Update README.md (#2)

- Update README.md (8c9e0aa439d336d5ec36b17ff8dcc7a5cd420e5f)


Co-authored-by: Kerem <keremberke@users.noreply.huggingface.co>

README.md

@@ -30,18 +30,19 @@ model-index:
 ['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`:
+- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
 
 ```bash
-pip install -U ultralytics ultralyticsplus
+pip install -U ultralyticsplus==0.0.14
 ```
 
 - Load model and perform prediction:
 
 ```python
-from ultralyticsplus import YOLO, render_predictions
+from ultralyticsplus import YOLO, render_result
 
 # load model
 model = YOLO('ultralyticsplus/yolov8s')
@@ -53,12 +54,14 @@ 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'
+image = '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()
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
 ```