Update README.md

README.md

@@ -10,9 +10,9 @@ We provide PyTorch implementations for both unpaired and paired image-to-image t
 
 The code was written by [Jun-Yan Zhu](https://github.com/junyanz) and [Taesung Park](https://github.com/taesungp), and supported by [Tongzhou Wang](https://github.com/SsnL).
 
-This PyTorch implementation produces results comparable to or better than our original Torch software. If you would like to reproduce the same results as in the papers, check out the original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) and [pix2pix Torch](https://github.com/phillipi/pix2pix) code
+This PyTorch implementation produces results comparable to or better than our original Torch software. If you would like to reproduce the same results as in the papers, check out the original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) and [pix2pix Torch](https://github.com/phillipi/pix2pix) code in Lua/Torch. 
 
-**Note**: The current software works well with PyTorch 0.41+. Check out the older [branch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/tree/pytorch0.3.1) that supports PyTorch 0.1-0.3.
+**Note**: The current software works well with PyTorch 1.4. Check out the older [branch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/tree/pytorch0.3.1) that supports PyTorch 0.1-0.3.
 
 You may find useful information in [training/test tips](docs/tips.md) and [frequently asked questions](docs/qa.md). To implement custom models and datasets, check out our [templates](#custom-model-and-dataset). To help users better understand and adapt our codebase, we provide an [overview](docs/overview.md) of the code structure of this repository.
 
@@ -100,7 +100,7 @@ cd pytorch-CycleGAN-and-pix2pix
 
 - Install [PyTorch](http://pytorch.org) and 0.4+ and other dependencies (e.g., torchvision, [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate)).
   - For pip users, please type the command `pip install -r requirements.txt`.
-  - For Conda users, we provide a installation script `./scripts/conda_deps.sh`. Alternatively, you can create a new Conda environment using `conda env create -f environment.yml`.
+  - For Conda users, you can create a new Conda environment using `conda env create -f environment.yml`.
   - For Docker users, we provide the pre-built Docker image and Dockerfile. Please refer to our [Docker](docs/docker.md) page.
 
 ### CycleGAN train/test