README.md · chanelcolgate/chamdiemgianhang-vsk at main

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-vsk
    results:
      - task:
          type: object-detection
        dataset:
          type: chanelcolgate/yenthienviet
          name: yenthienviet
          split: validation
        metrics:
          - type: precision
            value: 0.92084
            name: mAP@0.5(box)

Supported Labels

['BOM_GEN', 'BOM_JUN', 'BOM_KID', 'BOM_SAC', 'BOM_VTG', 'BOM_YTV', 'HOP_FEJ', 'HOP_FRE', 'HOP_JUN', 'HOP_POC', 'HOP_VTG', 'HOP_YTV', 'LOC_JUN', 'LOC_KID', 'LOC_YTV', 'LOO_DAU', 'LOO_KID', 'LOO_MAM', 'LOO_YTV', 'POS_NHO', 'TUI_GEN', 'TUI_JUN', 'TUI_KID', 'TUI_SAC', 'TUI_VTG', 'TUI_YTV']

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-vsk')

# 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()