metadata
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.239
inference: false
datasets:
- chanelcolgate/yenthienviet
model-index:
- name: chanelcolgate/chamdiemgianhang-test1
results:
- task:
type: object-detection
dataset:
type: chanelcolgate/yenthienviet
name: yenthienviet
split: validation
metrics:
- type: precision
value: 0.95747
name: mAP@0.5(box)
Supported Labels
['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']
How to use
- Install ultralyticsplus:
pip install ultralyticsplus==0.1.0 ultralytics==8.0.239
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('chanelcolgate/chamdiemgianhang-test1')
# 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()