Depends on what you wish to achieve; if you want to achieve a neural network to predict on grayscale images, then you must train your neural network with grayscale images only. On the contrary, if you want you network to predict on RGB images, then you must train the network with RGB images.

So the network must be trained accordingly.

To convert images to grayscale, just use opencv:

grayscale_image = cv2.cvtColor(RGB_image, cv2.COLOR_BGR2GRAY)

Tahmid Chowdhury I want to predict on grayscale but my question is that my dataset is in RGB form , should i convert it to grayscale and then feed to NN or is there any method that it can be changed from RGB to grayscale automatically while passing to NN. Like can i do it using OpenCV or any other library?

I agree with Tahmid Chowdhury, if you want to predict something based on grayscale image, then you should transform your RGB image into grayscale before feed them into your neural network.

I think it's better to convert your dataset in advance so you don't have to transform the image multiple times in different batch.

Aside from OpenCV mentioned above, you can also check scikit-image, which provides some useful utilities such as rgb2gray to transform RGB into greyscale image (https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_rgb_to_gray.html)

Muhammad Asad Javed yes you need to convert to grayscale for training as well. As for converting all your RGBs to grayscale, you can follow opencv as i mentioned above or use scikit learn as mentioned by Ping-Han Hsieh .

Its just 3-4 lines of code in a for loop to convert them to grayscale and save in a folder; the execution won't take any time at all (depends on how large your dataset is of course)

OK. Thank you very much Tahmid Chowdhury and Ping-Han Hsieh