Yolov8n Blood Cell Detection

🚀 keremberke/yolov8n-blood-cell-detection

This project is a blood cell object detection model based on YOLOv8, which can accurately detect platelets, red blood cells (RBC), and white blood cells (WBC) in images.

✨ Features

• Object Detection: Capable of detecting platelets, RBC, and WBC in images.
• Supported Library: Utilizes the ultralytics library with version 8.0.21.
• Dataset: Trained on the keremberke/blood-cell-object-detection dataset.

📦 Installation

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8n-blood-cell-detection')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()

📚 Documentation

Supported Labels

['Platelets', 'RBC', 'WBC']

Model Performance

More models available at: awesome-yolov8-models