the URL [http://localhost:8097](http://localhost:8097/).
 On a remote server, replace `localhost` with your server's name, such as [http://server.trento.cs.edu:8097](http://server.trento.cs.edu:8097).