faster_rcnn_pytorch | Faster RCNN with PyTorch | Computer Vision library

by longcw Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | faster_rcnn_pytorch Summary

faster_rcnn_pytorch is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. faster_rcnn_pytorch has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However faster_rcnn_pytorch build file is not available. You can download it from GitHub.

Faster RCNN with PyTorch

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Quality

Security

License

Reuse

Support

faster_rcnn_pytorch has a medium active ecosystem.

It has 1642 star(s) with 465 fork(s). There are 52 watchers for this library.

It had no major release in the last 6 months.

There are 69 open issues and 31 have been closed. On average issues are closed in 187 days. There are 1 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.

faster_rcnn_pytorch has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

faster_rcnn_pytorch saves you 3280 person hours of effort in developing the same functionality from scratch.

It has 7044 lines of code, 365 functions and 62 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.

Example tests for faster RCNN
Return image blob from image
Convert an image list to blob
Detects the best box classification
Calculate the total time
Set the time of the simulation
Forward a single image
Compute classification loss
An anchor layer
Convert numpy array to a tensor
Perform the forward computation
Calculates the classification loss
Computes proposal target layer
Return a list of the next blobs
Return the next minibatch
Load configuration from file
Recursively merge two configurations
Add bounding boxes to the image
Compute ground - truth predictions for ground - truth
Locate the CUDA
Find a file in a search path
Convert an image into a blob
Calculate the elapsed time
Return a Dataset object
Import all functions
Print a log message
Perform the backward computation
Set time to time

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

Train

Python

Lines of Code : 9

License : No License

Copy

cd Faster_RCNN_pytorch/faster_rcnn/backbone
mkdir pretrained
cd pretrained
# resnet50-caffe
wget https://drive.google.com/open?id=0B7fNdx_jAqhtbllXbWxMVEdZclE

# resnet101-caffe
wget https://drive.google.com/open?id=0B7fNdx_jAqhtaXZ4aWppWV96czg

pyth

Training on Pascal VOC 2007

Python

Lines of Code : 4

License : No License

Copy

cd faster_rcnn_pytorch
mkdir data
cd data
ln -s $VOCdevkit VOCdevkit2007

Community Discussions

Trending Discussions on faster_rcnn_pytorch

coverting roi pooling in pytorch to nn layer

QUESTION

coverting roi pooling in pytorch to nn layer

Asked 2018-Nov-05 at 18:29

I have a an mlmodel using ROI pooling for which I am using this (adapted from here) (non NN layer version)

...

ANSWER

Answered 2018-Nov-05 at 18:29

Found the issue - The rois after multiplication with spatial scale were being rounded down and had to call round function before calling long like so

Source https://stackoverflow.com/questions/53157978

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

Vulnerabilities

No vulnerabilities reported

Install faster_rcnn_pytorch

Install the requirements (you can use pip or Anaconda):. Clone the Faster R-CNN repository. Build the Cython modules for nms and the roi_pooling layer. Download the trained model VGGnet_fast_rcnn_iter_70000.h5 and set the model path in demo.py. Run demo python demo.py.
Install the requirements (you can use pip or Anaconda): conda install pip pyyaml sympy h5py cython numpy scipy conda install -c menpo opencv3 pip install easydict
Clone the Faster R-CNN repository git clone git@github.com:longcw/faster_rcnn_pytorch.git
Build the Cython modules for nms and the roi_pooling layer cd faster_rcnn_pytorch/faster_rcnn ./make.sh
Download the trained model VGGnet_fast_rcnn_iter_70000.h5 and set the model path in demo.py
Run demo python demo.py

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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