NUAA-XSF's Projects
500 Lines or Less
Awesome Incremental Learning
Reading list for research topics in multimodal machine learning
Awesome-open-world-learning
A curated list of awesome Python frameworks, libraries, software and resources
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
An implementation of "CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model".
Dive into CPython internals, trying to illustrate every detail of CPython implementation
Convolutional Neural Networks
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
深度学习论文翻译,包括分类论文,检测论文等
码农的荒岛求生
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
:cn: GitHub中文排行榜,各语言分离设置「软件 / 资料」榜单,精准定位中文好项目。各取所需,互不干扰。
This is a Gobang game based on Pygame
this is the test repository
Pygame for Conway's Game of Life
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
PyTorch implementation of BMVC2022 paper Masked Vision-Language Transformers for Scene Text Recognition
Config files for my GitHub profile.
来自一位 Pythonista 的编程经验分享,内容涵盖编码技巧、最佳实践与思维模式等方面。
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
PyTorch implementation of moe, which stands for mixture of experts
Some tricks of pytorch... :star:
A simplified implemention of Faster R-CNN that replicate performance from origin paper