electricsheepafrica/malaria-parasite-detection-yolo

Malaria Parasite Detection Yolo | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: not declared - Sector: health - Engineered by Electric Sheep Africa

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.

Dataset context from the existing Hugging Face card: Malaria Parasite Detection Dataset (YOLO Format) Dataset Description This dataset provides high-quality bounding box annotations for malaria parasite detection, converted from the NIH malaria classification dataset using advanced computer vision techniques. It enables training of object detection models for clinical malaria diagnosis with proven performance of 99.1% mAP50. Dataset Summary Total Images: 27,558 microscopy images Format: YOLO v8 object detection… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/malaria-parasite-detection-yolo.

Dataset Profile

Field	Value
Hugging Face repo	electricsheepafrica/malaria-parasite-detection-yolo
Sector	health
Topic tags	medical, malaria, object-detection, yolo, yolov8, clinical-ai, microscopy
Modalities	not declared
Formats	not declared
Size category	10K<n<100K
Countries	Africa-wide or source-defined African coverage
ISO3 coverage	not declared
Last modified on HF	2025-09-01 11:29:10+00:00
Inventory snapshot	2026-07-16T16:00:34Z

How To Read This Dataset

• Start from the repository files and the dataset viewer when available.
• Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
• Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
• Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/malaria-parasite-detection-yolo")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

• This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
• Exact schema, row counts, and source files should be inspected in the repository data files.
• Metadata gaps from the inventory: country, upstream_publisher, modality, format.
• Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

• Source context: Electric Sheep Africa metadata inventory
• Publisher/source attribution: Public dataset metadata
• License: mit
• Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/malaria-parasite-detection-yolo
• Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

• Inspect schema and missingness before modeling.
• Profile variables by geography, time, and subgroup columns where present.
• Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
• Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_malaria_parasite_detection_yolo_2026,
  title        = {Malaria Parasite Detection Yolo | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/malaria-parasite-detection-yolo},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/malaria-parasite-detection-yolo}}
}

License

Released under mit.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

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Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.