Learning of Greyscale Images: How to Finetune Pretrained Models on Black-and-White

In the computer vision and image processing, competency in perceiving the divides between coloured and black-and-white images is paramount. One of the significant things to note is that pretrained models are designed to handle various image formats. To successfully integrate them into grayscale datasets, researchers must find ways to adapt the models, so they can work effectively with grayscale images. In this blog post we will look at the differences between RGB and grayscale images then proceed to analyse the effect of the pretrained models and guide you in the finetuning of the models for optimal performance on black-and-white datasets.

What is the difference between RGB and grayscale images?

When it comes to digital imaging, RGB and grayscale images play a very important role, their understanding is essential. Such differences are visual and more complex, such as how they are processed and applied in different areas, especially machine learning and computer vision. RGB images are more colorful than grayscale ones, which channel the luminance and contrast despite not having color information. Here, we will look at the fundamental features of both RGB (red, green, blue) and grayscale images by demonstrating how these images are constructed and which of them are used for which applications.

RGB Images

RGB images, also known as color images, are composed of three channels: red, green, and blue. Every pixel in an RGB image contains three numbers that determine how these primary colors are rendered in the varied hues it can create. When you make an image black and white or convert it into black and white, the information from the three channels is compressed into a single channel.

Understanding Component Intensities

In RGB images, from 0 to 255, each color channel represents the intensity. A black pixel with no color is a (0, 0, 0) value, and a white pixel with nothing but pure white is a (255, 255, 255) value. Through adjusting the intensity of red, green, and blue channels you can not only produce any color in RGB color spectrum but also range of other colors that does not have pure shades of red, green, and blue hues.

Greyscale Images

On the other hand, grayscale images can use just a fixed channel. Each pixel in the grayscale image depicts the color gray, which varies from the darkest shade to the whitest one. The brightness of each pixel is assigned its unique scalar value, usually ranging from 0 (black) to 255 (white). This is exactly what occurs when you use such an online service to make an image black and white. You bring down the color information to one dimension.

Why does this affect pre-trained models?

Generally, pre-trained models, including those used for object detection or image classification, are mostly trained with large sets of datasets full of RGB images. Such models are capable of discerning patterns and features, and they just use the information represented by color in the training set. In the case of converting to black and white, these pre-trained models may not produce correct object identification or image classification results.

How does the number of channels affect filters?

Filters of convolutional neural networks (CNNs) are applied to find or obtain distinctive characteristics of images. The number of channels present in an image will be matched by the shape of the filter. For RGB images, the filters are mostly 3D, in other words it implies (height, width, channels). However, within the grayscale, the filter becomes 2D, meaning one has (rows and columns) as there are only two dimensions. Check out our solutions today and see how easy it is to integrate greyscale into your workflows.

How to use greyscale images with pretrained models

To make image black and white with pretrained models, you have two main options:

Convert grayscale images to RGB: Apply a black-and-white converter or the image to a black-and-white filter to convert your grayscale images to RGB. For this purpose, the single-channel luminance values are replicated to all RGB color channels, thus leading to an image where shades of gray are reproduced instead of colored pixels.

Modify the pretrained model: You can redesign the pretrained model's image input structure to get single-channel grayscale input images. This involves changing the net's input layer and probably shaping the filter dimensions everywhere.

Adding additional channels to greyscale images

Whether it is adding additional channels or using simple interpolation, working with grayscale images and pretrained models is another path worth exploring. This can be done using such strategies, including image stacking and pseudo-color channeling.

  • The image stacking technique performs this function by merging several gray images into a single multi-channel image, with all the channels resulting from the deepening of the data input.
  • Pseudo-color channels can be obtained by employing some transformations or filters to the original grayscale image. This will produce color information that a pre-trained model can use to learn important features and transfer the learning from grayscale to the color domain.


Employing black and white image converter along with pre-trained models could easily turn into a learning process. With the expertise on interpreting the differences between RGB and black & white photographs, and knowing what precautions to take, we can build reliable models with or without sophisticated image data. If you are still struggling to develop your skills in black-and-white image conversion or want to add more tools and techniques to the black-and-white image converter capabilities check our Grayscale Image tool where you can enjoy several systems for making images black and white online and, with time, progressing your projects.

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