Update README.md

README.md

@@ -85,50 +85,40 @@ Users should be informed about the model's limitations and potential biases. Fur
 
  Load model and perform prediction:
 
+## How to Get Started with the Model
+To get started with the YOLOv8s object Detection and Classification model, follow these steps:
 
-### How to use
-
-- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
+1. Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus) and [ultralytics](https://github.com/ultralytics/ultralytics) libraries using pip:
 
 ```bash
-pip install -U yolov5
+pip install ultralyticsplus ultralytics
 ```
 
-- Load model and perform prediction:
+2. Load the model and perform prediction using the provided code snippet.
 
 ```python
-import yolov5
+from ultralyticsplus import YOLO, render_result
 
 # load model
-model = yolov5.load('foduucom/object_detection')
-  
+model = YOLO('foduucom/plant-leaf-detection-and-classification')
+
 # set model parameters
-model.conf = 0.25  # NMS confidence threshold
-model.iou = 0.45  # NMS IoU threshold
-model.agnostic = False  # NMS class-agnostic
-model.multi_label = False  # NMS multiple labels per box
-model.max_det = 1000  # maximum number of detections per image
+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
-img = path
+image = 'path/to/your/image'
 
 # perform inference
-results = model(img, size=640)
-
-# inference with test time augmentation
-results = model(img, augment=True)
-
-# parse results
-predictions = results.pred[0]
-boxes = predictions[:, :4] # x1, y1, x2, y2
-scores = predictions[:, 4]
-categories = predictions[:, 5]
-
-# show detection bounding boxes on image
-results.show()
+results = model.predict(image)
 
-# save results into "results/" folder
-results.save(save_dir='results/')
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
 ```
 
 - Finetune the model on your custom dataset: