Yolov8m Blood Cell Detection

🚀 keremberke/yolov8m-blood-cell-detection

This model is based on the Ultralytics framework and is designed for blood cell object - detection tasks, offering high - precision detection of platelets, red blood cells (RBC), and white blood cells (WBC).

🚀 Quick Start

✨ Features

• Object Detection: Specifically designed to detect platelets, RBC, and WBC in blood cell images.
• High Precision: Achieved a mAP@0.5(box) of 0.92674 on the blood - cell - object - detection validation dataset.

📦 Installation

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.24 ultralytics==8.0.23

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8m-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 Information

Results

More models available at: awesome - yolov8 - models