add ultralytics model card

README.md

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+ 
+---
+tags:
+- ultralyticsplus
+- yolov8
+- ultralytics
+- yolo
+- vision
+- image-segmentation
+- pytorch
+library_name: ultralytics
+library_version: 8.0.6
+inference: false
+
+datasets:
+- keremberke/pothole-segmentation
+
+model-index:
+- name: keremberke/yolov8n-pothole-segmentation
+  results:
+  - task:
+      type: image-segmentation
+
+    dataset:
+      type: keremberke/pothole-segmentation
+      name: pothole-segmentation
+      split: validation
+
+    metrics:
+      - type: precision  # since [email protected] is not available on hf.co/metrics
+        value: 0.00706  # min: 0.0 - max: 1.0
+        name: [email protected](box)
+      - type: precision  # since [email protected] is not available on hf.co/metrics
+        value: 0.00456  # min: 0.0 - max: 1.0
+        name: [email protected](mask)
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/yolov8n-pothole-segmentation" src="https://huggingface.co/keremberke/yolov8n-pothole-segmentation/resolve/main/thumbnail.jpg">
+</div>
+
+### Supported Labels
+
+```
+['pothole']
+```
+
+### How to use
+
+- Install [ultralytics](https://github.com/ultralytics/ultralytics) and [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
+
+```bash
+pip install -U ultralytics ultralyticsplus
+```
+
+- Load model and perform prediction:
+
+```python
+from ultralyticsplus import YOLO, render_model_output
+
+# load model
+model = YOLO('keremberke/yolov8n-pothole-segmentation')
+
+# 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
+for result in model.predict(image, return_outputs=True):
+    print(result["det"]) # [[x1, y1, x2, y2, conf, class]]
+    print(result["segment"]) # [segmentation mask]
+    render = render_model_output(model=model, image=image, model_output=result)
+    render.show()
+```
+