Popular GitHub Repositories for Grayscale

Keras/Tensorflow implementation of our paper Grayscale Image Colorization using deep CNN and Inception-ResNet-v2 (https://arxiv.org/abs/1712.03400)

This is the official implementation of the AAAI 2021 accepted paper "Sketch Generation with Drawing Process Guided by Vector Flow and Grayscale"

Grayscale Image Colorization with Generative Adversarial Networks. https://arxiv.org/abs/1803.05400

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.

Automatic colorization of the grayscale images

Colorization of grayscale images using CycleGAN in TensorFlow.

Convolutional Neural Network (CNN) was trained on 48x48 pixel grayscale images to predict 5 different emotions from images. Ten different models with different settings were trained to find the best model and The best model was able to predict 5 emotions from images with 88% training accuracy and 70% testing accuracy.

It is a simple machine learning algorithm to convert Grayscale Images to Colored Images. It uses VGG-16 model. We have finetuned this model and made it accurate for this algorithm to convert images from Grayscale to RGB.

Conditional Generative Adversarial Networks for Image Colorization

PictureColorDiffusion is a program that automate 2d colorization of grayscale drawings using Automatic111 Stable Diffusion's WebUI API, it's interrogation feature and the controlnet extension. Additional features such as YoloV8 segmentation are also available. Specifiec modes for coloring manga and drawing are available.

Translates a grayscale image as series of weight-axis variations

Convolutional neural network code for colorization and up-scaling of grayscale flowers images.

A customizable GAN that is based on the DC-GAN architecture. Can be used to generate grayscale or colored images.

This repository provides a demonstration of a deep learning-based system for detecting melanoma from grayscale images. The model predicts whether an input image is classified as "BENIGN" or "MELANOMA," along with a confidence score. Please note that this application is for educational purposes only and should not be used for real medical diagnosis.

Identification of Species of Medicinal Leaves using a pre-trained CNN model

K-Pop Idol Classification: Computer vision project using fine-tuned Vision Transformers (ViT) to identify K-pop idols from TOMORROW X TOGETHER. Features YOLOv8 face detection, grayscale preprocessing technique improving accuracy from 60% to 85%, and interactive Gradio demo interface.

K-Pop Idol Classification: Computer vision project using fine-tuned Vision Transformers (ViT) to identify K-pop idols from TOMORROW X TOGETHER. Features YOLOv8 face detection, grayscale preprocessing technique improving accuracy from 60% to 85%, and interactive Gradio demo interface.

A Python project for converting RGB images to grayscale and binary formats. Uses luminance calculation for grayscale conversion and thresholding for binary conversion. Includes functions to save the processed images. Built with NumPy and PIL (Python Imaging Library).