| tags: | |
| - ultralyticsplus | |
| - yolov8 | |
| - ultralytics | |
| - yolo | |
| - vision | |
| - object-detection | |
| - pytorch | |
| library_name: ultralytics | |
| library_version: 8.0.21 | |
| inference: false | |
| model-index: | |
| - name: uisikdag/msgsulogo_v8 | |
| results: | |
| - task: | |
| type: object-detection | |
| metrics: | |
| - type: precision # since mAP@0.5 is not available on hf.co/metrics | |
| value: 0.88786 # min: 0.0 - max: 1.0 | |
| name: mAP@0.5(box) | |
| <div align="center"> | |
| <img width="640" alt="uisikdag/msgsulogo_v8" src="https://huggingface.co/uisikdag/msgsulogo_v8/resolve/main/thumbnail.jpg"> | |
| </div> | |
| ### Supported Labels | |
| ``` | |
| ['msgsu'] | |
| ``` | |
| ### How to use | |
| - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): | |
| ```bash | |
| pip install ultralyticsplus==0.0.23 ultralytics==8.0.21 | |
| ``` | |
| - Load model and perform prediction: | |
| ```python | |
| from ultralyticsplus import YOLO, render_result | |
| # load model | |
| model = YOLO('uisikdag/msgsulogo_v8') | |
| # 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() | |
| ``` | |