.gitattributes

+*.pth filter=lfs diff=lfs merge=lfs -text

.github/CODE_OF_CONDUCT.md

+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+ advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+ address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+ professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at chenkaidev@gmail.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md → .github/CONTRIBUTING.md

@@ -21,10 +21,33 @@ please contact Kai Chen (chenkaidev[at]gmail[dot]com). We will much appreciate y
 
 ### Python
 We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
-We use [flake8](http://flake8.pycqa.org/en/latest/) as the linter and [yapf](https://github.com/google/yapf) as the formatter.
-Please upgrade to the latest yapf (>=0.27.0) and refer to the [configuration](.style.yapf).
+
+We use the following tools for linting and formatting:
+- [flake8](http://flake8.pycqa.org/en/latest/): linter
+- [yapf](https://github.com/google/yapf): formatter
+- [isort](https://github.com/timothycrosley/isort): sort imports
+
+Style configurations of yapf and isort can be found in [.style.yapf](../.style.yapf) and [.isort.cfg](../.isort.cfg).
+
+We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`,
+ fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.
+The config for a pre-commit hook is stored in [.pre-commit-config](../.pre-commit-config.yaml).
+
+After you clone the repository, you will need to install initialize pre-commit hook.
+
+```
+pip install -U pre-commit
+```
+
+From the repository folder
+```
+pre-commit install
+```
+
+After this on every commit check code linters and formatter will be enforced.
+
 
 >Before you create a PR, make sure that your code lints and is formatted by yapf.
 
 ### C++ and CUDA
-We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
\ No newline at end of file
+We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).

.github/ISSUE_TEMPLATE/config.yml

+blank_issues_enabled: false

.github/ISSUE_TEMPLATE/error-report.md

+---
+name: Error report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+Thanks for your error report and we appreciate it a lot.
+
+**Checklist**
+1. I have searched related issues but cannot get the expected help.
+2. The bug has not been fixed in the latest version.
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Reproduction**
+1. What command or script did you run?
+```
+A placeholder for the command.
+```
+2. Did you make any modifications on the code or config? Did you understand what you have modified?
+3. What dataset did you use?
+
+**Environment**
+
+1. Please run `python tools/collect_env.py` to collect necessary environment infomation and paste it here.
+2. You may add addition that may be helpful for locating the problem, such as
+    - How you installed PyTorch [e.g., pip, conda, source]
+    - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+
+**Error traceback**
+If applicable, paste the error trackback here.
+```
+A placeholder for trackback.
+```
+
+**Bug fix**
+If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!

.github/ISSUE_TEMPLATE/feature_request.md

+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the feature**
+
+**Motivation**
+A clear and concise description of the motivation of the feature.
+Ex1. It is inconvenient when [....].
+Ex2. There is a recent paper [....], which is very helpful for [....].
+
+**Related resources**
+If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
+If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated.

.github/ISSUE_TEMPLATE/general_questions.md

+---
+name: General questions
+about: Ask general questions to get help
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+

.gitignore

@@ -115,3 +115,4 @@ data
 *.pkl.json
 *.log.json
 work_dirs/
+

.isort.cfg

+[isort]
+line_length = 79
+multi_line_output = 0
+known_standard_library = setuptools
+known_first_party = mmdet
+known_third_party = Cython,asynctest,cv2,matplotlib,mmcv,numpy,pycocotools,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY

.pre-commit-config.yaml

+repos:
+- repo: https://github.com/asottile/seed-isort-config
+  rev: v1.9.3
+  hooks:
+      - id: seed-isort-config
+- repo: https://github.com/pre-commit/mirrors-isort
+  rev: v4.3.21
+  hooks:
+      - id: isort
+- repo: https://github.com/pre-commit/mirrors-yapf
+  rev: v0.29.0
+  hooks:
+    - id: yapf
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v2.4.0
+  hooks:
+    - id: flake8
+    - id: trailing-whitespace
+    - id: check-yaml
+    - id: end-of-file-fixer
+    - id: requirements-txt-fixer

.travis.yml

-dist: xenial
+dist: bionic  # ubuntu 18.04
 language: python
 
-install:
-  - pip install flake8
-
 python:
   - "3.5"
   - "3.6"
   - "3.7"
 
