hurryup186

hurryup186

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DPIS_SSVI-ReID

Dual Pseudo-Labels Interactive Self-Training for Semi-Supervised Visible-Infrared Person Re-Identification

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PMT

Code for paper ( AAAI-2023)

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UNIReID

Towards Modality-Agnostic Person Re-identification with Descriptive Query CVPR2023

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DeepLearning

A deep learning code base, mainly for paper replication, in the areas of image recognition, object detection, image segmentation, self-supervision, etc. Each project can be run independently, and there are corresponding articles to explain.

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yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

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yolov9

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

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Cross-Modal-Re-ID-baseline

Pytorch Code for Cross-Modality Person Re-Identification (Visible Thermal/Infrared Re-ID)

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CoOp

Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)

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CLIP-ReID

Official implementation for "CLIP-ReID: Exploiting Vision-Language Model for Image Re-identification without Concrete Text Labels" (AAAI 2023)

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TOP-ReID

【AAAI2024】TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation

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GPT_API_free

Free ChatGPT API Key,免费ChatGPT API,支持GPT4 API(免费),ChatGPT国内可用免费转发API,直连无需代理。可以搭配ChatBox等软件/插件使用,极大降低接口使用成本。国内即可无限制畅快聊天。

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MetaTransformer

Meta-Transformer for Unified Multimodal Learning

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LUPerson

Unsupervised Pre-training for Person Re-identification (LUPerson)

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GUR

The Implementation of our ICCV 2023 paper: Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification.

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pytorch-image-models

Forked from https://github.com/rwightman/pytorch-image-models

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imageLabeler-API

Convenient image annotation tool API

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yolov3-yolov4-matlab

Import and export Darknet™ models within MATLAB deep learning networks.

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opencv

Open Source Computer Vision Library

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cocoAPI

Simple, efficient and convenient!

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PaddleX

Low-code development tool based on PaddlePaddle(飞桨低代码开发工具)

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retinanet-pytorch

这是一个retinanet-pytorch的源码,可以用于训练自己的模型。

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yoloX-yoloV5-onnx-matlab

Simple reasoning code, just run demo.mlx

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