Describe how to use the model

README.md

@@ -7,4 +7,51 @@ language:
 metrics:
 - accuracy
 pipeline_tag: object-detection
----
+---
+
+## How to Get Started with the Model
+
+### Install `ultralytics YOLO` package
+
+``` shell
+$ pip install ultralytics
+```
+
+### Perform Inference as per [kurkurzz](https://github.com/kurkurzz/custom-yolov8-auto-annotation-cvat-blueprint/blob/master/main.py)
+
+``` python
+from ultralytics import YOLO
+from json import dumps
+
+checkpoint_path = "path/to/model/weight.pt" # e.g weights/best.pt in this directory
+model = YOLO(checkpoint_path)
+
+image_path = "path/to/image"
+infered = model(image_path)
+results = infered[0]
+
+boxes = result.boxes.data[:,:4]
+confs = result.boxes.conf
+clss = result.boxes.cls
+class_name = result.names
+
+#detected = results[0].boxes.xywh # or xywhn, xyxy pr xyxyn
+
+detections = []
+threshold = 0.3 # 0 < threshold <= 1
+
+for box, conf, cls in zip(boxes, confs, clss):
+    label = class_name[int(cls)]
+    if conf >= threshold:
+        # must be in this format
+        detections.append({
+            'confidence': str(float(conf)),
+            'label': label,
+            'points': box.tolist(),
+            'type': 'rectangle',
+        })
+
+detected_objects = dumps(detections)
+print(detected_objects)
+                        
+```