Create README.md

README.md

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+ 
+---
+tags:
+- ultralyticsplus
+- ultralytics
+- yolov8
+- yolo
+- vision
+- object-detection
+- pytorch
+library_name: ultralytics
+library_version: 8.0.4
+inference: false
+
+model-index:
+- name: ultralyticsplus/yolov8s
+  results:
+  - task:
+      type: object-detection
+
+    metrics:
+      - type: precision  # since mAP is not available on hf.co/metrics
+        value: 0.449  # min: 0.0 - max: 1.0
+        name: mAP
+---
+
+### Supported Labels
+
+```
+['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
+```
+
+### How to use
+
+- Install `ultralytics` and `ultralyticsplus`:
+
+```bash
+pip install -U ultralytics ultralyticsplus
+```
+
+- Load model and perform prediction:
+
+```python
+from ultralyticsplus import YOLO, render_predictions
+
+# load model
+model = YOLO('ultralyticsplus/yolov8s')
+
+# 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
+img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
+
+# perform inference
+for result in model.predict(img, imgsz=640, return_outputs=True):
+    print(result) # [x1, y1, x2, y2, conf, class]
+    render = render_predictions(model, img=img, det=result["det"])
+    render.show()
+```
+