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.
This is an evolving dataset, where we will release more data as the dataset grows in size.
Ensure you have docker and nvidia-docker installed by following the instructions here :
- Docker
- nvidia-docker NOTE : You do not need nvidia-docker if you do not want to use GPU when testing your submission locally
git clone https://github.com/AIcrowd/food-recognition-challenge-starter-kit
cd food-recognition-challenge-starter-kit
pip install awscli botocore certifi cffi colorama cycler Cython decorator jmespath kiwisolver matplotlib networkx numpy olefile Pillow pyasn1 pycparser pyparsing python-dateutil pytz PyWavelets rsa s3transfer scikit-image scipy six tqdm jupyter-client jupyter-console jupyter-core jupyter-repo2docker jupyterlab jupyterlab-launcher crowdai
pip install pip install git+https://github.com/AIcrowd/coco.git#subdirectory=PythonAPI
The dataset for the AIcrowd Food Recognition Challenge is available at https://www.aicrowd.com/challenges/food-recognition-challenge/dataset_files
This dataset contains :
-
train.tar.gz: This is the Training Set of 5545 (as RGB images) food images, along with their corresponding annotations in MS-COCO format -
val.tar.gz: This is the suggested Validation Set of 291 (as RGB images) food images, along with their corresponding annotations in MS-COCO format -
test_images.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 :
|-- 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 datasetWe are also assuming that you have already installed all the requirements for this notebook, or you can still install them by :
# Usage
Now you can refer to the list of Jupyter Notebooks for different aspects of the challenge and the datasets.
You can access all of them by :
```bash
jupyter-notebook
For for a known ground truth mask A, you propose a mask B, then we first compute IoU (Intersection Over Union) :
Then we can define the following parameters :
- Precision (IoU > 0.5)
- Recall (IoU > 0.5)
The final scoring parameters AP_{IoU > 0.5} and AR_{IoU > 0.5} are computed by averaging over all the precision and recall values for all known annotations in the ground truth.
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.
We have created this sample submission repository which you can use as reference.
Each repository should have a aicrowd.json file with the following fields:
{
"challenge_id" : "aicrowd-food-recognition-challenge",
"grader_id": "aicrowd-food-recognition-challenge",
"authors" : ["aicrowd-user"],
"description" : "Food Recognition Challenge Submission",
"license" : "MIT",
"gpu": false
}
This file is used to identify your submission as a part of the Snake Species Identification 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.
You can specify the software runtime of your code by modifying the included Dockerfile.
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.
export TEST_IMAGES_PATH="./data/test_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`.
To make a submission, you will have to create a private repository on https://gitlab.aicrowd.com.
You will have to add your SSH Keys to your GitLab account by following the instructions here. If you do not have SSH Keys, you will first need to generate one.
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://github.com/AIcrowd/food-recognition-challenge-starter-kit
cd food-recognition-challenge-starter-kit
# Add AICrowd git remote endpoint
git remote add aicrowd git@gitlab.aicrowd.com:<YOUR_AICROWD_USER_NAME>/food-recognition-challenge-starter-kit.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-recognition-challenge-starter-kit/issues
Best of Luck