+env: CUDA=10.1.105-1 CUDA_SHORT=10.1 UBUNTU_VERSION=ubuntu1804 FORCE_CUDA=1
+cache: pip
+
+# Ref to CUDA installation in Travis: https://github.com/jeremad/cuda-travis
+before_install:
+  - INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb
+  - wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER}
+  - sudo dpkg -i ${INSTALLER}
+  - wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub
+  - sudo apt-key add 7fa2af80.pub
+  - sudo apt update -qq
+  - sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-}
+  - sudo apt clean
+  - CUDA_HOME=/usr/local/cuda-${CUDA_SHORT}
+  - LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH}
+  - PATH=${CUDA_HOME}/bin:${PATH}
+
+install:
+  - pip install Pillow==6.2.2  # remove this line when torchvision>=0.5
+  - pip install Cython torch==1.2 torchvision==0.4.0  # TODO: fix CI for pytorch>1.2
+  - pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
+  - pip install -r requirements.txt
+
+before_script:
+  - flake8 .
+  - isort -rc --check-only --diff mmdet/ tools/ tests/
+  - yapf -r -d --style .style.yapf mmdet/ tools/ tests/ configs/
+
 script:
-  - flake8
+  - python setup.py check -m -s
+  - python setup.py build_ext --inplace
+  - coverage run --source mmdet -m py.test -v --xdoctest-modules tests mmdet
+
+after_success:
+  - coverage report

Dockerfile

+ARG PYTORCH="1.1.0"
+ARG CUDA="10.0"
+ARG CUDNN="7.5"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+RUN apt-get update && apt-get install -y libglib2.0-0 libsm6 libxrender-dev libxext6 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    bzip2 \
+    cmake \
+    curl \
+    git \
+    g++ \
+    libboost-all-dev \
+    pkg-config \
+    rsync \
+    software-properties-common \
+    sudo \
+    tar \
+    timidity \
+    unzip \
+    wget \
+    locales \
+    zlib1g-dev \
+    python3-dev \
+    python3 \
+    python3-pip \
+    python3-tk \
+    libjpeg-dev \
+    libpng-dev
+
+# Python3
+RUN pip3 install pip --upgrade
+RUN pip3 install utm cython aicrowd_api timeout_decorator \
+  numpy \
+  aicrowd-repo2docker \
+  pillow
+RUN pip3 install git+https://github.com/AIcrowd/coco.git#subdirectory=PythonAPI
+	
+RUN conda install cython -y && conda clean --all
+RUN git clone --branch v1.0rc1 https://github.com/open-mmlab/mmdetection.git /mmdetection
+WORKDIR /mmdetection
+RUN pip install --no-cache-dir -e .
+RUN python3.6 -m pip install aicrowd_api   aicrowd-repo2docker
+# Unicode support:
+RUN locale-gen en_US.UTF-8
+ENV LANG en_US.UTF-8
+ENV LANGUAGE en_US:en
+ENV LC_ALL en_US.UTF-8
+
+# Enables X11 sharing and creates user home directory
+ENV USER_NAME aicrowd
+ENV HOME_DIR /home/$USER_NAME
+#
+# Replace HOST_UID/HOST_GUID with your user / group id (needed for X11)
+ENV HOST_UID 1000
+ENV HOST_GID 1000
+
+RUN export uid=${HOST_UID} gid=${HOST_GID} && \
+    mkdir -p ${HOME_DIR} && \
+    echo "$USER_NAME:x:${uid}:${gid}:$USER_NAME,,,:$HOME_DIR:/bin/bash" >> /etc/passwd && \
+    echo "$USER_NAME:x:${uid}:" >> /etc/group && \
+    echo "$USER_NAME ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/$USER_NAME && \
+    chmod 0440 /etc/sudoers.d/$USER_NAME && \
+    chown ${uid}:${gid} -R ${HOME_DIR}
+
+USER ${USER_NAME}
+WORKDIR ${HOME_DIR}
+
+COPY . .
+
+RUN sudo chown ${HOST_UID}:${HOST_GID} -R *
+RUN sudo chmod 775 -R *
+
+

INSTALL.md

-## Installation
-
-### Requirements
-
-- Linux
-- Python 3.5+ ([Say goodbye to Python2](https://python3statement.org/))
-- PyTorch 1.0+ or PyTorch-nightly
-- CUDA 9.0+
-- NCCL 2+
-- GCC 4.9+
-- [mmcv](https://github.com/open-mmlab/mmcv)
-
-We have tested the following versions of OS and softwares:
-
-- OS: Ubuntu 16.04/18.04 and CentOS 7.2
-- CUDA: 9.0/9.2/10.0
-- NCCL: 2.1.15/2.2.13/2.3.7/2.4.2
-- GCC: 4.9/5.3/5.4/7.3
-
-### Install mmdetection
-
-a. Create a conda virtual environment and activate it. Then install Cython.
-
-```shell
-conda create -n open-mmlab python=3.7 -y
-source activate open-mmlab
-
-conda install cython
-```
-
-b. Install PyTorch stable or nightly and torchvision following the [official instructions](https://pytorch.org/).
-
-c. Clone the mmdetection repository.
-
-```shell
-git clone https://github.com/open-mmlab/mmdetection.git
-cd mmdetection
-```
-
-d. Compile cuda extensions.
-
-```shell
-./compile.sh
-```
-
-e. Install mmdetection (other dependencies will be installed automatically).
-
-```shell
-python setup.py develop
-# or "pip install -e ."
-```
-
-Note:
-
-1. It is recommended that you run the step e each time you pull some updates from github. If there are some updates of the C/CUDA codes, you also need to run step d.
-The git commit id will be written to the version number with step e, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
-
-2. Following the above instructions, mmdetection is installed on `dev` mode, any modifications to the code will take effect without installing it again.
-
-### Prepare COCO dataset.
-
-It is recommended to symlink the dataset root to `$MMDETECTION/data`.
-
-```
-mmdetection
-├── mmdet
-├── tools
-├── configs
-├── data
-│   ├── coco
-│   │   ├── annotations
-│   │   ├── train2017
-│   │   ├── val2017
-│   │   ├── test2017
-│   ├── VOCdevkit
-│   │   ├── VOC2007
-│   │   ├── VOC2012
-
-```
-
-### Scripts
-[Here](https://gist.github.com/hellock/bf23cd7348c727d69d48682cb6909047) is
-a script for setting up mmdetection with conda.
-
-### Notice
-You can run `python(3) setup.py develop` or `pip install -e .` to install mmdetection if you want to make modifications to it frequently.
-
-If there are more than one mmdetection on your machine, and you want to use them alternatively.
-Please insert the following code to the main file
-```python
-import os.path as osp
-import sys
-sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
-```
-or run the following command in the terminal of corresponding folder.
-```shell
-export PYTHONPATH=`pwd`:$PYTHONPATH
-```

