Update pages/Data Collection.py

pages/Data Collection.py

@@ -527,7 +527,6 @@ def image_details_page():
         - Used for simple image processing tasks where color isn't essential.
         - **Disadvantage**: It only preserves black and white, so other colors (like red, green, or brown) are completely lost.
         - For example, by using the image's width and height (rows and columns), we can create a **2D array** where each pixel is represented by either 0 (black) or 255 (white). 
-        - **Use case**: Binary classification problems like simple object detection, where only the presence or absence of a feature matters.
         """)
         
         # Subheading for Grayscale color space
@@ -537,7 +536,6 @@ def image_details_page():
         - Preserves brightness details but loses color information.
         - **Disadvantage**: If the image has colors like red, green, or brown, it cannot preserve those since grayscale only represents shades of gray.
         - After converting an image to grayscale, each pixel can take values from 0 (black) to 255 (white), with every intermediate value representing a shade of gray.
-        - **Use case**: Applications where only intensity (brightness) matters, like edge detection or certain medical imaging applications.
         """)
         
         # Subheading for RGB color space
@@ -548,7 +546,6 @@ def image_details_page():
         - By mixing different intensities of red, green, and blue, you can create over **16 million possible colors**.
         - This is the most commonly used color space for colored images and is widely used in digital displays, cameras, and image processing tasks.
         - In RGB color space, each pixel is represented by three values, one for each channel (Red, Green, Blue). The image is represented as a **3D array** where each pixel has three values (R, G, B).
-        - **Disadvantage**: It requires more data (3 values per pixel), which can be computationally intensive.
         """)
         
         # Connecting the concepts