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wyz-lxw's Projects

-2.0 icon -2.0

相比上次的管理系统本次结合了mysql数据库,实现了增删改查,并增加了一部分内容

-opencv- icon -opencv-

本项目是基于OpenCV的人脸识别技术,将一个完整的视频中自己想要了解的某个具体的人物片段,通过将视频分割成帧并且训练需要提取人物的面部特征,通过主成分分析算法进行人脸模型清洗并通过特征脸算法进行人脸识别,将该人物从视频中识别出来并将他的片段提取出来,组合成专属个人的视频剪辑,项目完成后,本产品可以使人们节省大量的时间从大量的视频片段中快速的了解自己想要了解的人物,还可以应用在监控的锁定人物中,就如《速度与激情》影片中通过天眼来快速找到某人的效果一样,快速的查找到某个人在一段时间内的全部活动。 ———————————————— 版权声明:本文为CSDN博主「CSDNFly1」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。

2018paper_code icon 2018paper_code

paper:Spectral–Spatial Unified Networks for Hyperspectral Image Classification

ai-learn icon ai-learn

人工智能学习路线图,整理近200个实战案例与项目,免费提供配套教材,零基础入门,就业实战!包括:Python,数学,机器学习,数据分析,深度学习,计算机视觉,自然语言处理,PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

amr-benchmark icon amr-benchmark

A Unified Implementation of Several Baseline Deep Learning Models for Automatic Modulation Recognition

anamde icon anamde

基于多光谱的植被数据分析

anomalygpt icon anomalygpt

[AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

bin icon bin

Blurry Video Frame Interpolation (CVPR20)

c3d-tensorflow icon c3d-tensorflow

C3D is a modified version of BVLC tensorflow to support 3D ConvNets.

chineseocr_lite icon chineseocr_lite

超轻量级中文ocr,支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M

deep_hand_pose icon deep_hand_pose

Provides the source code for the deep learning components mentioned in "Depth-based hand pose estimation: methods, data, and challenges"

deepspeed icon deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

defect-detection icon defect-detection

基于QT的缺陷检测系统,包括图像检测以及目标检测两个部分,支持ONNXRuntime加速

ecg-mit-bih icon ecg-mit-bih

ECG classification using MIT-BIH data, a deep CNN learning implementation of Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, https://www.nature.com/articles/s41591-018-0268-3 and also deploy the trained model to a web app using Flask, introduced at

fire-detection-image-dataset icon fire-detection-image-dataset

This dataset contains normal images and images with fire. It is highly unbalanced to reciprocate real world situations. It consists of a variety of scenarios and different fire situations (intensity, luminosity, size, environment etc).

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