Update README.md

README.md

@@ -13,29 +13,29 @@ pipeline_tag: image-segmentation
 
 ## Overview
 
-This model demonstrates a road segmentation implemented using deep learning techniques which predics the road regions in the input image and returns it in a grayscale image.
+This model demonstrates a road segmentation implemented using **deep learning** techniques which predicts the road regions in the input image and returns it in a grayscale format.
 
 ## Model Structure
- - **rs1-low.pth**: The lowest performer model with a loss of 0.69%.
- - **rs1-high.pth**: The highest performer model with a loss of 0.07%.
+ - **rs1-low.pth**: The lowest performer model with a loss of **0.69%**.
+ - **rs1-high.pth**: The highest performer model with a loss of **0.07%**.
 
 ## Features
 1. ### Architecture
-    - Basic Resnet50 model with few upsampling and batch normalisation layers.
-    - Contains over _____ Trainable paramameters.
-    - Training Duration: 1 hour.
+    - Basic **Resnet50** model with few upsampling and batch normalisation layers.
+    - Contains over ** ** Trainable paramameters.
+    - Training Duration: **1 hour**.
      ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6787e9bb4151553bf9307186/rdnXew3tWUVGoKXhRK8SX.png)
 2. ### Training Data
     - Source: ([bnsapa/road-detection](https://huggingface.co/datasets/bnsapa/road-detection))
-    - Format: The dataset includes RGB images of roads around the globe and thrie corresponding segment and lane.
+    - Format: The dataset includes RGB images of roads around the globe and their corresponding segment and lane.
     - Preprocessing: With the help of torch and torchvission api basic preprocessing like resizing and convertion to tensor were implemented.
 3. ### CostFunctions Score
     - BCE:
     - MSE:
 
 ## License
-This project is licensed under the Apache License 2.0.
+This project is licensed under the **Apache License 2.0**.
 
 ## Acknowledgments
- - Apple M1 Pro 16gb of unfied memory for efficient GPU acceleration during model training
- - Pytorch for robust deep learning framework 
+ - **Apple M1 Pro 16gb** of unfied memory for efficient GPU acceleration during model training
+ - **Pytorch** for robust deep learning framework