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SylviaHolic

SylviaHolic

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tensorrt_deploy

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BaiduPCS-Go

iikira/BaiduPCS-Go原版基础上集成了分享链接/秒传链接转存功能

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synthtiger

Official Implementation of SynthTIGER (Synthetic Text Image Generator), ICDAR 2021

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Transformer_STR

PyTorch implementation of my new method for Scene Text Recognition (STR) based on Transformer,Equipped with Transformer, this method outperforms the best model of the aforementioned deep-text-recognition-benchmark by 7.6% on CUTE80.

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up-detr

[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

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Office

主要应用于解决日常的OFFICE相关问题，如word/pdf/image/txt的转换、分割、合并；pdf/img中表格的抽取；pdf文档的翻译及输出等

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ScaledYOLOv4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

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ScaledYOLOv4

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DetectoRS

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

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copy-paste-aug

Copy-paste augmentation for segmentation and detection tasks

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automl

Google Brain AutoML

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ictext_eval

Official evaluation code for the ICDAR 2021 ICText Challenge.

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MASTER-TF

MASTER

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maskrcnn-benchmark-expansion

expansion of maskrcnn-benchmark

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DB

A PyTorch implementation of "Real-time Scene Text Detection with Differentiable Binarization".

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research-charnet

CharNet: Convolutional Character Networks

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opencv-build-vs

Visual Studio project files for OpenCV – no need to use CMake

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ipp-resize

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Text-Image-Augmentation

Geometric Augmentation for Text Image

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Scene-Text-Recognition

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Scene-Text-End2end

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MaskTextSpotter

A PyTorch implementation of Mask TextSpotter

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TensorRT

NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

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Scene-Text-Detection

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ImprovedORN

Oriented Response Networks, with a little improvement

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ORN

Oriented Response Networks, in CVPR 2017

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SynthText3D

Project page of SynthText3D

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Auto-PyTorch

Automatic architecture search and hyperparameter optimization for PyTorch

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amc-models

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

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pytorch-slimming

Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

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