Pinned Repositories
1D-2D-Segmentation-AutoEncoder-TF2-KERAS
1D and 2D Segmentation Models with options such as Deep Supervision, Guided Attention, BiConvLSTM, Autoencoder, etc.
2023KD
fabric novelty detetcion
Algorithms_MathModels
【国赛】【美赛】数学建模相关算法 MATLAB实现（2018年初整理）
awesome-semantic-segmentation-pytorch
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
CatDogClassifier
Leverages feature extraction to correctly classify pictures of cats and dogs at least 97.5% of the time
CBAM-keras
CBAM implementation on Keras
deep-learning-models
Keras code and weights files for popular deep learning models.
Edge-Intelligence
随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)
Semantic-Segmentation-with-Unet
Pytorch implementation of Human(person) segmentation from RGB images.
Suface_defect_detection
kagomeAli's Repositories
kagomeAli/1D-2D-Segmentation-AutoEncoder-TF2-KERAS
1D and 2D Segmentation Models with options such as Deep Supervision, Guided Attention, BiConvLSTM, Autoencoder, etc.
kagomeAli/2023KD
fabric novelty detetcion
kagomeAli/Algorithms_MathModels
【国赛】【美赛】数学建模相关算法 MATLAB实现（2018年初整理）
kagomeAli/awesome-semantic-segmentation-pytorch
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
kagomeAli/CatDogClassifier
Leverages feature extraction to correctly classify pictures of cats and dogs at least 97.5% of the time
kagomeAli/CBAM-keras
CBAM implementation on Keras
kagomeAli/deep-learning-models
Keras code and weights files for popular deep learning models.
kagomeAli/Edge-Intelligence
随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)
kagomeAli/Semantic-Segmentation-with-Unet
Pytorch implementation of Human(person) segmentation from RGB images.
kagomeAli/Suface_defect_detection
kagomeAli/EMNLP2017_DOC
code for our EMNLP 2017 paper "DOC: Deep Open Classification of Text Documents"
kagomeAli/Grad-CAM.pytorch
pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...
kagomeAli/gradcamplusplus
keras implementation of gradcam_plus_plus
kagomeAli/introduceOfdeeplearning
kagomeAli/k3sKubeflow
kagomeAli/Keras-GAN
Keras implementations of Generative Adversarial Networks.
kagomeAli/keras-grad-cam
An implementation of Grad-CAM with keras
kagomeAli/lab1
kagomeAli/LocalViT-pytorch
LocalViT: Bringing Locality to Vision Transformers
kagomeAli/NCDSS
Novel Class Discovery in Semantic Segmentation, CVPR 2022
kagomeAli/neural-style-transfer
艺术风格转换
kagomeAli/Object-Detection-Metrics
Most popular metrics used to evaluate object detection algorithms.
kagomeAli/PaddleSeg
End-to-end image segmentation kit based on PaddlePaddle.
kagomeAli/pyimagesearch
deep learning
kagomeAli/pytorch-retinanet
Pytorch implementation of RetinaNet object detection.
kagomeAli/reco
The implementation of "Bootstrapping Semantic Segmentation with Regional Contrast".
kagomeAli/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
kagomeAli/Unsupervised-Semantic-Segmentation
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
kagomeAli/vae
a simple vae and cvae from keras
kagomeAli/web-knowledge
web 前端基础知识