LICENSE

+Copyright 2018-2019 Open-MMLab. All rights reserved.
+
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
@@ -186,7 +188,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
+   Copyright 2018-2019 Open-MMLab.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

README.md

+# food-recognition-challenge-mmdetection-baseline
+![AIcrowd-Logo](https://raw.githubusercontent.com/AIcrowd/AIcrowd/master/app/assets/images/misc/aicrowd-horizontal.png)
 
-# mmdetection
+# Problem Statement
 
-## Introduction
+The goal of this challenge is to train models which can look at images of food items and detect the individual food items present in them.
+We provide a novel dataset of food images collected using the MyFoodRepo project where numerous volunteer Swiss users provide images of their daily food intake. The images have been hand labelled by a group of experts to map the individual food items to an ontology of Swiss Food items.
 
-The master branch works with **PyTorch 1.1** or higher. If you would like to use PyTorch 0.4.1,
-please checkout to the [pytorch-0.4.1](https://github.com/open-mmlab/mmdetection/tree/pytorch-0.4.1) branch.
+This is an evolving dataset, where we will release more data as the dataset grows in size.
 
-mmdetection is an open source object detection toolbox based on PyTorch. It is
-a part of the open-mmlab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).
+![image1](https://i.imgur.com/zS2Nbf0.png)
 
-![demo image](demo/coco_test_12510.jpg)
+# Baseline
+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/)
 
-### Major features
+Follow the installation instructions as given in the above link.
+# Installation
 
-- **Modular Design**
+Ensure you have `docker` and `nvidia-docker` installed by following the instructions here : 
 
-  One can easily construct a customized object detection framework by combining different components.
+* [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
 
-- **Support of multiple frameworks out of box**
+[MMDetection Installation instructions](https://github.com/open-mmlab/mmdetection/blob/master/docs/INSTALL.md)
+# Dataset
 
-  The toolbox directly supports popular detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc.
+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)
 
-- **Efficient**
+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)
 
-  All basic bbox and mask operations run on GPUs now.
-  The training speed is nearly 2x faster than Detectron and comparable to maskrcnn-benchmark.
+* `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)
 
-- **State of the art**
+* `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.
 
-  This was the codebase of the *MMDet* team, who won the [COCO Detection 2018 challenge](http://cocodataset.org/#detection-leaderboard).
+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 : 
 
-Apart from mmdetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research,
-which is heavily depended on by this toolbox.
+```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
+```
 
-## License
+We are also assuming that you have already installed all the requirements for this notebook, or you can still install them by :
 
-This project is released under the [Apache 2.0 license](LICENSE).
+# Usage
 
-## Updates
+# Training with MMDetection:
+Let us look at training MMDetection using Hybrid Task Cascade [HTC research paper](https://arxiv.org/abs/1901.07518).
 
-v0.6.0 (14/04/2019)
-- Up to 30% speedup compared to the model zoo.
-- Support both PyTorch stable and nightly version.
-- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.
+A score of AP_50 of 0.526	and AR_50 of 0.729, can be achieved with Hybrid Task Cascade of Resnet50 Backbone.
 
-v0.6rc0(06/02/2019)
-- Migrate to PyTorch 1.0.
+MMDetection provides us with a config file especially for HTC, available at [HTC config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc)
 
-v0.5.7 (06/02/2019)
-- Add support for Deformable ConvNet v2. (Many thanks to the authors and [@chengdazhi](https://github.com/chengdazhi))
-- This is the last release based on PyTorch 0.4.1.
+Also make sure you have downloaded the training data to a subfolder of your project.
 
