The CIFAR-100 dataset This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (...
Classify the CIFAR10 dataset using a custom made k Nearest Neighbor classifier - pillairamdas/CIFAR10_kNN
This repository contains some of the latest data augmentation techniques and optimizers for image classification using pytorch and the CIFAR10 dataset - etetteh/sota-data-augmentation-and-optimizers
In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture...
(0.5,)) ]) # 加载数据集 train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)...
We test our activation function on the standard MNIST and CIFAR-10 datasets, which are classification problems, as well as on a novel Computational Fluid Dynamics(CFD) dataset which is posed as a regression problem. On the baseline network for the MNIST dataset, having two hidden layers, our ...
We show that our model achieves an error rate of 3.73% and 19.45% on CIFAR-10 and CIFAR-100 respectively, that outperforms almost all of the existing models. We also demonstrate that our model outperforms very deep residual networks by 0.22% (top-1 error) on the full ImageNet 2012 ...
2024-12-26 05:10:37 积分:1 人脸口罩分类数据集(Face-Mask-Classification-Dataset,FMCD)_Face-Mask- 2024-12-26 05:10:33 积分:1 svipchao 2024-12-26 05:10:06 积分:1 基于pytorch的Vision_Transformer(VIT)复现,实现了CIFAR10数据集的_ 2024-12-26 05:04:18 积分:1 本...
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])# 注意下面代码中:训练的 shuffle 是 True,测试的 shuffle 是 false# 训练时可以打乱顺序增加多样性,测试是没有必要trainset= torchvision.datasets.CIFAR10(root='./data', train=True,download=True, transform=transform)trainloader= torch.utils.data...
(45%). Director or Release Date were far behind around 10% each. It is not surprising, since most people I know don't care who the director is. Lots of US blockbusters don't even mention it on the movie poster. I am aware that collaborative filtering is based on user proximity , ...