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...@@ -17,190 +17,4 @@ This repository contains: ...@@ -17,190 +17,4 @@ This repository contains:
- **Baselines** - **Baselines**
- **Notebook Submissions** - **Notebook Submissions**
# 🏆 About the Benchmark **@shivam to add the starter kit readme**
<img src="https://i.imgur.com/YvIIgOZ.png" width="600">
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
<details align="left">
<summary>💪 Getting Started</summary>
* [Using this repository](#using-this-repository)
* [Using colab starter kit](#using-colab-starter-kit)
* [Running the code locally](#running-the-code-locally)
</details>
<details align="left">
<summary>👥 Participation</summary>
* [Quick Participation 🏃](#-participation)
* [Active Participation 👨‍💻](#-participation)
</details>
<details align="left">
<summary>🧩 Repository Structure</summary>
* [Required files](#required-files)
* [Other files](#other-files)
</details>
<details align="left">
<summary>🚀 Submission</summary>
* [Quick Participation 🏃](#-submission)
* [Active Participation 👨‍💻](#-submission)
</details>
<details align="left">
<summary>📎 Important Links</summary>
* [Challenge pages](#-important-links)
* [Colab notebook links](#-important-links)
* [Other resources](#-important-links)
</details>
<br>
# 💪 Getting Started
## Download Dataset
<a href="https://www.aicrowd.com/challenges/food-recognition-benchmark-2022/dataset_files"><img src="https://i.imgur.com/EnD7Rvl.png" width="600"></a>
## Using this repository
This repository contains prediction codebase for `mmdetection`, `detectron2` and random agents.
```bash
# 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.
# 👥 Participation
Before we do a deep dive into submissions. Check which user persona suits you the best!
<table style="undefined;table-layout: fixed; width: 602px">
<colgroup>
<col style="width: 301px">
<col style="width: 301px">
</colgroup>
<thead>
<tr>
<th>Quick Participation 🏃</th>
<th>Active Participation 👨‍💻</th>
</tr>
</thead>
<tbody>
<tr>
<td>You need to <b>upload prediction</b> json files</td>
<td>You need to <b>submit code</b> (and AIcrowd evaluators runs the code to generate predictions)</td>
</tr>
<tr>
<td>Scores are computed on <b>40% of the publicly released test set</b> </td>
<td>Scores are computed on <b>100% of the publicly released test set + 40% of the (unreleased) extended test set</b></td>
</tr>
<tr>
<td>You are not eligible for the final leaderboard (and prizes)</td>
<td>You are eligible for the final leaderboard and prizes</td>
</tr>
</tbody>
</table>
The flow for active participation look as follows:
<img src="https://i.imgur.com/xzQkwKV.jpg" width="700">
# 🧩 Repository structure
## Required files
**File** | **Description**
--- | ---
`aicrowd.json` | A configuration file used to identify the benchmark and resources needed for evaluation
`apt.txt` | List of packages that should be installed (via `apt`) for your code to run
`requirements.txt` | List of python packages that should be installed (via `pip`) for your code to run
`predict.py` | Entry point to your model
## Other important files
**File** | **Description**
--- | ---
`score.py` | Helps your generate score for your run locally
`utils/` | Directory containing some useful scripts and notebooks
`utils/requirements_detectron2.txt` | A sample `requirements.txt` file for using `detectron2`
`utils/requirements_mmdetection.txt` | A sample `requirements.txt` file for using `mmdetection`
# 🚀 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](https://www.aicrowd.com/challenges/food-recognition-benchmark-2022/submissions/new)
- Get scores, iterate, improve! 💪
## Active Participation 👨‍💻
- Prepare your runtime environment
- Make submissions by pushing your code repository
- Get scores, [**more scores**](#-participation) 😉, iterate faster, improve faster! 💪
More details for active participation in present in [SUBMISSION.md](/utils/SUBMISSION.md)
# 📎 Important links
- 💪 &nbsp;Benchmark Page: https://www.aicrowd.com/challenges/food-recognition-benchmark-2022
- 🗣️ &nbsp;Discussion Forum: https://www.aicrowd.com/challenges/food-recognition-benchmark-2022/discussion
- 🏆 &nbsp;Leaderboard: https://www.aicrowd.com/challenges/food-recognition-benchmark-2022/leaderboards
- 👥 &nbsp;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](https://colab.research.google.com/drive/1A5p9GX5X3n6OMtLjfhnH6Oeq13tWNtFO#scrollTo=ok54AWT_VoWV)
* [Baseline with `mmdetection` (pytorch)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-pytorch-baseline)
* [Baseline with `matterport-maskrcnn` (keras - tensorflow)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-recognition)
- Resources - Round 2
* [Colab Notebook for Data Analysis and Tutorial](https://colab.research.google.com/drive/1vXdv9quZ7CXO5lLCjhyz3jtejRzDq221)
* [Baseline with `mmdetection` (pytorch)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-round2)
- Resources - Round 3
* [Colab Notebook for data exploration](https://discourse.aicrowd.com/t/detectron2-colab-notebook-from-data-exploration-to-training-the-model/3691)
* [Colab Notebook for Detectron2](https://www.aicrowd.com/showcase/baseline-detectron2-starter-kit-for-food-recognition)
* [Starter kit for Detectron2](https://gitlab.aicrowd.com/food-recognition-challenge/food-starterkit-detectron2)
- [Participant contributions](https://discourse.aicrowd.com/tags/c/food-recognition-challenge/112/explainer)
- External resources:
* [Convert Annotations from MS COCO format to PascalVOC format](https://github.com/CasiaFan/Dataset_to_VOC_converter/blob/master/anno_coco2voc.py)
# ✍️ Maintainers
* **[Sharada Mohanty](https://twitter.com/memohanty?lang=en)**
* **[Shivam Khandelwal](https://twitter.com/skbly7?lang=en)**
## Thanks to our awesome contributors! ✨
<br>
<a href="https://github.com/AIcrowd/food-recognition-challenge-starter-kit/graphs/contributors">
<img src="https://contrib.rocks/image?repo=AIcrowd/food-recognition-challenge-starter-kit" />
</a>
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