Neural-IMage-Assessment

Image Assessor

Trains neural networks to assess image aesthetics using pre-trained models and custom datasets

A PyTorch Implementation of Neural IMage Assessment

GitHub

538 stars
5 watching
95 forks
Language: Python
last commit: about 3 years ago
computer-visionimage-enhancementmachine-learningphoto-editing

Related projects:

Repository Description Stars
truskovskiyk/nima.pytorch Assesses and evaluates images using deep learning models 335
atiyo/deep_image_prior Reconstructs images using untrained neural networks to manipulate and transform existing images 216
felixgwu/img_classification_pk_pytorch A PyTorch project for comparing image classification models and facilitating quick experiment setup 366
d-li14/regnet.pytorch An implementation of a PyTorch-based neural network architecture for image classification tasks. 68
zwx8981/dbcnn A deep learning-based system for assessing the quality of images without their original references. 108
ypxie/pytorch-neucom An implementation of the Differentiable Neural Computer architecture in PyTorch 94
owenzlz/deepimageblending A PyTorch implementation of blending images by optimizing a Poisson loss with style and content loss 432
misaogura/flashtorch Toolkit for visualizing neural network behavior in PyTorch 735
jiupinjia/neural-magic-eye Training an image processing neural network to recover depth and content from autostereograms 72
ocampor/image-quality Library providing a set of tools and algorithms for evaluating the quality of digital images 401
kaiyangzhou/dassl.pytorch A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. 1,227
bestivictory/ilgnet A deep learning-based framework for image aesthetics assessment using a convolutional neural network structure 112
ethanhe42/u-net A convolutional neural network architecture for biomedical image segmentation 427
akanimax/pro_gan_pytorch Implementation of a deep learning model for generating high-quality images with improved stability and variation. 537
zcyang/imageqa-san This project provides code for training image question answering models using stacked attention networks and convolutional neural networks. 108