From 27e87e14c9c54671a88647d271931c7feefbbc9d Mon Sep 17 00:00:00 2001 From: milesial Date: Tue, 17 Aug 2021 17:18:19 +0200 Subject: [PATCH] Add ToC to README Former-commit-id: 7ff11399bda293718b868aef79d8b438e9a18c2f --- README.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index adacb61..2a7b437 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,16 @@ Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in PyTorch for Kaggle's [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge) from high definition images. +- [Quick start using Docker](#quick-start-using-docker) +- [Description](#description) +- [Usage](#usage) + - [Docker](#docker) + - [Training](#training) + - [Prediction](#prediction) +- [Weights & Biases](#weights--biases) +- [Pretrained model](#pretrained-model) +- [Data](#data) + ## Quick start using Docker 1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/) @@ -116,7 +126,7 @@ optional arguments: ``` You can specify which model file to use with `--model MODEL.pth`. -### Weights & Biases +## Weights & Biases The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform. @@ -124,7 +134,7 @@ When launching a training, a link will be printed in the console. Click on it to by setting the `WANDB_API_KEY` environment variable. -### Pretrained model +## Pretrained model A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v1.0) is available for the Carvana dataset. It can also be loaded from torch.hub: ```python @@ -135,10 +145,10 @@ The training was done with a 100% scale and bilinear upsampling. ## Data The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data). -You can also download it using your Kaggle API key with: +You can also download it using the helper script: ```shell script -bash scripts/download_data.sh +bash scripts/download_data.sh ``` The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively. For Carvana, images are RGB and masks are black and white.