StudentEngagementDetection | Private repo to hold code and scripts
kandi X-RAY | StudentEngagementDetection Summary
kandi X-RAY | StudentEngagementDetection Summary
StudentEngagementDetection is a Python library. StudentEngagementDetection has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Private repo to hold code and scripts
Private repo to hold code and scripts
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Quality
Security
License
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Support
StudentEngagementDetection has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
StudentEngagementDetection has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of StudentEngagementDetection is current.
Quality
StudentEngagementDetection has no bugs reported.
Security
StudentEngagementDetection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
StudentEngagementDetection 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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StudentEngagementDetection releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
Top functions reviewed by kandi - BETA
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of StudentEngagementDetection
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of StudentEngagementDetection
StudentEngagementDetection Key Features
No Key Features are available at this moment for StudentEngagementDetection.
StudentEngagementDetection Examples and Code Snippets
No Code Snippets are available at this moment for StudentEngagementDetection.
Community Discussions
No Community Discussions are available at this moment for StudentEngagementDetection.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install StudentEngagementDetection
To use the repo and run real-time inference on your local machine using a valid camera source, please follow the steps given below:. This repository also contains the code files used to construct each module and the entire notebook used to train models on the DAiSEE dataset using a NVIDIA GPU and cuda on the PyTorch framework. The notebook can be accessed by downloading it and running it on a local GPU after ensuring that the command given below is successfully executed.
Clone, enter, set up and run the files in the repository: $ git clone https://github.com/01pooja10/StudentEngagementDetection $ cd StudentEngagementDetection/ $ pip install -r requirements.txt $ python run.py
The command below can be executed for integrating all facial tracking modules with the 3D ResNet model predictions on 3 consecutive frames: $ python runmod.py
The command below can be executed for selecting convolutional models to be trained on custom/DAiSEE dataset: $ python train.py
PyTorch
OpenCV
Pillow
Scipy
Numpy
Matplotlib
Mediapipe
Clone, enter, set up and run the files in the repository: $ git clone https://github.com/01pooja10/StudentEngagementDetection $ cd StudentEngagementDetection/ $ pip install -r requirements.txt $ python run.py
The command below can be executed for integrating all facial tracking modules with the 3D ResNet model predictions on 3 consecutive frames: $ python runmod.py
The command below can be executed for selecting convolutional models to be trained on custom/DAiSEE dataset: $ python train.py
PyTorch
OpenCV
Pillow
Scipy
Numpy
Matplotlib
Mediapipe
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 .
Find more information at:
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