#计算机科学#PyTorch implementation of the U-Net for image semantic segmentation with high quality images
翻译 - U-Net的PyTorch实现,用于高质量图像的图像语义分割
#计算机科学#Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
翻译 - 将Darknet转换为张量流。加载经过训练的权重,使用张量流进行再训练/微调,将常量图形def导出到移动设备
#计算机科学#Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
翻译 - 最新的深度学习与计划的项目和练习https://www.udacity.com/course/deep-learning-nanodegree--nd101
#计算机科学#Differentiable architecture search for convolutional and recurrent networks
翻译 - 卷积和递归网络的可微体系结构搜索
#大语言模型#Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
#计算机科学#Image Deblurring using Generative Adversarial Networks
#计算机科学#Paper Lists for Graph Neural Networks
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Ari...
翻译 - 基于pytorch的模型压缩(1,量化:8/4 / 2bits(dorefa),三进制/二进制值(twn / bnn / xnornet); 2,修剪:常规,常规和组卷积通道修剪; 3,组卷积结构; 4,特征(A)的二进制值的分批归一化折叠)
#计算机科学#PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
翻译 - 完全卷积网络的PyTorch实现。 (可提供重现原始结果的训练代码。)
#计算机科学#Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
#计算机科学#Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results
翻译 - pytorch库,具有最新的架构,预训练的模型和实时更新的结果
CNN visualization tool in TensorFlow
Evaluation of the CNN design choices performance on ImageNet-2012.
#计算机科学#Fully Convlutional Neural Networks for state-of-the-art time series classification
#计算机科学#real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)
#计算机科学#A self driving toy car using end-to-end learning
#计算机科学#High-quality Neural Networks for Computer Vision 😎
#计算机科学#U-Net: Convolutional Networks for Biomedical Image Segmentation