-v0.5.6 (17/01/2019)
-- Add support for Group Normalization.
-- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.
+Modify your config file and point your dataset variables to your data folder.
 
-v0.5.5 (22/12/2018)
-- Add SSD for COCO and PASCAL VOC.
-- Add ResNeXt backbones and detection models.
-- Refactoring for Samplers/Assigners and add OHEM.
-- Add VOC dataset and evaluation scripts.
+As given in [MMDetection Getting Started](https://github.com/open-mmlab/mmdetection/blob/master/docs/GETTING_STARTED.md),
+You can use:
 
-v0.5.4 (27/11/2018)
-- Add SingleStageDetector and RetinaNet.
+    python tools/train.py ${CONFIG_FILE}
+to train the model on a single GPU or
 
-v0.5.3 (26/11/2018)
-- Add Cascade R-CNN and Cascade Mask R-CNN.
-- Add support for Soft-NMS in config files.
+    ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
+to train the model on multiple GPUs.
 
-v0.5.2 (21/10/2018)
-- Add support for custom datasets.
-- Add a script to convert PASCAL VOC annotations to the expected format.
+Make sure you have edited the config file to point to the dataset and also have made changes to the number of classes.
+If you are going to use the dataloader from the mmdetection.
 
-v0.5.1 (20/10/2018)
-- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured.
-- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency.
+## Testing with MMDetection:
+To test your results with MMDetection, 
+you can use the commands:
+```
+*single-gpu testing*
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
 
-## Benchmark and model zoo
+*multi-gpu testing*
+./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
+```
 
-Supported methods and backbones are shown in the below table.
-Results and models are available in the [Model zoo](MODEL_ZOO.md).
+**Log Analysis**
 
-|                    | ResNet   | ResNeXt  | SENet    | VGG      |
-|--------------------|:--------:|:--------:|:--------:|:--------:|
-| RPN                | ✓        | ✓        | ☐        | ✗        |
-| Fast R-CNN         | ✓        | ✓        | ☐        | ✗        |
-| Faster R-CNN       | ✓        | ✓        | ☐        | ✗        |
-| Mask R-CNN         | ✓        | ✓        | ☐        | ✗        |
-| Cascade R-CNN      | ✓        | ✓        | ☐        | ✗        |
-| Cascade Mask R-CNN | ✓        | ✓        | ☐        | ✗        |
-| SSD                | ✗        | ✗        | ✗        | ✓        |
-| RetinaNet          | ✓        | ✓        | ☐        | ✗        |
-| Hybrid Task Cascade| ✓        | ✓        | ☐        | ✗        |
-| FCOS               | ✓        | ✓        | ☐        | ✗        |
-| Libra R-CNN        | ✓        | ✓        | ☐        | ✗        |
+The training logs can be analyzed using the plot_curve provided with the mmdetection:
 
-Other features
-- [x] DCNv2
-- [x] Group Normalization
-- [x] Weight Standardization
-- [x] OHEM
-- [x] Soft-NMS
-- [ ] Mixed Precision (FP16) Training (coming soon)
+```
+import os
+import matplotlib
+%matplotlib inline
+from tools.analyze_logs import plot_curve
+matplotlib.rcParams['figure.figsize'] = [20, 10]
+args = {
+    'keys':['segm_mAP_50'],
+    'legend':'segm_mAP_50',
+    'backend': None,
+    'json_logs': [os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json'],
+    'title': 'loss'
+}
+print(os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json')
+plot_curve([os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json'], args)
+```
 
 
-## Installation
 
-Please refer to [INSTALL.md](INSTALL.md) for installation and dataset preparation.
 
+## Other Associated Notebooks
 
-## Get Started
+* [Dataset Utils](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb)
+  * [Import Dependencies](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Import-dependencies)
+  * [Configuration Variables](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Configuration-Variables)
+  * [Parsing Annotations](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Parsing-the-annotations)
+  * [Collecting and Visualizing Images](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Collecting-and-Visualizing-Images)
+  * [Understanding Annotations](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Understanding-Annotations)
+  * [Visualizing Annotations](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Visualizing-Annotations)
+  * [Advanced](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#Advanced)
+    * [Convert poly segmentation to rle](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#1.-Convert-poly-segmentation-to-rle)
+    * [Convert segmentation to pixel level masks](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Dataset%20Utils.ipynb#2.-Convert-segmentation-to-pixel-level-masks)
+* [Random Submission](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/run.py)
+* [Locally test the evaluation function](https://github.com/AIcrowd/food-recognition-challenge-starter-kit/blob/master/Local%20Evaluation.ipynb)   
 
