faster-rcnn.pytorch | A faster pytorch implementation of faster r-cnn | Machine Learning library

by jwyang Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | faster-rcnn.pytorch Summary

faster-rcnn.pytorch is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. 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.

A faster pytorch implementation of faster r-cnn

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Security

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faster-rcnn.pytorch has a medium active ecosystem.

It has 7269 star(s) with 2342 fork(s). There are 91 watchers for this library.

It had no major release in the last 6 months.

There are 404 open issues and 426 have been closed. On average issues are closed in 126 days. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of faster-rcnn.pytorch is current.

Quality

faster-rcnn.pytorch has 0 bugs and 0 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 0 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 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 are not available. Examples and code snippets are available.

faster-rcnn.pytorch saves you 2512 person hours of effort in developing the same functionality from scratch.

It has 5464 lines of code, 287 functions and 77 files.

It has high 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.

Forward forward computation
Transform a batch of bboxes
Unmap data
Compute the bounding boxes for a batch
Forward the prediction
Transform boxes into invoations
Clip boxes
Wrapper for nms
Return a list of the image s roidb
Return a list of the gt roidb
Append flipped images
Create a config dictionary from a nested list
Downloads all images
M munge files
Train the model
Evaluate detection
Perform the forward computation
Clip boxes to given image size
Loads the image set index
Evaluate recalling box
Parse arguments
Performs RPNNN on input image data
Forward the RPN layer
Create a roiddb for each image
Sample a two grid
Get image blob from image
Return the roidb for selective search

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

Iterative-Visual-Reasoning.pytorch,Compilation (for computing roi crop_and_resize)

Python

Lines of Code : 3

License : Permissive (MIT)

Copy

cd ./lib
sh make.sh
cd ..

A Pytorch Implementation of Faster R-CNN,Test result

Python

Lines of Code : 2

License : Permissive (MIT)

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python3 trainval_net.py --dataset pascal_voc --net res101 --nw 8 --bs 2 --epochs 20 --cuda

python3 trainval_net_alt.py --dataset pascal_voc --net res101 --nw 8 --bs 2 --epochs 20 20 20 20 --cuda

Low Shot Box Correction for Weakly Supervised Object Detection,Installation and Preparation

Python

Lines of Code : 1

License : Permissive (MIT)

Copy

git clone https://github.com/ptx9363/BCNet.git

Community Discussions

Trending Discussions on faster-rcnn.pytorch

Pytorch Faster R-CNN size mismatch errors in testing

QUESTION

Pytorch Faster R-CNN size mismatch errors in testing

Asked 2020-Jun-08 at 03:36

there!

When running test_net.py in pytorch1.0 Faster R-CNN and demo.py on coco dataset with faster_rcnn_1_10_9771.pth(the pretrained resnet101 model on coco dataset provided by jwyang), I encounter the same errors below :

...

ANSWER

Answered 2020-Jun-08 at 03:36

It says your model doesn't fit the pre-trained parameters you want to load.

Maybe check the model you're using and the .pth file and find out if they match or what.

Or post the code of your model and let's see what's going wrong.

Source https://stackoverflow.com/questions/62247674

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

This project is a faster pytorch implementation of faster R-CNN, aimed to accelerating the training of faster R-CNN object detection models. Recently, there are a number of good implementations:.

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