Food Recognition Benchmark - Starter Kit

This repository is the main Food Recognition Benchmark template and Starter kit. Clone the repository to compete now!

This repository contains:

• mmdetection and detectron2 baselines for tackling this benchmark
• Documentation on how to submit your models to the leaderboard
• The procedure for best practices and information on how we evaluate your agent, etc.
• Starter code for you to get started!

NOTE: If you are resource-constrained or would not like to setup everything in your system, you can make your submission from inside Google Colab too. Check out the beta version of the Notebook.

🏆 About the Benchmark

The goal of this benchmark is to train models which can look at images of food items and detect the individual food items present in them. This is an ongoing, multi-round benchmark. At each round, the specific tasks and / or datasets will be updated, and each round will have its own prizes. You can participate in multiple rounds, or in single rounds.

This data set has been annotated with respect to segmentation, classification (mapping the individual food items onto an ontology of Swiss Food items), and weight/volume estimation.

Table of contents

💪 Getting Started

Download Dataset

Using this repository

This repository contains prediction codebase for mmdetection, detectron2 and random agents.

# Clone the repository
git clone https://github.com/AIcrowd/food-recognition-benchmark-starter-kit
cd food-recognition-benchmark-starter-kit

# Install dependencies
pip install -r requirements.txt

# Download the dataset, and place it in `data/images/`

# Run model locally
./run.sh

This will generate predictions.json file in your data/ directory.

Using colab starter kit

Please refer this notebook for Detectron2 quick and active submission.

Please refer this notebook for MMDetection quick and active submission.

Running the code locally

Refer predict_detectron2.py for Detectron2 submission

Refer predict_mmdetection.py for MMdetection submission

👥 Participation

Before we do a deep dive into submissions. Check which user persona suits you the best!

The flow for active participation look as follows:

🧩 Repository structure

Required files

Other important files

🚀 Submission

Quick Participation 🏃

As promised, we will keep it quick for you. Participating is as simple as:

• Generate your predictions using the starter kit
• Upload predictions.json on the benchmark website
• Get scores, iterate, improve! 💪

Active Participation 👨‍💻

• Prepare your runtime environment
• Make submissions by pushing your code repository
• Get scores, more scores 😉, iterate faster, improve faster! 💪

More details for active participation in present in SUBMISSION.md

📎 Important links

• 💪  Benchmark Page: https://www.aicrowd.com/challenges/food-recognition-benchmark-2022
• 🗣️  Discussion Forum: https://www.aicrowd.com/challenges/food-recognition-benchmark-2022/discussion
• 🏆  Leaderboard: https://www.aicrowd.com/challenges/food-recognition-benchmark-2022/leaderboards
• 👥  Find Teammates: https://discourse.aicrowd.com/t/looking-for-teammates-reply-here/6702
• 💬 Chat with other participants: https://discord.gg/jVFTB8A
• Resources - Round 1
  • Colab Notebook for Data Analysis and Tutorial
  • Baseline with mmdetection (pytorch)
  • Baseline with matterport-maskrcnn (keras - tensorflow)
• Resources - Round 2
  • Colab Notebook for Data Analysis and Tutorial
  • Baseline with mmdetection (pytorch)
• Resources - Round 3
  • Colab Notebook for data exploration
  • Colab Notebook for Detectron2
  • Starter kit for Detectron2
• Participant contributions
• External resources:
  • Convert Annotations from MS COCO format to PascalVOC format

✍️ Maintainers

• Sharada Mohanty
• Shivam Khandelwal

Thanks to our awesome contributors! ✨