-Please see [GETTING_STARTED.md](GETTING_STARTED.md) for the basic usage of mmdetection.
+## Other Baselines
+# Round 1
+* [Colab Notebook for Data Analysis and Tutorial](https://colab.research.google.com/drive/1A5p9GX5X3n6OMtLjfhnH6Oeq13tWNtFO#scrollTo=ok54AWT_VoWV)
+A notebook with data analysis on the Food Recognition Dataset and then a short tutorial on training with keras and pytorch. This lets you immediately jump onto the challenge and solve the challenge
+### Pretrained Baselines
+* [mmdetection (pytorch)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-pytorch-baseline)  
+* [matterport-maskrcnn (keras - tensorflow)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-recognition)
 
-## Contributing
+# Round 2
+* [Colab Notebook for Data Analysis and Tutorial](https://colab.research.google.com/drive/1vXdv9quZ7CXO5lLCjhyz3jtejRzDq221)
+A notebook with data analysis on the Food Recognition Dataset and then a short tutorial on training with keras and pytorch. This lets you immediately jump onto the challenge and solve the challenge
+### Pretrained Baselines
+* [mmdetection (pytorch)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-round2)  
 
-We appreciate all contributions to improve mmdetection. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
+# Submission Instructions
 
+To submit to the challenge you'll need to ensure you've set up an appropriate repository structure, create a private git repository at https://gitlab.aicrowd.com with the contents of your submission, and push a git tag corresponding to the version of your repository you'd like to submit.
 
-## Citation
+## Repository Structure
+We have created this sample submission repository which you can use as reference.
 
-If you use our codebase or models in your research, please cite this project.
-We will release a paper or technical report later.
+#### aicrowd.json
+Each repository should have a aicrowd.json file with the following fields:
 
 ```
-@misc{mmdetection2018,
-  author =       {Kai Chen and Jiangmiao Pang and Jiaqi Wang and Yu Xiong and Xiaoxiao Li
-                  and Shuyang Sun and Wansen Feng and Ziwei Liu and Jianping Shi and
-                  Wanli Ouyang and Chen Change Loy and Dahua Lin},
-  title =        {mmdetection},
-  howpublished = {\url{https://github.com/open-mmlab/mmdetection}},
-  year =         {2018}
+{
+    "challenge_id" : "aicrowd-food-recognition-challenge",
+    "grader_id": "aicrowd-food-recognition-challenge",
+    "authors" : ["aicrowd-user"],
+    "description" : "Food Recognition Challenge Submission",
+    "license" : "MIT",
+    "gpu": true
 }
 ```
+This file is used to identify your submission as a part of the Food Recognition Challenge.  You must use the `challenge_id` and `grader_id` specified above in the submission. The `gpu` key in the `aicrowd.json` lets your specify if your submission requires a GPU or not. In which case, a NVIDIA-K80 will be made available to your submission when evaluation the submission.
+
+#### Submission environment configuration
+You can specify the software runtime of your code by modifying the included [Dockerfile](Dockerfile). 
+
+#### Code Entrypoint
+The evaluator will use `/home/aicrowd/run.sh` as the entrypoint. Please remember to have a `run.sh` at the root which can instantiate any necessary environment variables and execute your code. This repository includes a sample `run.sh` file.
+
+### Local Debug
+
+```
+export TEST_IMAGES_PATH="../data/test_images"  # or path to your local folder containing images
+export IMAGE_NAME="aicrowd-food-recognition-challenge-submission"
+
+./build.sh
+./debug.sh
+
+######################################
+## NOTE : 
+## 
+## * If you do not wish to your a GPU when testing locally, please feel free to replace nvidia-docker with docker
+##
+## * If you want to test on images located at an alternate location, set the `TEST_IMAGES_PATH` environment variable accordingly before running `build.sh` and `debug.sh`.
+```
+
+### Submitting 
+To make a submission, you will have to create a private repository on [https://gitlab.aicrowd.com](https://gitlab.aicrowd.com).
+
+You will have to add your SSH Keys to your GitLab account by following the instructions [here](https://docs.gitlab.com/ee/gitlab-basics/create-your-ssh-keys.html).
+If you do not have SSH Keys, you will first need to [generate one](https://docs.gitlab.com/ee/ssh/README.html#generating-a-new-ssh-key-pair).
+
+Then you can create a submission by making a *tag push* to your repository, adding the correct git remote and pushing to the remote:
+
+```
+git clone https://gitlab.aicrowd.com/nikhil_rayaprolu/food-round2
+cd food-round2
+
+# Add AICrowd git remote endpoint
+git remote add aicrowd git@gitlab.aicrowd.com:<YOUR_AICROWD_USER_NAME>/food-challenge-pytorch-baseline.git
+git push aicrowd master
+
+# Create a tag for your submission and push
+git tag -am "submission-v0.1" submission-v0.1
+git push aicrowd master
+git push aicrowd submission-v0.1
+
+# Note : If the contents of your repository (latest commit hash) does not change, 
+# then pushing a new tag will not trigger a new evaluation.
+```
+You now should be able to see the details of your submission at : 
+[gitlab.aicrowd.com/<YOUR_AICROWD_USER_NAME>/food-challenge-pytorch-baseline/issues](gitlab.aicrowd.com/<YOUR_AICROWD_USER_NAME>/food-challenge-pytorch-baseline/issues)
+
+
+## Using http instead of ssh (Personal Access Token):
+
+In order to use http to clone repositories and submit on gitlab:
+
+a) Create a personal access token
+
+1.  Log in to GitLab.
+    
+2.  In the upper-right corner, click your avatar and select Settings.
+    
+3.  On the User Settings menu, select Access Tokens.
+    
+4.  Choose a name and optional expiry date for the token.
+    
+5.  Choose the desired scopes.
+    
+6.  Click the Create personal access token button.
+    
+7.  Save the personal access token somewhere safe, lets call it XXX for now.
+    
+
+Once you leave or refresh the page, you won’t be able to access it again.
+
+b) to clone a repo use the following command:
+
+git clone [https://oauth2:XXX@gitlab.aicrowd.com/(username)/(repo_name).git](https://oauth2:XXX@gitlab.aicrowd.com/(username)/(repo_name).git)
+
+c)submit a solution:
+```
+cd into your submission repo on gitlab
+
+cd (repo_name)
+
+#Add AICrowd git remote endpoint
+
+git remote add aicrowd https://oauth2:XXX@gitlab.aicrowd.com/(username)/(repo_name).git
+
+git push aicrowd master
+
+# Create a tag for your submission and push
+
+git tag -am "submission-v0.1" submission-v0.1
+
+git push aicrowd master
+
+git push aicrowd submission-v0.1
+
+# Note : If the contents of your repository (latest commit hash) does not change,
+
+# then pushing a new tag will not trigger a new evaluation.
+```
+
+
+**Best of Luck**
+
+## Miscelaneous Resources
+* [Convert Annotations from MS COCO format to PascalVOC format](https://github.com/CasiaFan/Dataset_to_VOC_converter/blob/master/anno_coco2voc.py)
+
+## Credits
+* Parts of the documentation for this baseline was taken from : https://github.com/AIcrowd/food-recognition-challenge-starter-kit
+* and the baseline is built using MMDetection: https://github.com/open-mmlab/mmdetection/
+
+# Author   
+**[Nikhil Rayaprolu](nikhil@aicrowd.com)**
+

