Kang's starred repositories
pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
MAE-pytorch
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners
IQA-PyTorch
👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, TOPIQ, NIMA, DBCNN, BRISQUE, PI and more...
Awesome-Image-Composition
A curated list of papers, code and resources pertaining to image composition/compositing or object insertion, which aims to generate realistic composite image.
Awesome-Image-Quality-Assessment
A comprehensive collection of IQA papers
NSFC-application-template-latex
国家自然科学基金申请书正文(面上项目)LaTeX 模板(非官方)
stable-diffusion-aesthetic-gradients
Personalization for Stable Diffusion via Aesthetic Gradients 🎨
TANet-image-aesthetics-and-quality-assessment
[IJCAI 2022, Official Code] for paper "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks". Official Weights and Demos provided. 首个面向多主题场景的美学评估数据集、算法和benchmark.
MAE-pytorch
Masked Autoencoders Are Scalable Vision Learners
A01_cvclass_basic
resources for CV
segmentation-paper-reading-notes
segmentation paper reading notes
MUAD-Dataset
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks [BMVC 2022]
DPC-Captions
A image caption dataset about images from www.dpchallenge.com.
troubleshooting_BIQA
This is the official released code for troubleshooting BIQA in the wild
Video-Summarization-Using-Attention-Based-Encoder-Decoder-Model
In this model we uses Attention Based model to summarize the video. The output of the video will be 15% of the original video as summary. Also It's a Supervised Model.