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title: README
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# Detection datasets

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*Easily load and transform datasets for object detection.*

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**Documentation**: https://blinjrm.github.io/detection-datasets/

**Source Code**: https://github.com/blinjrm/detection-datasets

**Datasets on Hugging Face Hub**: https://huggingface.co/detection-datasets

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`detection_datasets` aims to make it easier to work with detection datasets.  

This library works alongside the [Detection dataset](https://huggingface.co/detection-datasets) organisation on the Hugging Face Hub, where some detection datasets have been uploaded in the format expected by the library, and are ready to use.  

The main features are:
* **Read** the dataset :
    * From disk if it has already been downloaded.
    * Directly from the Hugging Face Hub if it [already exist](https://huggingface.co/detection-datasets).
* **Transform** the dataset:
    * Select a subset of data.
    * Remap categories.
    * Create new train-val-test splits.
* **Visualize** the annotations and images.
* **Write** the dataset:
    * To disk, selecting the target detection format: `COCO`, `YOLO` and more to come.
    * To the Hugging Face Hub for easy reuse in a different environment and share with the community.

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*Read the quick start bellow, or directly jump to the tutorials:*

| Goal                                 | Tutorial | Colab |
|--------------------------------------|:--------:|:-----:|
| Load from disk and upload to the Hub | [Open in the docs](https://blinjrm.github.io/detection-datasets/tutorials/1_Read/)     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blinjrm/detection-datasets/blob/main/docs/tutorials/1_Read.ipynb)      |
| Load from the Hub and transform          | [Open in the docs](https://blinjrm.github.io/detection-datasets/tutorials/2_Transform/)     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blinjrm/detection-datasets/blob/main/docs/tutorials/2_Transform.ipynb)      |

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More datasets to come! 🔥