aicrowd.json

+{
+    "challenge_id" : "aicrowd-food-recognition-challenge",
+    "grader_id": "aicrowd-food-recognition-challenge",
+    "authors" : ["nikhil13prs"],
+    "description" : "Food Recognition Challenge Submission",
+    "license" : "MIT",
+    "gpu": true
+}
+

aicrowd_helpers.py

+#!/usr/bin/env python
+import aicrowd_api
+import os
+
+########################################################################
+# Instatiate Event Notifier
+########################################################################
+aicrowd_events = aicrowd_api.events.AIcrowdEvents()
+
+
+def execution_start():
+    ########################################################################
+    # Register Evaluation Start event
+    ########################################################################
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_INFO,
+                message="execution_started",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_started"
+                    }
+                )
+
+
+def execution_progress(progress_payload):
+    image_ids = progress_payload["image_ids"]
+    ########################################################################
+    # Register Evaluation Progress event
+    ########################################################################
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_INFO,
+                message="execution_progress",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_progress",
+                    "image_ids" : image_ids
+                    }
+                )
+
+def execution_success(payload):
+    predictions_output_path = payload["predictions_output_path"]
+    ########################################################################
+    # Register Evaluation Complete event
+    ########################################################################
+    expected_output_path = os.getenv("AICROWD_PREDICTIONS_OUTPUT_PATH", False)
+    if expected_output_path != predictions_output_path:
+        raise Exception("Please write the output to the path specified in the environment variable : AICROWD_PREDICTIONS_OUTPUT_PATH instead of {}".format(predictions_output_path))
+
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_SUCCESS,
+                message="execution_success",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_success",
+                    "predictions_output_path" : predictions_output_path
+                    },
+                blocking=True
+                )
+
+def execution_error(error):
+    ########################################################################
+    # Register Evaluation Complete event
+    ########################################################################
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_ERROR,
+                message="execution_error",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_error",
+                    "error" : error
+                    },
+                blocking=True
+                )
+

