Neural-IMage-Assessment
by yunxiaoshi
A PyTorch Implementation of Neural IMage Assessment
AI summary
Image Assessor
Trains neural networks to assess image aesthetics using pre-trained models and custom datasets
- stars
- 539
- forks
- 95
- watching
- 5
Similar projects
Found by comparing what the projects do, not just their names.
Image evaluator
Assesses and evaluates images using deep learning models
Image transformer
Reconstructs images using untrained neural networks to manipulate and transform existing images
Image classifier
A PyTorch project for comparing image classification models and facilitating quick experiment setup
Neural net architecture
An implementation of a PyTorch-based neural network architecture for image classification tasks.
Image quality assessor
A deep learning-based system for assessing the quality of images without their original references.
Neural computer
An implementation of the Differentiable Neural Computer architecture in PyTorch
Image Blender
A PyTorch implementation of blending images by optimizing a Poisson loss with style and content loss
Feature visualization toolkit
Toolkit for visualizing neural network behavior in PyTorch
Autostereogram processor
Training an image processing neural network to recover depth and content from autostereograms
Image quality assessment library
Library providing a set of tools and algorithms for evaluating the quality of digital images
Computer Vision Toolkit
A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision.
Image aesthetic analyzer
A deep learning-based framework for image aesthetics assessment using a convolutional neural network structure
Image Segmentation Model
A convolutional neural network architecture for biomedical image segmentation
Image generator
Implementation of a deep learning model for generating high-quality images with improved stability and variation.
Image QA model
This project provides code for training image question answering models using stacked attention networks and convolutional neural networks.