--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - object-detection - pytorch library_name: ultralytics library_version: 8.0.238 inference: false model-index: - name: adityaeucloid/YOLOv8 results: - task: type: object-detection metrics: - type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.02602 # min: 0.0 - max: 1.0 name: mAP@0.5(box) ---
adityaeucloid/YOLOv8
### 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 - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): ```bash pip install ultralyticsplus==0.0.29 ultralytics==8.0.238 ``` - Load model and perform prediction: ```python from ultralyticsplus import YOLO, render_result # load model model = YOLO('adityaeucloid/YOLOv8') # 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() ```