Update README.md

README.md

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 # kaggle-ds-bowl-2018-baseline
-Full train/inference/submission pipeline adapted to the competition from https://github.com/matterport/Mask_RCNN
+
+Full train/inference/submission pipeline adapted to the competition from https://github.com/matterport/Mask_RCNN. Kudos to @matterport, @waleedka and others for the code. It is well written, but is also somewhat opinionated, which makes it harder to guess what's going on under the hood. And hat's the main reason for my fork to exist.
+
+I did almost no changes to the original code, except for:
+
+* Everything custom in `bowl_config.py`.
+* `VALIDATION_STEPS` and `STEPS_PER_EPOCH` are now forced to depend on the dataset size.
+
+## Quick Start
+
+1. First, you have to download the train masks. Thanks [@lopuhin](https://github.com/lopuhin/) for bringing all the fixes to one place. You might want to do it outside of this repo to be able to pull changes later and use symlinks:
+
+```bash
+git clone https://github.com/lopuhin/kaggle-dsbowl-2018-dataset-fixes ../kaggle-dsbowl-2018-dataset-fixes
+ln -s stage1_train ../kaggle-dsbowl-2018-dataset-fixes/stage1_train
+```
+
+2. Download the rest of the official dataset and unzip it to the repo:
+
+```bash
+unzip ~/Downloads/stage1_test.zip .
+unzip ~/Downloads/stage1_train_labels.csv.zip .
+unzip ~/Downloads/stage1_sample_submission.csv.zip .
+```
+
+3. Install `pycocotools` and COCO pretrained weights (`mask_rcnn_coco.h5`). General idea is described [here](https://github.com/matterport/Mask_RCNN#installation). Keep in mind, to install pycocotools properly, it's better to run `make install` instead of `make`.
+
+4. For a single GPU training, run:
+
+```
+CUDA_VISIBLE_DEVICES="0" python train.py
+```
+
+5. To generate a submission, run:
+
+```
+CUDA_VISIBLE_DEVICES="0" python inference.py
+```
+
+This will create `submission.csv` in the repo and overwrite the old one (you're welcome to fix this with a PR).
+
+6. Submit!