all_categories.json

+[
+    {
+      "id": 2578,
+      "name": "water",
+      "name_readable": "Water",
+      "supercategory": "food"
+    },
+    {
+      "id": 2939,
+      "name": "pizza-margherita-baked",
+      "name_readable": "Pizza, Margherita, baked",
+      "supercategory": "food"
+    },
+    {
+      "id": 1085,
+      "name": "broccoli",
+      "name_readable": "Broccoli",
+      "supercategory": "food"
+    },
+    {
+      "id": 1040,
+      "name": "salad-leaf-salad-green",
+      "name_readable": "Salad, leaf / salad, green",
+      "supercategory": "food"
+    },
+    {
+      "id": 1070,
+      "name": "zucchini",
+      "name_readable": "Zucchini",
+      "supercategory": "food"
+    },
+    {
+      "id": 2022,
+      "name": "egg",
+      "name_readable": "Egg",
+      "supercategory": "food"
+    },
+    {
+      "id": 2053,
+      "name": "butter",
+      "name_readable": "Butter",
+      "supercategory": "food"
+    },
+    {
+      "id": 1566,
+      "name": "bread-white",
+      "name_readable": "Bread, white",
+      "supercategory": "food"
+    },
+    {
+      "id": 1151,
+      "name": "apple",
+      "name_readable": "Apple",
+      "supercategory": "food"
+    },
+    {
+      "id": 2131,
+      "name": "dark-chocolate",
+      "name_readable": "Dark chocolate",
+      "supercategory": "food"
+    },
+    {
+      "id": 2521,
+      "name": "white-coffee-with-caffeine",
+      "name_readable": "White coffee, with caffeine",
+      "supercategory": "food"
+    },
+    {
+      "id": 1068,
+      "name": "sweet-pepper",
+      "name_readable": "Sweet pepper",
+      "supercategory": "food"
+    },
+    {
+      "id": 1026,
+      "name": "mixed-salad-chopped-without-sauce",
+      "name_readable": "Mixed salad (chopped without sauce)",
+      "supercategory": "food"
+    },
+    {
+      "id": 2738,
+      "name": "tomato-sauce",
+      "name_readable": "Tomato sauce",
+      "supercategory": "food"
+    },
+    {
+      "id": 1565,
+      "name": "bread-wholemeal",
+      "name_readable": "Bread, wholemeal",
+      "supercategory": "food"
+    },
+    {
+      "id": 2512,
+      "name": "coffee-with-caffeine",
+      "name_readable": "Coffee, with caffeine",
+      "supercategory": "food"
+    },
+    {
+      "id": 1061,
+      "name": "cucumber",
+      "name_readable": "Cucumber",
+      "supercategory": "food"
+    },
+    {
+      "id": 1311,
+      "name": "cheese",
+      "name_readable": "Cheese",
+      "supercategory": "food"
+    },
+    {
+      "id": 1505,
+      "name": "pasta-spaghetti",
+      "name_readable": "Pasta, spaghetti",
+      "supercategory": "food"
+    },
+    {
+      "id": 1468,
+      "name": "rice",
+      "name_readable": "Rice",
+      "supercategory": "food"
+    },
+    {
+      "id": 1967,
+      "name": "salmon",
+      "name_readable": "Salmon",
+      "supercategory": "food"
+    },
+    {
+      "id": 1078,
+      "name": "carrot",
+      "name_readable": "Carrot",
+      "supercategory": "food"
+    },
+    {
+      "id": 1116,
+      "name": "onion",
+      "name_readable": "Onion",
+      "supercategory": "food"
+    },
+    {
+      "id": 1022,
+      "name": "mixed-vegetables",
+      "name_readable": "Mixed vegetables",
+      "supercategory": "food"
+    },
+    {
+      "id": 2504,
+      "name": "espresso-with-caffeine",
+      "name_readable": "Espresso, with caffeine",
+      "supercategory": "food"
+    },
+    {
+      "id": 1154,
+      "name": "banana",
+      "name_readable": "Banana",
+      "supercategory": "food"
+    },
+    {
+      "id": 1163,
+      "name": "strawberries",
+      "name_readable": "Strawberries",
+      "supercategory": "food"
+    },
+    {
+      "id": 2750,
+      "name": "mayonnaise",
+      "name_readable": "Mayonnaise",
+      "supercategory": "food"
+    },
+    {
+      "id": 1210,
+      "name": "almonds",
+      "name_readable": "Almonds",
+      "supercategory": "food"
+    },
+    {
+      "id": 2620,
+      "name": "wine-white",
+      "name_readable": "Wine, white",
+      "supercategory": "food"
+    },
+    {
+      "id": 1310,
+      "name": "hard-cheese",
+      "name_readable": "Hard cheese",
+      "supercategory": "food"
+    },
+    {
+      "id": 1893,
+      "name": "ham-raw",
+      "name_readable": "Ham, raw",
+      "supercategory": "food"
+    },
+    {
+      "id": 1069,
+      "name": "tomato",
+      "name_readable": "Tomato",
+      "supercategory": "food"
+    },
+    {
+      "id": 1058,
+      "name": "french-beans",
+      "name_readable": "French beans",
+      "supercategory": "food"
+    },
+    {
+      "id": 1180,
+      "name": "mandarine",
+      "name_readable": "Mandarine",
+      "supercategory": "food"
+    },
+    {
+      "id": 2618,
+      "name": "wine-red",
+      "name_readable": "Wine, red",
+      "supercategory": "food"
+    },
+    {
+      "id": 1010,
+      "name": "potatoes-steamed",
+      "name_readable": "Potatoes steamed",
+      "supercategory": "food"
+    },
+    {
+      "id": 1588,
+      "name": "croissant",
+      "name_readable": "Croissant",
+      "supercategory": "food"
+    },
+    {
+      "id": 1879,
+      "name": "salami",
+      "name_readable": "Salami",
+      "supercategory": "food"
+    },
+    {
+      "id": 3080,
+      "name": "boisson-au-glucose-50g",
+      "name_readable": "Boisson au glucose 50g",
+      "supercategory": "food"
+    },
+    {
+      "id": 2388,
+      "name": "biscuits",
+      "name_readable": "Biscuits",
+      "supercategory": "food"
+    },
+    {
+      "id": 1108,
+      "name": "corn",
+      "name_readable": "Corn",
+      "supercategory": "food"
+    },
+    {
+      "id": 1032,
+      "name": "leaf-spinach",
+      "name_readable": "Leaf spinach",
+      "supercategory": "food"
+    },
+    {
+      "id": 2099,
+      "name": "jam",
+      "name_readable": "Jam",
+      "supercategory": "food"
+    },
+    {
+      "id": 2530,
+      "name": "tea-green",
+      "name_readable": "Tea, green",
+      "supercategory": "food"
+    },
+    {
+      "id": 1013,
+      "name": "chips-french-fries",
+      "name_readable": "Chips, french fries",
+      "supercategory": "food"
+    },
+    {
+      "id": 1323,
+      "name": "parmesan",
+      "name_readable": "Parmesan",
+      "supercategory": "food"
+    },
+    {
+      "id": 2634,
+      "name": "beer",
+      "name_readable": "Beer",
+      "supercategory": "food"
+    },
+    {
+      "id": 1056,
+      "name": "avocado",
+      "name_readable": "Avocado",
+      "supercategory": "food"
+    },
+    {
+      "id": 1520,
+      "name": "bread-french-white-flour",
+      "name_readable": "Bread, French (white flour)",
+      "supercategory": "food"
+    },
+    {
+      "id": 1788,
+      "name": "chicken",
+      "name_readable": "Chicken",
+      "supercategory": "food"
+    },
+    {
+      "id": 1352,
+      "name": "soft-cheese",
+      "name_readable": "Soft cheese",
+      "supercategory": "food"
+    },
+    {
+      "id": 2498,
+      "name": "tea",
+      "name_readable": "Tea",
+      "supercategory": "food"
+    },
+    {
+      "id": 2711,
+      "name": "sauce-savoury",
+      "name_readable": "Sauce (savoury)",
+      "supercategory": "food"
+    },
+    {
+      "id": 2103,
+      "name": "honey",
+      "name_readable": "Honey",
+      "supercategory": "food"
+    },
+    {
+      "id": 1554,
+      "name": "bread-whole-wheat",
+      "name_readable": "Bread, whole wheat",
+      "supercategory": "food"
+    },
+    {
+      "id": 1556,
+      "name": "bread-sourdough",
+      "name_readable": "Bread, sourdough",
+      "supercategory": "food"
+    },
+    {
+      "id": 1307,
+      "name": "gruyere",
+      "name_readable": "Gruyère",
+      "supercategory": "food"
+    },
+    {
+      "id": 1060,
+      "name": "pickle",
+      "name_readable": "Pickle",
+      "supercategory": "food"
+    },
+    {
+      "id": 1220,
+      "name": "mixed-nuts",
+      "name_readable": "Mixed nuts",
+      "supercategory": "food"
+    },
+    {
+      "id": 2580,
+      "name": "water-mineral",
+      "name_readable": "Water, mineral",
+      "supercategory": "food"
+    }
+  ]

