EfficientNet-SSD | Object Detection using EfficientNet

by shaoshengsong Python Version: Current License: No License

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

kandi X-RAY | EfficientNet-SSD Summary

EfficientNet-SSD is a Python library. EfficientNet-SSD has no bugs, it has no vulnerabilities and it has low support. However EfficientNet-SSD build file is not available. You can download it from GitHub.

Object Detection using EfficientNet

Support

Quality

Security

License

Reuse

Support

EfficientNet-SSD has a low active ecosystem.

It has 40 star(s) with 6 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 3 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of EfficientNet-SSD is current.

Quality

EfficientNet-SSD has 0 bugs and 0 code smells.

Security

EfficientNet-SSD has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

EfficientNet-SSD code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

EfficientNet-SSD 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

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

EfficientNet-SSD 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.

EfficientNet-SSD saves you 1155 person hours of effort in developing the same functionality from scratch.

It has 2608 lines of code, 208 functions and 53 files.

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

Top functions reviewed by kandi - BETA

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

Runs the detection algorithm
Get the checkpoint file
Builds the transformations from the input image
Loads the checkpoint from f
Assigns a priors to each target
Compute the area of two cells
Compute the overlap area of two boxes
Train detection model
Create data loaders
Build a dataset from a list of datasets
Run nms on boxes
Call an attribute on the model
Perform the forward projection
Drop the input tensor
Gather all tensors from all ranks
Returns the world size
Returns an instance of MobileNetV2
Creates an instance ofefficient_b0
Constructs a simple elasticnet model
Forward the convolution layer
Encode a sequence of blocks
Put the data into a tensor
Decorator to register a module
Configure a logger
Return a list of all supported extensions
Get the size of a given model

Get all kandi verified functions for this library.

EfficientNet-SSD Key Features

No Key Features are available at this moment for EfficientNet-SSD.

EfficientNet-SSD Examples and Code Snippets

No Code Snippets are available at this moment for EfficientNet-SSD.

Community Discussions

No Community Discussions are available at this moment for EfficientNet-SSD.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install EfficientNet-SSD

You can download it from GitHub.
You can use EfficientNet-SSD 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 .

Find more information at: