Checker TB Yolov8 Ver1

🚀 linhcuem/checker_TB_yolov8_ver1

This project is based on the Ultralytics library and utilizes the YOLOv8 model for object detection tasks. It can accurately detect various types of objects in images, providing high-precision detection results.

🚀 Quick Start

Prerequisites

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43

Usage Steps

1. Load the model and perform prediction:

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('linhcuem/checker_TB_yolov8_ver1')

# 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()

✨ Features

Supported Labels

The model supports the following labels for object detection:

['bom_gen', 'bom_jn', 'bom_knp', 'bom_sachet', 'bom_vtgk', 'bom_ytv', 'hop_dln', 'hop_jn', 'hop_vtg', 'hop_ytv', 'lo_kids', 'lo_ytv', 'loc_dln', 'loc_jn', 'loc_kids', 'loc_ytv', 'pocky', 'tui_gen', 'tui_jn', 'tui_sachet', 'tui_vtgk']

📚 Documentation

Model Information

Model Visualization