build.sh

+#!/bin/bash
+
+docker build -t $IMAGE_NAME
\ No newline at end of file

compile.sh

-#!/usr/bin/env bash
-
-PYTHON=${PYTHON:-"python"}
-
-echo "Building roi align op..."
-cd mmdet/ops/roi_align
-if [ -d "build" ]; then
-    rm -r build
-fi
-$PYTHON setup.py build_ext --inplace
-
-echo "Building roi pool op..."
-cd ../roi_pool
-if [ -d "build" ]; then
-    rm -r build
-fi
-$PYTHON setup.py build_ext --inplace
-
-echo "Building nms op..."
-cd ../nms
-if [ -d "build" ]; then
-    rm -r build
-fi
-$PYTHON setup.py build_ext --inplace
-
-echo "Building dcn..."
-cd ../dcn
-if [ -d "build" ]; then
-    rm -r build
-fi
-$PYTHON setup.py build_ext --inplace
-
-echo "Building sigmoid focal loss op..."
-cd ../sigmoid_focal_loss
-if [ -d "build" ]; then
-    rm -r build
-fi
-$PYTHON setup.py build_ext --inplace
-
-echo "Building masked conv op..."
-cd ../masked_conv
-if [ -d "build" ]; then
-    rm -r build
-fi
-$PYTHON setup.py build_ext --inplace