pytorch-cifar10 | 10 dataset consists of 60000 32x32 colour images | Computer Vision library

by icpm Python Version: Current License: Apache-2.0

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kandi X-RAY | pytorch-cifar10 Summary

pytorch-cifar10 is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. pytorch-cifar10 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However pytorch-cifar10 build file is not available. You can download it from GitHub.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

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pytorch-cifar10 has a low active ecosystem.

It has 130 star(s) with 59 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-cifar10 is current.

Quality

pytorch-cifar10 has 0 bugs and 16 code smells.

Security

pytorch-cifar10 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-cifar10 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-cifar10 is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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pytorch-cifar10 releases are not available. You will need to build from source code and install.

pytorch-cifar10 has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

pytorch-cifar10 saves you 253 person hours of effort in developing the same functionality from scratch.

It has 614 lines of code, 57 functions and 10 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pytorch-cifar10 and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-cifar10 implemented functionality, and help decide if they suit your requirements.

Initialize inception .
Print a progress bar .
Format time .
Loads the model .
Forward convolution function .
Make a single layer layer .
Create a list of layers .
Run the main function .
Create dense layers .
3x3 convolution

Get all kandi verified functions for this library.

pytorch-cifar10 Key Features

No Key Features are available at this moment for pytorch-cifar10.

pytorch-cifar10 Examples and Code Snippets

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Community Discussions

Trending Discussions on pytorch-cifar10

IndexError: list index out of range in prediction of images

QUESTION

IndexError: list index out of range in prediction of images

Asked 2021-Jul-12 at 02:13

I am doing predictions on images where I write all classes' names and in the test folder, I have 20 images. Please give me some hint as, why I am getting error? How we can check the indices of the model?

Code

...

ANSWER

Answered 2021-Jul-12 at 02:13

Your error stems from the fact that you don't do any modification to the linear layers of your resnet model.

I suggest adding this code:

Source https://stackoverflow.com/questions/68340882

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pytorch-cifar10

You can download it from GitHub.
You can use pytorch-cifar10 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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