FlowNet | Modified Version of FlowNet | Continuous Deployment library

by liruoteng C++ Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | FlowNet Summary

FlowNet is a C++ library typically used in Devops, Continuous Deployment, Docker, Drupal applications. FlowNet has no bugs, it has no vulnerabilities and it has low support. However FlowNet has a Non-SPDX License. You can download it from GitHub.

Modified Version of FlowNet, specifically for adversed environment optical flow

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Quality

Security

License

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Support

FlowNet has a low active ecosystem.

It has 113 star(s) with 59 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 29 have been closed. On average issues are closed in 14 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FlowNet is current.

Quality

FlowNet has no bugs reported.

Security

FlowNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

FlowNet has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

FlowNet releases are not available. You will need to build from source code and install.

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

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FlowNet Key Features

No Key Features are available at this moment for FlowNet.

FlowNet Examples and Code Snippets

No Code Snippets are available at this moment for FlowNet.

Community Discussions

Trending Discussions on FlowNet

CUDA_OUT_OF_MEMORY in PyTorch head2head model

Optical flow input for Caffe

QUESTION

CUDA_OUT_OF_MEMORY in PyTorch head2head model

Asked 2021-Mar-05 at 12:17

I am executing the head2head model presented in the Github repo here. When I am running the code using the following command:

...

ANSWER

Answered 2021-Mar-05 at 12:17

Data management:

You can try reducing the dataset used for training to check if is a hardware limitation. Moreover, if it is an image dataset, you can reduce the dimensions of the images by reducing the dpi.

Model parameters management:

Another approach is to reduce the number of parameters of your model. The first suggestion would be to change the Dense layer size and then the other neural network hyperparameters.

Source https://stackoverflow.com/questions/66492008

QUESTION

Optical flow input for Caffe

Asked 2017-Sep-23 at 05:55

I wonder if Caffe can take optical flow image as input, instead of RGB. I am aware that there is such library like FlowNet that learns optical flow, but that is not what I am aiming at.

Please provide me a pointer if any.

...

ANSWER

Answered 2017-Sep-23 at 05:55

Caffe is a very flexible framework. It can process almost any shape of input data you might provide it with.
A very common way to input images to caffe is via lmdb/leveldb datasets created using convert_imageset tool.
For more complex input shapes one can use binary hdf5 files to be read using "HDF5Data" layer.

As for optical flow, you can input it as an image via lmdb or as a two-channel tensor via hdf5. Caffe can handle either way, it's up to you to make sure the net knows how to make sense of the input data.

Source https://stackoverflow.com/questions/44463195

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

Vulnerabilities

No vulnerabilities reported

Install FlowNet

You can download it from GitHub.

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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