README.md

---
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configs:
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    path: a2_s2t/test-*
- config_name: b_light
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    path: b_light/test-*
- config_name: c_external
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  - split: test
    path: c_external/test-*
license: mit
task_categories:
- object-detection
tags:
- biology
pretty_name: COLO
size_categories:
- 1K<n<10K
---

# COw LOcalization (COLO) Dataset

The COw LOcalization (COLO) dataset is designed to localize cows in various indoor environments using different lighting conditions and view angles. This dataset offers 1,254 images and 11,818 cow instances, serving as a benchmark for the precision livestock farming community.

![COLO](figure_1.jpg)

## Dataset Configurations

<style>
table {
    width: 50%;
    margin-left: auto;
    margin-right: auto;
}
</style>


| **Configuration** | **Training Split** | **Testing Split** |
|:------------------|:-------------------|:---------------------|
| _0_all_           | Top-View + Side-View | Top-View + Side-View|
| _1_top_           | Top-View             | Top-View            |
| _2_side_          | Side-View            | Side-View           |
| _3_external_      | External             | External            |
| _a1_t2s_          | Top-View             | Side-View           |
| _a2_s2t_          | Side-View            | Top-View            |
| _b_light_         | Daylight             | Indoor + NIR        |
| _c_external_      | Top-View + Side-View | External            |


## Download the Dataset

To download the dataset, you need to have the required Python dependencies installed. You can install them using either of the following commands:

```sh
python -m pip install pyniche
```
or 

```sh
pip install pyniche
```

Once the dependencies are installed, use the Python console to provide the download destination folder in the `root` parameter and specify the export data format in the `format` parameter:

```python
from pyniche.data.download import COLO

# Example: Download COLO in the YOLO format
COLO(
    root="download/yolo",  # Destination folder
    format="yolo",  # Data format
)

# Example: Download COLO in the COCO format
COLO(
    root="download/coco",  # Destination folder
    format="coco",  # Data format
)
```

## Citation


[The page of the arXiv article](https://arxiv.org/abs/2407.20372)

```bibtex
@misc{das2024model,
    title={A Model Generalization Study in Localizing Indoor Cows with COw LOcalization (COLO) dataset},
    author={Mautushi Das and Gonzalo Ferreira and C. P. James Chen},
    year={2024},
    eprint={2407.20372},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

or

Das, M., Ferreira, G., & Chen, C. P. J. (2024). A Model Generalization Study in Localizing Indoor Cows with COw LOcalization (COLO) dataset. arXiv preprint arXiv:2407.20372

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