Flow-Guided-Feature-Aggregation | Guided Feature Aggregation for Video Object | Computer Vision library

by msracver Python Version: Current License: MIT

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

kandi X-RAY | Flow-Guided-Feature-Aggregation Summary

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

Flow-Guided Feature Aggregation (FGFA) is initially described in an ICCV 2017 paper. It provides an accurate and end-to-end learning framework for video object detection. The proposed FGFA method, together with our previous work of Deep Feature Flow, powered the winning entry of ImageNet VID 2017. It is worth noting that:.

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Quality

Security

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Flow-Guided-Feature-Aggregation has a low active ecosystem.

It has 677 star(s) with 193 fork(s). There are 33 watchers for this library.

It had no major release in the last 6 months.

There are 57 open issues and 17 have been closed. On average issues are closed in 47 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Flow-Guided-Feature-Aggregation is current.

Quality

Flow-Guided-Feature-Aggregation has 0 bugs and 226 code smells.

Security

Flow-Guided-Feature-Aggregation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Flow-Guided-Feature-Aggregation code analysis shows 0 unresolved vulnerabilities.

There are 16 security hotspots that need review.

License

Flow-Guided-Feature-Aggregation is licensed under the MIT License. This license is Permissive.

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

Reuse

Flow-Guided-Feature-Aggregation releases are not available. You will need to build from source code and install.

Flow-Guided-Feature-Aggregation has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Flow-Guided-Feature-Aggregation saves you 2888 person hours of effort in developing the same functionality from scratch.

It has 6241 lines of code, 352 functions and 65 files.

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

Top functions reviewed by kandi - BETA

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

Train an RPN model
Load parameters from checkpoint
Loads checkpoint parameters
Convert the given context into a dictionary
Train RNN
Get the number of images per image
Get image from roidb
Resize an image
Clips boxes
Generate a triple - indexed trie
Get a batch of test data
Generate pair image image
Load a checkpoint from a checkpoint
Locate CUDA
Binds all executors
Forward computation
Get outputs
Get the gradients of the inputs
Save checkpoint
Set the state arrays
Convenience function to extract a test batch
Combine checkpoint files
Return the next iteration
Generate anchors
Get the pred and label
Return the next batch

Get all kandi verified functions for this library.

Flow-Guided-Feature-Aggregation Key Features

No Key Features are available at this moment for Flow-Guided-Feature-Aggregation.

Flow-Guided-Feature-Aggregation Examples and Code Snippets

No Code Snippets are available at this moment for Flow-Guided-Feature-Aggregation.

Community Discussions

Trending Discussions on Flow-Guided-Feature-Aggregation

How to make pycharm use a different cuda toolkit

QUESTION

How to make pycharm use a different cuda toolkit

Asked 2019-Apr-15 at 07:01

I want to run an MXNet module in GPU.

I have a system which has Ubuntu 18.04 along Cuda 10.0 installed. Apparently this is not covered yet by MXNet binary files so I was focusing on installing 2 cuda versions in my pc (see also here).

Anyway I now have 2 cuda toolkits in my pc in different folders. I need a way to direct my system to use Cuda 9.2 when run from PyCharm. The funny thing is that from a typical console I can run it just fine (at least the MXNet loading part that is of course).

In the module I want to run the program is stuck in:

...

ANSWER

Answered 2019-Apr-15 at 07:01

Well, to make this available to anyone facing the same problem I will post my solution.

I managed to make it work by defining the environmental variable inside pycharm from the run configuration menu (the one that it's available from Run->Run... or Alt+Shift+F10) and defining it there as environmental variable.

LD_LIBRARY_PATH: /usr/local/cuda-9.2/lib64

I am not sure why for that case pycharm is working fine while when the same variable is defined inside the code it does not though (any explanation welcome).

Source https://stackoverflow.com/questions/55648989

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

Vulnerabilities

No vulnerabilities reported

Install Flow-Guided-Feature-Aggregation

For Windows users, run cmd .\init.bat. For Linux user, run sh ./init.sh. The scripts will build cython module automatically and create some folders. 3.1 Clone MXNet and checkout to MXNet@(v0.10.0) by. 3.2 Copy operators in $(FGFA_ROOT)/fgfa_rfcn/operator_cxx to $(YOUR_MXNET_FOLDER)/src/operator/contrib by. 3.4 Install the MXNet Python binding by. 3.5 For advanced users, you may put your Python packge into ./external/mxnet/$(YOUR_MXNET_PACKAGE), and modify MXNET_VERSION in ./experiments/fgfa_rfcn/cfgs/*.yaml to $(YOUR_MXNET_PACKAGE). Thus you can switch among different versions of MXNet quickly.
Clone the Flow-Guided Feature Aggregation repository, and we call the directory that you cloned as ${FGFA_ROOT}.
For Windows users, run cmd .\init.bat. For Linux user, run sh ./init.sh. The scripts will build cython module automatically and create some folders.
Install MXNet: 3.1 Clone MXNet and checkout to MXNet@(v0.10.0) by git clone --recursive https://github.com/apache/incubator-mxnet.git cd incubator-mxnet git checkout v0.10.0 git submodule update 3.2 Copy operators in $(FGFA_ROOT)/fgfa_rfcn/operator_cxx to $(YOUR_MXNET_FOLDER)/src/operator/contrib by cp -r $(FGFA_ROOT)/fgfa_rfcn/operator_cxx/* $(MXNET_ROOT)/src/operator/contrib/ 3.3 Compile MXNet cd ${MXNET_ROOT} make -j4 3.4 Install the MXNet Python binding by Note: If you will actively switch between different versions of MXNet, please follow 3.5 instead of 3.4 cd python sudo python setup.py install 3.5 For advanced users, you may put your Python packge into ./external/mxnet/$(YOUR_MXNET_PACKAGE), and modify MXNET_VERSION in ./experiments/fgfa_rfcn/cfgs/*.yaml to $(YOUR_MXNET_PACKAGE). Thus you can switch among different versions of MXNet quickly.

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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