MDVC | PyTorch implementation of Multi-modal Dense Video Captioning | Computer Vision library
kandi X-RAY | MDVC Summary
kandi X-RAY | MDVC Summary
MDVC is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. MDVC has no bugs, it has no vulnerabilities and it has low support. However MDVC build file is not available. You can download it from GitHub.
PyTorch implementation of Multi-modal Dense Video Captioning (CVPR 2020 Workshops)
PyTorch implementation of Multi-modal Dense Video Captioning (CVPR 2020 Workshops)
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Quality
Security
License
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Support
MDVC has a low active ecosystem.
It has 112 star(s) with 18 fork(s). There are 7 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 22 have been closed. On average issues are closed in 11 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of MDVC is current.
Quality
MDVC has 0 bugs and 0 code smells.
Security
MDVC has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
MDVC code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
MDVC does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
Reuse
MDVC releases are not available. You will need to build from source code and install.
MDVC 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.
MDVC saves you 799 person hours of effort in developing the same functionality from scratch.
It has 1836 lines of code, 88 functions and 9 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed MDVC and discovered the below as its top functions. This is intended to give you an instant insight into MDVC implemented functionality, and help decide if they suit your requirements.
- The main validation loop
- Update the iterator
- Calculate the metrics for ANETCapture
- Evaluate the classification
- Creates a pandas dataframe from a list of subtitles
- Filter dataframe
- Parse a sub - file
- Add adjustments to the dataframe
- Perform validation
- Load multiple features from an hdf5 file
- Filter features based on average_split
- Provide predictions for a single Board
- Greedy decoding
- Generates a mask of the given size
- Mask src with padding
- Compute the attention function
- Compute attention
- Train the model
- Return a string representation of the model
- Save a model
- Compute the average of metrics in two dictionaries
- Calculate timer time
Get all kandi verified functions for this library.
MDVC Key Features
No Key Features are available at this moment for MDVC.
MDVC Examples and Code Snippets
No Code Snippets are available at this moment for MDVC.
Community Discussions
Trending Discussions on MDVC
QUESTION
Changes on navigation bar in UIPageViewController not working properly
Asked 2019-Aug-05 at 10:27
My code :
...ANSWER
Answered 2019-Aug-05 at 10:27implement:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install MDVC
You can download it from GitHub.
You can use MDVC 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.
You can use MDVC 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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