YOLOv / README.md
adityaeucloid's picture
add ultralytics model card
48f804b
|
raw
history blame
1.71 kB
metadata
tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - object-detection
  - pytorch
library_name: ultralytics
library_version: 8.0.238
inference: false
model-index:
  - name: adityaeucloid/YOLOv
    results:
      - task:
          type: object-detection
        metrics:
          - type: precision
            value: 0.03509
            name: mAP@0.5(box)
adityaeucloid/YOLOv

Supported Labels

['customer_address', 'customer_gst', 'customer_name', 'customer_pan', 'doc_type', 'invoice_date', 'invoice_number', 'invoice_table', 'net_amount', 'supplier_address', 'supplier_gst', 'supplier_name', 'supplier_pan', 'tax_amount', 'total_amount']

How to use

pip install ultralyticsplus==0.0.29 ultralytics==8.0.238
  • Load model and perform prediction:
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('adityaeucloid/YOLOv')

# 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()