Update README.md

README.md

@@ -29,34 +29,32 @@ The model was trained for 100 epochs with a batch size of 16 using dual NVIDIA T
 # Example Usage
 
 ```python
-# load libraries
+# Load libraries
 import cv2
 from ultralytics import YOLO
-
 from pathlib import Path
 import matplotlib.pyplot as plt
-
 from huggingface_hub import hf_hub_download
 
 
-# download model
+# Download model
 model_path = hf_hub_download(repo_id="Daniil-Domino/yolo11x-text-detection", filename="model.pt")
 
-# load model
+# Load model
 model = YOLO(model_path)
 
-# inference
+# Inference
 image_path = "/path/to/image"
 image = cv2.imread(image_path).copy()
 output = model.predict(image, conf=0.3)
 
+# Draw bounding boxes
 out_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
 for data in output[0].boxes.data.tolist():
     xmin, ymin, xmax, ymax, _, _ = map(int, data)        
-    
     cv2.rectangle(out_image, (xmin, ymin), (xmax, ymax), color=(0, 0, 255), thickness=3)
-    
+
+# Display result
 plt.figure(figsize=(15, 10))
 plt.imshow(out_image)
 plt.axis('off')
@@ -64,8 +62,7 @@ plt.show()
 ```
 
 # Metrics
-Below are the key metrics from the model evaluation on the validation set:
-
+Below are the key evaluation metrics on the validation set:
 - **Precision**: 0.929
 - **Recall**: 0.937
 - **mAP50**: 0.966