faster-rcnn-pytorch | library implemented by pytorch of faster-rcnn

by bubbliiiing Python Version: v3.1 License: MIT

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

kandi X-RAY | faster-rcnn-pytorch Summary

faster-rcnn-pytorch is a Python library. faster-rcnn-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

This is a library implemented by pytorch of faster-rcnn, which can use the data in the voc dataset format for training.

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Quality

Security

License

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Support

faster-rcnn-pytorch has a medium active ecosystem.

It has 1147 star(s) with 306 fork(s). There are 7 watchers for this library.

It had no major release in the last 12 months.

There are 141 open issues and 30 have been closed. On average issues are closed in 7 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of faster-rcnn-pytorch is v3.1

Quality

faster-rcnn-pytorch has 0 bugs and 27 code smells.

Security

faster-rcnn-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

faster-rcnn-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

faster-rcnn-pytorch 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

faster-rcnn-pytorch releases are available to install and integrate.

Build file is available. You can build the component from source.

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

faster-rcnn-pytorch saves you 1006 person hours of effort in developing the same functionality from scratch.

It has 2286 lines of code, 78 functions and 20 files.

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

Top functions reviewed by kandi - BETA

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

Fit one epoch
Prepare the map file for training
Downloads and saves the ground truth results
Get a txt file
Get the ground truth map
Compute ground - truth map
Draw a plot of a dictionary
Adjust axes limits
Draw text inside an image
Detect image
Resize an image
Convert an image to RGB
Calculate new image size
Forward computation
Convert location to bounding box
Correct boxes according to given image
decomposition of VGG16
Make a list of convolutional layers
Compute the FPS of the image
Return a function for learning rate based on learning rate
Downloads the ground truth test results
Converts an annotation file into a list
Generate anchor base
Show configuration options
Reads the class names
Set lr for optimizer
Freeze BatchNorm layers

Get all kandi verified functions for this library.

faster-rcnn-pytorch Key Features

No Key Features are available at this moment for faster-rcnn-pytorch.

faster-rcnn-pytorch Examples and Code Snippets

Source-Free Object Detection by Learning to Overlook Domain Style (CVPR 2022 ORAL Paper),Overlooking style module,Prepare data

Python

Lines of Code : 2

License : Permissive (MIT)

Copy

cd Overlook
ln -s [your dataset dir] data

Run Object Detector (or download the detections we provide),Create your own,Step 2: Run faster-rcnn

HTML

Lines of Code : 1

License : No License

Copy

python -m exp.hoi_classifier.data.write_faster_rcnn_feats_to_hdf5

Community Discussions

No Community Discussions are available at this moment for faster-rcnn-pytorch.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install faster-rcnn-pytorch

You can download it from GitHub.
You can use faster-rcnn-pytorch 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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