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Fix README instructions

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milesial 2021-08-17 22:25:24 +02:00 committed by GitHub
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README.md
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@ -19,11 +19,11 @@ Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in Py
## Quick start using Docker
1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/)
```
```bash
curl https://get.docker.com | sh && sudo systemctl --now enable docker
```
2. [Install the NVIDIA container toolkit:](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
```
```bash
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
@ -32,12 +32,12 @@ sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
```
3. [Download and run the image:](https://hub.docker.com/repository/docker/milesial/unet)
```
sudo docker run --rm --gpus all milesial/unet
```bash
sudo docker run --rm --gpus all -it milesial/unet
```
4. Download the data and run training:
```
```bash
bash scripts/download_data.sh
python train.py --amp
```
@ -55,15 +55,14 @@ This model was trained from scratch with 5000 images (no data augmentation) and
A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet).
You can **download and jump in the container** with ([docker >=19.03](https://docs.docker.com/get-docker/)):
```shell script
```console
docker run -it --rm --gpus all milesial/unet
```
### Training
```shell script
```console
> python train.py -h
usage: train.py [-h] [--epochs E] [--batch-size B] [--learning-rate LR]
[--load LOAD] [--scale SCALE] [--validation VAL] [--amp]
@ -101,7 +100,7 @@ To predict a multiple images and show them without saving them:
`python predict.py -i image1.jpg image2.jpg --viz --no-save`
```shell script
```console
> python predict.py -h
usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...]
[--output INPUT [INPUT ...]] [--viz] [--no-save]
@ -147,7 +146,7 @@ The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/c
You can also download it using the helper script:
```shell script
```
bash scripts/download_data.sh
```