metadata

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
      num_examples: 266
    - name: validation
      num_bytes: 145478
      num_examples: 4
    - name: test
      num_bytes: 205522
      num_examples: 5
  download_size: 9069482
  dataset_size: 9188079

mosquito-egg-detection

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

Intended uses & limitations

To be fulfilled.

How to use

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/
        }
    }
}