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2021-cv-surveys

2021 年，计算机视觉相关综述。包括目标检测、跟踪........

3dcnn

3D convolutional neural network for video classification

action-faster-rcnn

action_recognition_using_visual_attention

TensorFlow Implementation of "Action Recognition using Visual Attention"

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

allenwangcheng.github.io

alocc-cvpr2018

Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC)

annotatedcode

知名开源代码库的注释版：C++、Golang等

awesome-nas

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-tiny-object-detection

🕶 A curated list of Tiny Object Detection papers and related resources.

book

个人认为对技术提升很不错的书

books-pdf

books pdf

caffe-model-zoo

caffe pretrained models and prototxt

caffe_rfcn_myversion

caffe_s3fd

caffe for s3fd face detection

ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

centerface.pytorch

unofficial version of centerface, which achieves the best balance between speed and accuracy at face detection

channel-pruning

Channel Pruning for Accelerating Very Deep Neural Networks

chinese-bert-wwm

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

coding-interview-university

A complete computer science study plan to become a software engineer.

compute-features

Computes features for images using various pretrained Tensorflow models

cosine-similarity-embedded-with-contrastive-loss-function

cplusplusthings

C++那些事

cpp-primer

C++ Primer 5ed answers

cross-dataset-action-detection

Implementation of a Cross Dataset framework that both detects actions in a crowded scenes and adapt Models from a Source datset to a new Target dataset with minumum annotaions needed.

cvpr2020-code

CVPR 2020 论文开源项目合集

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

deep-svdd-pytorch

A PyTorch implementation of the Deep SVDD anomaly detection method

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，近30万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06