💬 Language Identification with Support for More Than 2000 Labels -- EMNLP 2023

Natural Language Processing Tutorial for Deep Learning Researchers

PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDN…

This is a warehouse for MobileNetV4-Pytorch-model, can be used to train your image-datasets for vision tasks.

Deep learning with pytorch library

The official gpt4free repository | various collection of powerful language models | o4, o3 and deepseek r1, gpt-4.1, gemini 2.5

flask后端开发接口示例，利用Flask开发后端API接口。包含基本的项目配置、统一响应、MySQL和Redis数据库操作、定时任务、图片生成、项目部署、用户权限认证、报表输出、无限层级生成目录树、阿里云手机验证码验证、微信授权、Celery、单元测试、Drone等模块。

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

Source code for the LabelMe annotation tool.

Component library based on element-plus secondary encapsulation

Single Page App with Flask and Vue.js

基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型

OpenMMLab Detection Toolbox and Benchmark

Few Shot Semantic Segmentation Papers

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Official PyTorch Implementation of Learning What Not to Segment: A New Perspective on Few-Shot Segmentation (CVPR'22 Oral & TPAMI'23).

Codes for "Learning Non-target Knowledge for Few-shot Semantic Segmentation", accepted by CVPR 2022.

yolov3 by pytorch

torchbearer: A model fitting library for PyTorch

COMP6200 MSc Project

基于Gin + Vue + Element UI & Arco Design & Ant Design 的前后端分离权限管理系统脚手架（包含了：多租户的支持，基础用户管理功能，jwt鉴权，代码生成器，RBAC资源控制，表单构建，定时任务等）3分钟构建自己的中后台项目；项目文档》：https://www.go-admin.pro V2 Demo： https://vue2.go-admin.d…

ExampleAgent for Intelligent Systems Module in UoS COMP2208

Scene recognition using multiple feature extractors (tiny-images, D-SIFT, BoVW, PHoW) and different classifiers (KNN, SVM).