mowenbin815

mowenbin815

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gpt_academic

为GPT/GLM等LLM大语言模型提供实用化交互接口,特别优化论文阅读/润色/写作体验,模块化设计,支持自定义快捷按钮&函数插件,支持Python和C++等项目剖析&自译解功能,PDF/LaTex论文翻译&总结功能,支持并行问询多种LLM模型,支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

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download

Lantern官方版本下载 蓝灯 翻墙 代理 科学上网 外网 加速器 梯子 路由 proxy vpn circumvention gfw

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pytorch-seq2seq

An Implementation of Encoder-Decoder model with global attention mechanism.

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Double-Branch-Dual-Attention-Mechanism-Network

Hyperspectral Image Classification

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pytorch-attention-mechanism

my codes for learning attention mechanism

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pytorch-beginner

pytorch tutorial for beginners

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pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch:入门与实战》)

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hyperopt-doc-zh

Github开源项目hyperopt系列的中文文档,以及学习教程等

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islandloss

islandloss 复现

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Awesome-pytorch-list-CNVersion

Awesome-pytorch-list 翻译工作进行中......

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facial-expression-recognition-svm

Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset

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HOG_SVM

使用HOG+SVM进行图像分类

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MNIST_center_loss_pytorch

A PyTorch implementation of center loss on MNIST

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pytorch-center-loss

Pytorch implementation of Center Loss

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center-loss.pytorch

center loss for face recognition

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Real-Time-Facial-Expression-Recognition-with-DeepLearning

A real-time facial expression recognition system with webcam streaming and CNN

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emotion-recognition-neural-networks

Emotion recognition using DNN with tensorflow

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facial-expression-recognition-using-cnn

Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream

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SVM-classification-localization

HoG, PCA, PSO, Hard Negative Mining, Sliding Window, Edge Boxes, NMS

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object-detector

Object Detection Framework using HOG as descriptor and Linear SVM as classifier.

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Facial-Expression-Recognition

Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).

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Facial-Expression-Recognition.Pytorch

A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset

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Facial-Expression-Recognition

A Pytorch Implementation of FER( facial expression recognition )

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