draw2

 pipeline_tag: object-detection
 library_name: transformers
 ---
+<div align="center">
+    <p>
+        <img src="https://raw.githubusercontent.com/HichTala/draw2/refs/heads/main/figures/banner-draw.png">
+    </p>
+<div>
+[![Licence](https://img.shields.io/pypi/l/ultralytics)](LICENSE)
+[![Twitter](https://badgen.net/badge/icon/twitter?icon=twitter&label)](https://twitter.com/tiazden)
+[![Github](https://img.shields.io/badge/-github-181717?logo=github&labelColor=555&color=%23181717)](https://github.com/HichTala/draw2)
+[![HuggingFace Downloads](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2FHichTala%2Fdraw2&query=%24.downloads&logo=huggingface&label=downloads&color=%23FFD21E)](https://huggingface.co/HichTala/draw2)
+[![OBS Plugin](https://img.shields.io/badge/-obs_plugin-302E31?logo=obsstudio&labelColor=555&color=%23302E31)](https://github.com/HichTala/draw2-obsplugin)
+[![WandB](https://img.shields.io/badge/visualize_in-W%26B-yellow?logo=weightsandbiases&color=%23FFBE00)](https://wandb.ai/hich_/draw)
+[![Medium](https://img.shields.io/badge/Medium-12100E?style=flat&logo=medium&logoColor=white)](https://medium.com/@hich.tala.phd/how-i-trained-a-model-to-detect-and-recognise-a-wide-range-of-yu-gi-oh-cards-6ea71da007fd)
+</div>
+</div>
+DRAW 2 (which stands for **D**etect and **R**ecognize **A** **W**ide range of cards version 2) is an object detector
+trained to detect _Yu-Gi-Oh!_ cards in all types of images, and in particular in dueling images.
+With this new version, **DRAW 2** goes beyond its predecessor. It’s more accurate, more robust, and way easier to use.
+It now includes an [OBS plugin](https://github.com/HichTala/draw2-obsplugin) that lets users seamlessly integrate the
+detector directly into their live streams or recorded videos; and those **without any particular technical skills**.
+The plugin can display detected cards in real time for an enhanced viewing experience.
+Other works exist (see [Related Works](#div-aligncenterrelated-worksdiv)) but none is capable of recognizing cards
+during a duel.
+This project is licensed under the [GNU Affero General Public License v3.0](LICENCE); all contributions are welcome.
+---
+## <div align="center">📄Documentation</div>
+If you juste want to use the plugin, please refer to the [OBS plugin page](https://github.com/HichTala/draw2-obsplugin).
+You don't need to install anything from this repository.
+The documentation below is for people who want to use the detector outside of OBS, this will require some coding skills.
+### Installation
+You need python to be installed. Python installation isn't going to be detailed here, you can refer to
+the [documentation](https://www.python.org/).
+We first need to install pytorch. It is recommended to use a package manager such
+as [miniconda](https://docs.conda.io/projects/miniconda/en/latest/).
+Please refer to the [documentation](https://docs.conda.io/projects/miniconda/en/latest/).
+When everything is set up you can run the following command to install pytorch:
+```shell
+python -m pip install torch torchvision
+```
+If you want to use you gpus to make everything run faster, please refer
+the [documentation](https://pytorch.org/get-started/locally/)
+Then you just have to clone the repo and install `requirements`:
+```shell
+git clone https://github.com/HichTala/draw
+cd draw
+python -m pip install -r requirements.txt
+```
+If you don't want to clone the repository and have already all the requirements installed, you can just run:
+```shell
+python -m pip install git+https://github.com/HichTala/draw2.git
+```
+Your installation is now completed.
+### 🚀 Usage
+Once the installation is done, you can use the detector by executing the following command:
+```shell
+python -m draw
+```
+You can use the `--help` flag to see all available options:
+```shell
+python -m draw --help
+```
+Here are the most important options:
+- `--source`: Path to your image, video, or webcam index (default is `0` for webcam).
