yolov8s

app.py

@@ -1,4 +1,21 @@
-import streamlit as st
+from ultralyticsplus import YOLO, render_result
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# 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
+image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
+
+# perform inference
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()