README.md

---
annotations_creators:
- crowdsourced
license: mit
task_categories:
- object-detection
pretty_name: Mosquito egg detection dataset
tags:
- mosquito
- object-detection
- aedes-aegypt
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: image_id
    dtype: int64
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: objects
    struct:
    - name: label
      sequence: string
    - name: category
      sequence: int64
    - name: area
      sequence: float64
    - name: bbox
      sequence:
        sequence: float64
    - name: id
      sequence: int64
  splits:
  - name: train
    num_bytes: 8837079.0
    num_examples: 266
  - name: validation
    num_bytes: 145478.0
    num_examples: 4
  - name: test
    num_bytes: 205522.0
    num_examples: 5
  download_size: 9069482
  dataset_size: 9188079.0
---
# mosquito-egg-detection

This dataset was generated using images from Fiocruz manually annotated by students of Federal University of Santa 
Maria.

![egg_detection](https://i.imgur.com/ZVekanw.png)

##  Intended uses & limitations

To be fulfilled.

## How to use

```python
import numpy as np
from PIL import ImageDraw

import torch
from datasets import load_dataset

TOKEN = 'generate your token at https://huggingface.co./settings/tokens'

path_space = 'henryzord/mosquito-egg-detection'

dataset = load_dataset(path_space, token=TOKEN)
image = dataset['test'][np.random.randint(len(dataset['test']))]['image']

draw = ImageDraw.Draw(image)
image.show()
```

## BibTeX entry and citation info

If you find this dataset useful, please cite it:

```
@misc{
    author={Fulfill me},
    title={Fulfill me},
    year={2024},
    howpublished={
        Available at \url{
            henryzord/](https://huggingface.co./henryzord/
        }
    }
}
```