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# Dataset Card for Linnaeus 5

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
Linnaeus 5 dataset contains RGB images (256x256) for classification across 5 categories: berry, bird, dog, flower, and other (negative set). It includes 1200 training images and 400 test images per class.

### Dataset Sources

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- **Homepage:** https://chaladze.com/l5/
- **Paper:** Chaladze, G., & Kalatozishvili, L. (2017). Linnaeus 5 dataset for machine learning. arXiv preprint arXiv:1707.06677.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

Total images: 8,000

Classes: 5 categories

Splits:

- **Train:** 6,000 images

- **Test:** 2,000 images

Image specs: JPEG format, 256×256 pixels, RGB

## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
```
from datasets import load_dataset  

# Load the dataset  
dataset = load_dataset("randall-lab/linnaeus5", split="train", trust_remote_code=True)   
# dataset = load_dataset("randall-lab/linnaeus5", split="test", trust_remote_code=True)  

# Access a sample from the dataset  
example = dataset[0]  
image = example["image"]  
label = example["label"]  

image.show()  # Display the image  
print(f"Label: {label}")
```

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

@article{chaladze2017linnaeus,
  title={Linnaeus 5 dataset for machine learning},
  author={Chaladze, G and Kalatozishvili, L},
  journal={arXiv preprint arXiv:1707.06677},
  year={2017}
}