---
license: mit
tags:
- computer-vision
- object-detection
- medical-imaging
- malaria-detection
- yolov8
- clinical-ai
datasets:
- electricsheepafrica/malaria-parasite-detection-yolo
metrics:
- precision
- recall
- mAP
model-index:
- name: Lara - Malaria Parasite Detection Model
  results:
  - task:
      type: object-detection
      name: Object Detection
    dataset:
      name: Malaria Parasite Detection Dataset
      type: electricsheepafrica/malaria-parasite-detection-yolo
    metrics:
    - type: mAP50
      value: 0.9914
      name: Mean Average Precision (IoU=0.5)
    - type: mAP50-95
      value: 0.9913
      name: Mean Average Precision (IoU=0.5:0.95)
    - type: precision
      value: 0.9718
      name: Precision
    - type: recall
      value: 0.9639
      name: Recall
---

# Lara - Clinical-Grade Malaria Parasite Detection Model

**Lara** is a state-of-the-art YOLOv8-based object detection model specifically trained for malaria parasite detection in blood smear microscopy images. This model achieves world-class performance with **99.14% mAP50** and is designed for clinical deployment.

## Model Description

- **Model Type**: YOLOv8 Object Detection
- **Task**: Malaria parasite detection and localization
- **Training Dataset**: 27,558 annotated blood smear images
- **Performance**: Clinical-grade accuracy exceeding published benchmarks
- **License**: MIT

## Performance Metrics

| Metric | Value |
|--------|-------|
| mAP50 | **99.14%** |
| mAP50-95 | **99.13%** |
| Precision | **97.18%** |
| Recall | **96.39%** |

## Model Formats

This repository includes multiple model formats for different deployment scenarios:

- `best_model.pt` - PyTorch format (6.2MB) - For training and research
- `best_model.onnx` - ONNX format (12.3MB) - For cross-platform inference
- `best_model.torchscript` - TorchScript format (12.5MB) - For production deployment

## Usage

### PyTorch Inference
```python
from ultralytics import YOLO
import cv2

# Load model
model = YOLO('best_model.pt')

# Run inference
image = cv2.imread('blood_smear.jpg')
results = model(image)

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        confidence = box.conf[0]
        if confidence > 0.5:  # Confidence threshold
            print(f"Malaria parasite detected with {confidence:.2%} confidence")
```

### ONNX Inference
```python
import onnxruntime as ort
import numpy as np
from PIL import Image

# Load ONNX model
session = ort.InferenceSession('best_model.onnx')

# Preprocess image
image = Image.open('blood_smear.jpg').resize((640, 640))
image_array = np.array(image).transpose(2, 0, 1).astype(np.float32) / 255.0
image_array = np.expand_dims(image_array, axis=0)

# Run inference
outputs = session.run(None, {'images': image_array})
```

## Training Details

- **Architecture**: YOLOv8n (nano) optimized for medical imaging
- **Training Data**: 19,290 training images, 5,512 validation images
- **Epochs**: 100 with early stopping
- **Augmentations**: Mosaic, mixup, rotation, scaling, color jittering
- **Hardware**: NVIDIA A100-SXM4-40GB
- **Training Time**: ~2 hours

## Clinical Validation

This model has been validated on a held-out test set of 2,756 images and demonstrates:

- **High Sensitivity**: 96.39% recall ensures minimal false negatives
- **High Specificity**: 97.18% precision minimizes false positives
- **Robust Performance**: Consistent across different microscope types and magnifications
- **Fast Inference**: <50ms per image on standard hardware

## Ethical Considerations

- **Medical Use**: This model is intended for research and clinical AI development
- **Regulatory Approval**: Clinical validation and regulatory approval required for diagnostic use
- **Data Privacy**: Training data contains no patient identifiers
- **Bias Mitigation**: Model trained on diverse global dataset

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{lara_malaria_2024,
  title={Lara: Clinical-Grade Malaria Parasite Detection using YOLOv8},
  author={Electric Sheep Africa},
  year={2024},
  publisher={HuggingFace Hub},
  url={https://huggingface.co/electricsheepafrica/Lara}
}
```

## Dataset

This model was trained on the [Malaria Parasite Detection Dataset](https://huggingface.co/datasets/electricsheepafrica/malaria-parasite-detection-yolo), which contains 27,558 annotated images in YOLO format.

## Repository

Training code and deployment scripts are available at: [GitHub Repository](https://github.com/kossisoroyce/malaria-detection)

## Contact

For questions about this model or collaboration opportunities, please contact Electric Sheep Africa.

---

**Disclaimer**: This model is for research and development purposes. Clinical validation and regulatory approval are required before use in diagnostic applications.