Training a Classifier — PyTorch Tutorials 1.10.1+cu102 ...
https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.htmlThis provides a huge convenience and avoids writing boilerplate code. For this tutorial, we will use the CIFAR10 dataset. It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of …
CIFAR-10 Classifier Using CNN in PyTorch - Stefan Fiott
https://www.stefanfiott.com/.../cifar-10-classifier-using-cnn-in-pytorch30.11.2018 · CIFAR-10 The CIFAR-10 data set is composed of 60,000 32x32 colour images, 6,000 images per class, so 10 categories in total. The training set is made up of 50,000 images, while the remaining 10,000 make up the testing set. The categories are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck.
GitHub - H-Aliqulu/Cifar-10_with-PyTorch
https://github.com/H-Aliqulu/Cifar-10_with-PyTorch23 timer siden · CIFAR-10 Image Recognizer with PyTorch. CIFAR-10 is a famous dataset consisting of 60,000 32 x 32 color images in 10 classes ('airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') with 6,000 images per class. I used the CIFAR-10 dataset to train a feed forward neural network to recognize the primary object in images.
[PyTorch] Tutorial(5) How to train a model to classify CIFAR ...
clay-atlas.com › us › blogApr 25, 2021 · Since PyTorch's datasets has CIFAR-10 data, it can be downloaded here without having to set it manually. If there is no data folder existed in the current directory, a folder will be created automatically and the CIFAR-10 data will be placed in it. In addition, batch_size can actually be adjusted by yourself, but the highest accuracy I have ...
CIFAR-10 Classifier Using CNN in PyTorch
www.stefanfiott.com › machine-learning › cifar-10Nov 30, 2018 · In this notebook, we trained a simple convolutional neural network using PyTorch on the CIFAR-10 data set. 50,000 images were used for training and 10,000 images were used to evaluate the performance. The model performed well, achieving an accuracy of 52.2% compared to a baseline of 10%, since there are 10 categories in CIFAR-10, if the model ...