+- `--save`: Save path for the output.
+- `--show`: Display the output in a window.
+- `--display-card`: Display detected cards on the output.
+- `--deck-list`: Path to a ydk file containing the list of cards in your deck for better recognition.
+- `--fps`: FPS of the saved video (default is 60).
+---
+## <div align="center">💡Inspiration</div>
+This project is inspired by content creator [SuperZouloux](https://www.youtube.com/watch?v=64-LfbggqKI)'s idea of a
+hologram bringing _Yu-Gi-Oh!_ cards to life.
+His project uses chips inserted under the sleeves of each card,
+which are read by the play mat, enabling the cards to be recognized.
+Inserting the chips into the sleeves is not only laborious, but also poses another problem:
+face-down cards are read in the same way as face-up ones.
+So an automatic detector is a really suitable solution.
+Although this project was discouraged by _KONAMI_ <sup>®</sup>, the game's publisher (which is quite understandable),
+we can nevertheless imagine such a system being used to display the cards played during a live duel,
+to allow viewers to read the cards.
+---
+## <div align="center">🔗Related Works</div>
+Although to my knowledge `draw` is the first detector capable of locating and detecting _Yu-Gi-Oh!_ cards in a dueling
+environment,
+other works exist and were a source of inspiration for this project. It's worth mentioning them here.
+[Yu-Gi-Oh! NEURON](https://www.konami.com/games/eu/fr/products/yugioh_neuron/) is an official application developed by
+_KONAMI_ <sup>®</sup>.
+It's packed with features, including cards recognition. The application is capable of recognizing a total of 20 cards at
+a time, which is very decent.
+The drawback is that the cards must be of good quality to be recognized, which is not necessarily the case in a duel
+context.
+What's more, it can't be integrated, so the only way to use it is to use the application.
+[yugioh one shot learning](https://github.com/vanstorm9/yugioh-one-shot-learning) made by `vanstorm9` is a
+Yu-Gi-Oh! cards classification program that allow you to recognize cards. It uses siamese network to train its
+classification
+model. It gives very impressive results on images with a good quality but not that good on low quality images, and it
+can't localize cards.
+[Yolov11](https://github.com/ultralytics/ultralytics) is the last version of the very famous `yolo` family of object
+detector models that handle oriented bounding boxes.
+I think it doesn't need to be presented today, it represents state-of-the-art real time object detection model.
+[ViT](https://arxiv.org/pdf/2010.11929.pdf) is a pre-trained model for image classification based on the Vision
+Transformer architecture.
+It relies entirely on attention mechanisms to process image patches instead of using convolutional layers.
+It fits our task well since pre-trained versions on large-scale datasets such as ImageNet-21K are available.
+This is particularly relevant for our use case, as it enables handling a large number of visual categories similar to
+the 13k+ unique cards found in _Yu-Gi-Oh!_.
+[SpellTable](https://spelltable.wizards.com/) is a free application designed and built by `Jonathan Rowny` and his team
+for playing paper _Magic: The Gathering_ from a distance.
+It allows player to click on a card on any player's feed to quickly identify it.
+It has some similarity with `draw` since it can localize and recognize any card from a built in database of 17 000
+cards.
+The idea is close to this project, but it didn't originate it.
+---
+## <div align="center">💬Contact</div>
+You can reach me on Twitter [@tiazden](https://twitter.com/tiazden) or by email
+at [hich.tala.phd@gmail.com](mailto:hich.tala.phd@gmail.com).
+---
+## <div align="center">⭐Star History</div>
+<a href="https://www.star-history.com/#HichTala/draw2&type=date&legend=top-left">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=HichTala/draw2&type=date&theme=dark&legend=top-left" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=HichTala/draw2&type=date&legend=top-left" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=HichTala/draw2&type=date&legend=top-left" />
+ </picture>
+</a>