afternoonzhou's repositories

attention-ocr

A Tensorflow model for text recognition (CNN + seq2seq with visual attention) available as a Python package and compatible with Google Cloud ML Engine.

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caffe

Caffe: a fast open framework for deep learning.

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capsule-networks

A Tensorflow implementation of Capsule Networks

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CNN_LSTM_CTC_Tensorflow

CNN+LSTM+CTC based OCR implemented using tensorflow.

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crack_segmentation

This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu

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CRNN_Attention_OCR

CRNN with attention to do OCR

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hand_detection

A Light CNN based Method for Hand Detection and Orientation Estimation

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ImageLabeler

基于Qt实现的图片数据标注工具

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keras_to_tensorflow

General code to convert a trained keras model into an inference tensorflow model

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Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

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MobileNet-SSD

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

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models

Models built with TensorFlow

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My-TensorFlow-tutorials

This repo contains all of my TensorFlow tutorials

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opencv_example

some examples of OpenCV

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opencv_ops_according_to_halcon

使用opencv实现部分halcon算子的功能

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OpencvDevelop

Qt+opencv制作的类似halcon的软件

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OpenHalcon

OpenSource Industrial Level Machine Vision Platform

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Person-reID_GAN

ICCV2017 Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

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pytorch-CycleGAN-and-pix2pix

Image-to-image translation in PyTorch (e.g. horse2zebra, edges2cats, and more)

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PyTorch-Tutorial

Build your neural network easy and fast

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Retina-Unet

眼底图像的血管分割

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Tensorflow-Tutorial

Tensorflow tutorial from basic to hard

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text-detection-ctpn

text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network

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tf-rnn-attention

Tensorflow implementation of attention mechanism for text classification tasks.

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unet

用caffe实现Unet

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VisionEdit

基于Halcon的一款通用图像处理工具

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YOLOv3-model-pruning

对 YOLOv3 做模型剪枝,在 oxford hand 数据集上模型的参数量减少 80% ,FLOPs 降低 70%,Infer 的速度可以达到原来的 200%,mAP 基本保持不变

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