Action-Recognition | Course Project for CS763 Computer Vision IIT Bombay | Computer Vision library

by Naman-ntc Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Action-Recognition Summary

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

Course Project for CS763 Computer Vision IIT Bombay

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Quality

Security

License

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Support

Action-Recognition has a low active ecosystem.

It has 29 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Action-Recognition is current.

Quality

Action-Recognition has no bugs reported.

Security

Action-Recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Action-Recognition is licensed under the MIT License. This license is Permissive.

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

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

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

Top functions reviewed by kandi - BETA

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

Train the model
Constructs a ResNet Resnet model
Check accuracy
Evaluate the given model
Evaluate accuracy
Scheduler
Compute validation accuracy
Plot a loss
Evaluate the test accuracy
Generate Gendata
Print toolbar
Read the coordinates of a skeleton skeleton file
Read skeleton from a skeleton file
Reads the coordinates of a skeleton file
Save model to path
Check the validation accuracy
Plot the loss
Construct a CNN resnet
Concatenate data
Data generator
Funcate a 3d array
Calculates train and test split between examples
Schedule the learning rate

Get all kandi verified functions for this library.

Action-Recognition Key Features

No Key Features are available at this moment for Action-Recognition.

Action-Recognition Examples and Code Snippets

No Code Snippets are available at this moment for Action-Recognition.

Community Discussions

Trending Discussions on Action-Recognition

Expected object of scalar type Long but got scalar type Byte for argument #2 'target'

QUESTION

Expected object of scalar type Long but got scalar type Byte for argument #2 'target'

Asked 2019-Oct-15 at 21:13

I am running a nn on colab and came across this error which was not there when i ran the same code on my local system. I have tried with reduced batch size too but the error still persists.

...

ANSWER

Answered 2019-Apr-19 at 14:04

The title of your question is telling what is causing this error. The target should have type torch.LongTensor, but it is instead torch.ByteTensor. Before calling nll_loss do:

Source https://stackoverflow.com/questions/55762581

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

Vulnerabilities

No vulnerabilities reported

Install Action-Recognition

You can download it from GitHub.
You can use Action-Recognition 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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