Image classification dataset (fashion MNIST)

Image classification dataset (fashion MNIST) The most commonly used image classification dataset is the handwritten numeral recognition dataset MNIST[1]. However, the classification accuracy of most models on MNIST exceeds 95%. In order to more intuitively observe the differences between algorithms, we will use a data set with more complex ima ...

Posted by F.Danials on Tue, 03 May 2022 15:43:56 +0300

Li Mu hands-on learning deep learning V2-NiN model and code implementation

1. NiN LeNet, AlexNet and VGG all share a common design pattern: extract spatial structure features through a series of convolutional layers and pooling layers; and then process the representation of features through fully connected layers. The improvement of LeNet by AlexNet and VGG mainly lies in how to expand and deepen these two modules. ...

Posted by phpshift on Tue, 03 May 2022 01:39:18 +0300

Deep learning recommendation method related papers and PyTorch implementation

Note that the code in this article is written by myself according to the paper. There must be some details that are not expressed, and it is inevitable that there are errors. It is recommended to see the original code of the paper for more model details. If you find errors in the code, please correct them in the comment area I. AutoRec 1.1 ...

Posted by kit on Mon, 02 May 2022 15:29:25 +0300

The first step in Pytorch: the use of transforms

In my previous article: The first step of Pytorch: (1) Use of Dataset class Here, whether we use torchvision.datasets or we customize the Dataset subclass, there is a parameter transforms that is passed in. I didn't explain it in detail in the last article, because this is a big piece of content, so I wrote this article to explain it. transform ...

Posted by jozard on Sun, 01 May 2022 09:13:21 +0300

ResNet learning notes

preface This article is written after reading many blogs. It requires readers to have at least some CNN knowledge. Of course, I don't know what level they need. Therefore, it may partially explain some basic terms that are very introductory, or many complex terms that are not explained because they are difficult to explain (mainly lazy). It's b ...

Posted by iceblox on Sat, 30 Apr 2022 22:25:47 +0300

Practical operation -- cloud deep learning workstation configuration guide

Reprint address: https://zhuanlan.zhihu.com/p/336429888 https://mp.weixin.qq.com/s?__biz=MzI5MDUyMDIxNA==&mid=2247527704&idx=1&sn=c1234acc04c8f011c1786135a520c336&chksm=ec1c80e1db6b09f751a32bd4160120a1ddfd099c414c1e05a9a564d0224a51e9eca4ebfdd640&mpshare=1&scene=23&srcid=1222RYsP3MA07RymDXXlUioT&sharer_sharetime= ...

Posted by twister47 on Sat, 30 Apr 2022 20:52:28 +0300

Train of MMdetection Detailed explanation of Py source code

catalogue 1, Tools / train py 2, Detailed source code 3, Detailed explanation of core functions (1) build_detector(mmdet/models/builder.py) (2) build_dataset(mmdet/datasets/builder) (3) train_detector(mmdet/apis/train.py) (4) set_random_seed: (5) get_root_logger: 1, Tools / train py Optional parameters: # =========== optional ...

Posted by voitek on Sat, 30 Apr 2022 15:49:21 +0300

Super detailed explanation of CTC theory and actual combat

CTC introduction For speech recognition, the input of training data is a piece of audio and the output is its transcribed text, but we don't know how to align letters and speech. This makes training speech recognition more complex than it seems. It is very difficult and error prone for people to mark this alignment, because the boundaries ...

Posted by Poofman on Sat, 30 Apr 2022 05:37:58 +0300

Real-time face recognition and object recognition based on nodejs on Raspberry Pi 4B using tfjs-node

start I had this idea a few years ago. I wanted to be an assistant that can recognize faces and objects, and can recognize commands through simple dialogues. Similar to the offline enhanced version of Xiao Ai, I can monitor my cabin by the way. However, I was too busy at the end of the year and didn’t have the time and energy to toss. I ...

Posted by ludjer on Fri, 29 Apr 2022 15:52:37 +0300

pytorch train.py and test.py code flow

First explain train.py, including the following steps 1. Configure the training set and test set train_data = torchvision.datasets.CIFAR10(root="./dataset", train=True, transform=torchvision.transforms.ToTensor(), download=True) test_data = torchvision.datasets.CIFAR10(root="./dataset", train=False, t ...

Posted by CodeEye on Fri, 29 Apr 2022 00:13:17 +0300