based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment C

[CVPRW oral 2022] MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

The code repo contains multiple code reproduction processes of various SOTA deep learning algorithms

Question Answering dataset generator of Document Visual in English and Chinese

Official implementation for SparseSwin: Swin transformer with sparse transformer block

keras implementation of custom backbones

This study presents a novel multimodal fusion technique for disaster identification in Bangla, combining text and image data using the "BanglaCalamityMMD" dataset. Employing DisasterTextNet, DisasterImageNet, and DisasterMultFusionNet, the approach addresses a key gap in Bangla disaster research.

Pose Estimation using Swin-Transformer based on DARK Pose.

This repository contains code used to perform image retrieval using transformers. It is a demonstration of how using vision transformers, metric learning, and a novel loss based on differential cross-entropy can lead to better retrieval than classical CNN-based methods.

Finetune Swin-Transformer to estimate 3d hand post from RGB images

This project aims to detect COVID-19 cases from chest X-ray images using the Swin Transformer model.

A deep learning project that classifies seven types of skin lesions using the HAM10000 dataset. Among four tested models, Swin Transformer achieved the best accuracy of 88.9%, showing AI’s potential in early skin cancer detection.

This Repo contains code for used for age and gender detection in CCTV videos

PyTorch implementation of Swin Transformer from scratch

A comprehensive guide to the Swin Transformer architecture, including implementation details, visualizations, and demos.

This study presents a hybrid multimodal fusion technique for disaster identification in Bangla, combining text and image data using the "BanglaCalamityMMD" dataset. Employing DisasterTextNet, DisasterImageNet, and DisasterMultFusionNet, the approach addresses a key gap in Bangla disaster research.