OpenMMLab Pre-training Toolbox and Benchmark

A treasure chest for visual classification and recognition powered by PaddlePaddle

OpenMMLab Detection Toolbox and Benchmark

Paddle Large Scale Classification Tools，supports ArcFace, CosFace, PartialFC, Data Parallel + Model Parallel. Model includes ResNet, ViT, Swin, DeiT, CaiT, FaceViT, MoCo, MAE, ConvMAE, CAE.

Integrate deep learning models for image classification | Backbone learning/comparison/magic modification project

PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision Transformer，DEiT，Swin Transformer，CvT，T2T-ViT，MLP-Mixer，XCiT，ConvNeXt，PVTv2 等基础视觉算法

A lightweight and extensible toolbox for image classification

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Simple study on ViT performance in medical image classification

[Ecological Informatics] TensorFlow implementation for the paper "Bag of tricks for long-tail visual recognition of animal species in camera-trap images"

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

TransMorph: Transformer for Unsupervised Medical Image Registration (PyTorch)

GRIT: Faster and Better Image-captioning Transformer (ECCV 2022)

Implementation of popular deep learning networks with TensorRT network definition API

[ACM MM 2022 - Demo] Restoration of Analog Videos Using Swin-UNet

This repository contains a CBIR system that uses swin transformer to extract image's feature.

More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt

Pytorch implementation for "A Novel Plug-in Module for Fine-Grained Visual Classification". fine-grained visual classification task.

This is the official implementation of our proposed SwinMR

SUNet: Swin Transformer with UNet for Image Denoising

This repository is for MORAI dataset training in 2D object detection with swin-transformer

"SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network" (ICRA 2023)

Implementation of a Paper related to Vision Transformer

Kaggle Competition Bronze Medal 🥉 (205th out of 3537 teams)

The initial stage of this work focuses on creating a segmented image from a given satellite image with different classes. In the second stage, these images should be converted to an ABM-friendly format. At the final stage, they can be used to simulate the real-world environment with related agents using a suitable Agent-based model.

Coursework created for the COM3001 Professional Project module during my Computer Science BSc at Surrey University

Coursework created for the COM3025 Deep Learning And Advanced AI module during my Computer Science BSc at Surrey University

An algorithm that predicts yearly Aboveground Biomass for Finnish forests using satellite imagery.

Segmentation of Brain Tumors using Vision Transformer