Add inference section

This PR adds a code snippet showcasing end-to-end inference.

README.md

@@ -6,6 +6,7 @@ tags:
 - yolov10
 datasets:
 - detection-datasets/coco
+inference: false
 ---
 
 ### Model Description
@@ -16,10 +17,10 @@ datasets:
 
 ### Installation
 ```
-pip install supervision git+https://github.com/THU-MIG/yolov10.git
+pip install git+https://github.com/THU-MIG/yolov10.git supervision
 ```
 
-### Yolov10 Training/Validation/Prediction
+### Training/Validation
 ```python
 from ultralytics import YOLOv10
 
@@ -36,6 +37,62 @@ model.val(...)
 model.predict(...)
 ```
 
+### Inference
+
+Here's an end-to-end example showcasing inference on a cats image:
+
+```python
+from ultralytics import YOLOv10
+import supervision as sv
+from PIL import Image
+import requests
+
+# load model
+model = YOLOv10.from_pretrained("nielsr/yolov10n")
+
+# load image
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+image = np.array(image)
+
+# perform inference
+results = model(source=image, conf=0.25, verbose=False)[0]
+detections = sv.Detections.from_ultralytics(results)
+box_annotator = sv.BoxAnnotator()
+
+category_dict = {
+    0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
+    6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
+    11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
+    16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
+    22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
+    27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
+    32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
+    36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
+    40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
+    46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
+    51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
+    56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
+    61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
+    67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
+    72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
+    77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
+}
+
+labels = [
+    f"{category_dict[class_id]} {confidence:.2f}"
+    for class_id, confidence in zip(detections.class_id, detections.confidence)
+]
+annotated_image = box_annotator.annotate(
+    image.copy(), detections=detections, labels=labels
+)
+
+Image.fromarray(annotated_image)
+```
+which shows:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f1158120c833276f61f1a84/N8cV9jam_UsEzexp-W98n.png)
+
 ### BibTeX Entry and Citation Info
  ```
 @article{wang2024yolov10,