add ultralytics model card

README.md

@@ -8,8 +8,9 @@ tags:
 - vision
 - image-segmentation
 - pytorch
+- awesome-yolov8-models
 library_name: ultralytics
-library_version: 8.0.6
+library_version: 8.0.21
 inference: false
 
 datasets:
@@ -28,10 +29,10 @@ model-index:
 
     metrics:
       - type: precision  # since mAP@0.5 is not available on hf.co/metrics
-        value: 0.00706  # min: 0.0 - max: 1.0
+        value: 0.995  # min: 0.0 - max: 1.0
         name: mAP@0.5(box)
       - type: precision  # since mAP@0.5 is not available on hf.co/metrics
-        value: 0.00456  # min: 0.0 - max: 1.0
+        value: 0.995  # min: 0.0 - max: 1.0
         name: mAP@0.5(mask)
 ---
 
@@ -47,16 +48,16 @@ model-index:
 
 ### How to use
 
-- Install [ultralytics](https://github.com/ultralytics/ultralytics) and [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
+- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
 
 ```bash
-pip install -U ultralytics ultralyticsplus
+pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
 ```
 
 - Load model and perform prediction:
 
 ```python
-from ultralyticsplus import YOLO, render_model_output
+from ultralyticsplus import YOLO, render_result
 
 # load model
 model = YOLO('keremberke/yolov8n-pothole-segmentation')
@@ -71,10 +72,12 @@ model.overrides['max_det'] = 1000  # maximum number of detections per image
 image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
 
 # perform inference
-for result in model.predict(image, return_outputs=True):
-    print(result["det"]) # [[x1, y1, x2, y2, conf, class]]
-    print(result["segment"]) # [segmentation mask]
-    render = render_model_output(model=model, image=image, model_output=result)
-    render.show()
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+print(results[0].masks)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
 ```