From 8c7cd2e23cdfa45e84301e66ff81bbc8a369aca7 Mon Sep 17 00:00:00 2001 From: junyanz Date: Sat, 26 Aug 2017 18:33:45 -0700 Subject: update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 14a43da..d50ab8c 100644 --- a/README.md +++ b/README.md @@ -111,7 +111,7 @@ python test.py --dataroot ./datasets/facades/testB/ --name facades_pix2pix --mod - CPU/GPU (default `--gpu_ids 0`): set`--gpu_ids -1` to use CPU mode; set `--gpu_ids 0,1,2` for multi-GPU mode. You need a large batch size (e.g. `--batchSize 32`) to benefit from multiple GPUs. - Visualization: during training, the current results can be viewed using two methods. First, if you set `--display_id` > 0, the results and loss plot will appear on a local graphics web server launched by [visdom](https://github.com/facebookresearch/visdom). To do this, you should have `visdom` installed and a server running by the command `python -m visdom.server`. The default server URL is `http://localhost:8097`. `display_id` corresponds to the window ID that is displayed on the `visdom` server. The `visdom` display functionality is turned on by default. To avoid the extra overhead of communicating with `visdom` set `--display_id 0`. Second, the intermediate results are saved to `[opt.checkpoints_dir]/[opt.name]/web/` as an HTML file. To avoid this, set `--no_html`. - Preprocessing: images can be resized and cropped in different ways using `--resize_or_crop` option. The default option `'resize_and_crop'` resizes the image to be of size `(opt.loadSize, opt.loadSize)` and does a random crop of size `(opt.fineSize, opt.fineSize)`. `'crop'` skips the resizing step and only performs random cropping. `'scale_width'` resizes the image to have width `opt.fineSize` while keeping the aspect ratio. `'scale_width_and_crop'` first resizes the image to have width `opt.loadSize` and then does random cropping of size `(opt.fineSize, opt.fineSize)`. -- Fine-tuning/Resume training: to fine-tune a pre-trained model, or resume the previous training, use the `--continue_train` flag. The program will then load the model based on `which_epoch`. By default, the program will initialize the epoch count as 0. Set '--epoch_count ' to specify a different starting epoch count. +- Fine-tuning/Resume training: to fine-tune a pre-trained model, or resume the previous training, use the `--continue_train` flag. The program will then load the model based on `which_epoch`. By default, the program will initialize the epoch count as 1. Set `--epoch_count ` to specify a different starting epoch count. ### CycleGAN Datasets -- cgit v1.2.3-70-g09d2