Update README.md

README.md

@@ -14,6 +14,45 @@ This is an evolving dataset, where we will release more data as the dataset grow
 MMdetection is an open source object detection toolbox based on PyTorch, with a large Model Zoo with many customised models that can be plugged and tested in with just a single config file modification. PYou can read more about it at: [mmdetection github](https://github.com/open-mmlab/mmdetection/)
 
 Follow the installation instructions as given in the above link.
+# Installation
+
+Ensure you have `docker` and `nvidia-docker` installed by following the instructions here : 
+
+* [Docker](https://docs.docker.com/install/)
+* [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)
+**NOTE** : You do not need nvidia-docker if you do not want to use GPU when testing your submission locally
+
+[MMDetection Installation instructions](https://github.com/open-mmlab/mmdetection/blob/master/docs/INSTALL.md)
+# Dataset
+
+The dataset for the [AIcrowd Food Recognition Challenge](https://www.aicrowd.com/challenges/food-recognition-challenge) is available at [https://www.aicrowd.com/challenges/food-recognition-challenge/dataset_files](https://www.aicrowd.com/challenges/food-recognition-challenge/dataset_files)
+
+This dataset contains :   
+* `train-v0.2.tar.gz` : This is the Training Set of **7949** (as RGB images) food images, along with their corresponding annotations in [MS-COCO format](http://cocodataset.org/#home)
+
+* `val-v0.2.tar.gz`: This is the suggested Validation Set of **418** (as RGB images) food images, along with their corresponding annotations in [MS-COCO format](http://cocodataset.org/#home)
+
+* `test_images-v0.2.tar.gz` : This is the debug Test Set for Round-1, where you are provided the same images as the validation set.
+
+To get started, we would advise you to download all the files, and untar them inside the `data/` folder of this repository, so that you have a directory structure like this : 
+
+```bash
+|-- data/
+|   |-- test_images/ (has all images for prediction)(**NOTE** : They are the same as the validation set images)
+|   |-- train/
+|   |   |-- images (has all the images for training)
+|   |   |__ annotation.json : Annotation of the data in MS COCO format
+|   |   |__ annotation-small.json : Smaller version of the previous dataset
+|   |-- val/
+|   |   |-- images (has all the images for training)
+|   |   |__ annotation.json : Annotation of the data in MS COCO format
+|   |   |__ annotation-small.json : Smaller version of the previous dataset
+```
+
+We are also assuming that you have already installed all the requirements for this notebook, or you can still install them by :
+
+# Usage
+
 # Training with MMDetection:
 Let us look at training MMDetection using Hybrid Task Cascade [HTC research paper](https://arxiv.org/abs/1901.07518).
 
@@ -70,52 +109,7 @@ plot_curve([os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json'], args
 ```
 
 
-# Installation
-
-Ensure you have `docker` and `nvidia-docker` installed by following the instructions here : 
-
-* [Docker](https://docs.docker.com/install/)
-* [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)
-**NOTE** : You do not need nvidia-docker if you do not want to use GPU when testing your submission locally
-
-```
-git clone http://gitlab.aicrowd.com/nikhil_rayaprolu/food-round2.git
-cd food-round2
-pip3 install -r requirements.txt
-pip3 install git+https://github.com/AIcrowd/coco.git
-```
-
-# Dataset
-
-The dataset for the [AIcrowd Food Recognition Challenge](https://www.aicrowd.com/challenges/food-recognition-challenge) is available at [https://www.aicrowd.com/challenges/food-recognition-challenge/dataset_files](https://www.aicrowd.com/challenges/food-recognition-challenge/dataset_files)
-
-This dataset contains :   
-* `train-v0.2.tar.gz` : This is the Training Set of **7949** (as RGB images) food images, along with their corresponding annotations in [MS-COCO format](http://cocodataset.org/#home)
-
-* `val-v0.2.tar.gz`: This is the suggested Validation Set of **418** (as RGB images) food images, along with their corresponding annotations in [MS-COCO format](http://cocodataset.org/#home)
-
-* `test_images-v0.2.tar.gz` : This is the debug Test Set for Round-1, where you are provided the same images as the validation set.
-
-To get started, we would advise you to download all the files, and untar them inside the `data/` folder of this repository, so that you have a directory structure like this : 
-
-```bash
-|-- data/
-|   |-- test_images/ (has all images for prediction)(**NOTE** : They are the same as the validation set images)
-|   |-- train/
-|   |   |-- images (has all the images for training)
-|   |   |__ annotation.json : Annotation of the data in MS COCO format
-|   |   |__ annotation-small.json : Smaller version of the previous dataset
-|   |-- val/
-|   |   |-- images (has all the images for training)
-|   |   |__ annotation.json : Annotation of the data in MS COCO format
-|   |   |__ annotation-small.json : Smaller version of the previous dataset
-```
-
-We are also assuming that you have already installed all the requirements for this notebook, or you can still install them by :
-
-# Usage
 
-**TODO** : @Nikhil : Please add some Usage instructions for people to use this codebase locally. 
 
 ## Other Associated Notebooks
 
@@ -279,7 +273,7 @@ git push aicrowd submission-v0.1
 
 ## Credits
 * Parts of the documentation for this baseline was taken from : https://github.com/AIcrowd/food-recognition-challenge-starter-kit
-* **TODO** @Nikhil : Please add relevant credits for the mmdetection codebases that you used as a reference.
+* and the baseline is built using MMDetection: https://github.com/open-mmlab/mmdetection/
 
 # Author   
 **[Nikhil Rayaprolu](nikhil@aicrowd.com)**