maskrcnn-benchmark | modular reference implementation of Instance Segmentation | Computer Vision library

 by   facebookresearch Python Version: v0.1 License: MIT

kandi X-RAY | maskrcnn-benchmark Summary

kandi X-RAY | maskrcnn-benchmark Summary

maskrcnn-benchmark is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. maskrcnn-benchmark has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However maskrcnn-benchmark has 4 bugs. You can download it from GitHub.

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

            kandi-support Support

              maskrcnn-benchmark has a medium active ecosystem.
              It has 9110 star(s) with 2549 fork(s). There are 181 watchers for this library.
              It had no major release in the last 12 months.
              There are 498 open issues and 563 have been closed. On average issues are closed in 197 days. There are 32 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of maskrcnn-benchmark is v0.1

            kandi-Quality Quality

              maskrcnn-benchmark has 4 bugs (0 blocker, 0 critical, 4 major, 0 minor) and 123 code smells.

            kandi-Security Security

              maskrcnn-benchmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              maskrcnn-benchmark code analysis shows 0 unresolved vulnerabilities.
              There are 4 security hotspots that need review.

            kandi-License License

              maskrcnn-benchmark is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              maskrcnn-benchmark releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              maskrcnn-benchmark saves you 4724 person hours of effort in developing the same functionality from scratch.
              It has 9971 lines of code, 607 functions and 137 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed maskrcnn-benchmark and discovered the below as its top functions. This is intended to give you an instant insight into maskrcnn-benchmark implemented functionality, and help decide if they suit your requirements.
            • Process a single image
            • Decode the image
            • The area of the bounding box
            • Convert a tensor
            • Convert CloudScapes instance
            • Convert xyxy coordinates to xy coordinates
            • Convert a polygon to box coordinates
            • Make data loader
            • Build a dataset from a list of datasets
            • Train the detection model
            • Evaluate a model on a dataset
            • Returns a list of C ++ extensions
            • Compute predictions for the given boxes
            • Compute the classification
            • Selects all boxes in the given boxes
            • Transpose the image
            • Resizes the bounding box
            • Add the last layer
            • Creates a 3x3 convolutional module
            • Subsample the given proposals
            • Forward convolution
            • Forward feature extraction
            • Given a list of image files and a list of instance ids return a dictionary of instances
            • Compute features for a single feature map
            • Match the predictions with the given predictions
            • Forward convolution function
            • Runs an inference on the given dataset
            Get all kandi verified functions for this library.

            maskrcnn-benchmark Key Features

            No Key Features are available at this moment for maskrcnn-benchmark.

            maskrcnn-benchmark Examples and Code Snippets

            C++dot img1Lines of Code : 115dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            import torch
            from torch.autograd import Function
            from torch.autograd.function import once_differentiable
            from torch.onnx.symbolic_opset9 import unsqueeze
            from torch.onnx.symbolic_helper import parse_args
            class NonMaxSuppression(Function):
            C++dot img2Lines of Code : 55dot img2License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            import os
            import torch
            import tensorrt as trt
            from PIL import Image
            import numpy as np
            import common
            from tools.convert_model import conver_engine
            import time
            import cv2
            import glob
            TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
            if __name__ == "__main__"  
            C++dot img3Lines of Code : 54dot img3License : Strong Copyleft (GPL-3.0)
            copy iconCopy
                    float* a = (float*)malloc(20 * 4 * sizeof(float));
                    cudaMemcpy(a, locData, 20 * 4 * sizeof(float), cudaMemcpyDeviceToHost);
                    for (int i = 0; i < 20; i ++) {
                        for (int j = 0; j < 4; j ++) {

            Community Discussions


            Evaluation on Coco-type data set returns error
            Asked 2020-Mar-22 at 04:35

            I am using the Faster R-CNN model available from I am trying to evaluate the results of a trained model on the KITTI data set, after converting it to Coco Format (2D object detection).

            The results are 0 or -1 and sometimes it throws an error in the CocoApi toolkit at g["area"].

            pycoco if g['ignore'] or (g['area']aRng[1]): "KeyError: 'area'"

            From what I found while researching the problem, "area" is used for segmentation and I do not have that kind of annotation in my data set.

            An small example of how my converted annotation file looks:



            Answered 2020-Mar-22 at 04:35

            According to the 1. Detection Evaluation of the COCO official documents, AP by area are also evaluated.

            Therefore, if there is no area in your own custom dataset, an error will occur in the following part of the code of site-packages/pycocotools/



            what is the biggest bottleneck in maskrcnn_benchmark repo?
            Asked 2019-Nov-24 at 19:27

            I am working on a repo that make use of the maskrcnn_benchmark repo. I have extensively, explored the bench-marking repo extensively for the cause of its slower performance on a cpu with respect to enter link description here.

            In order to create a benchmark for the individual forward passes I have put a time counter for each part and it gives me the time required to calculate each component. I have had a tough time exactly pinpointing as to the slowest component of the entire architecture.I believe it to be BottleneckWithFixedBatchNorm class in the maskrcnn_benchmark/modeling/backbone/ file.

            I will really appreciate any help in localisation of the biggest bottle neck in this architecture.



            Answered 2019-Nov-24 at 19:27

            I have faced the same problem, the best possible solution for the same is to look inside the main code, go through the forward pass of each module and have a timer setup to log the time that is spent in the computations of each module. How we worked in it was to create an architecture where we create the time logger for each class, therefore every instance of the class will now be logging its time of execution, after through comparison, atleast in our case we have found that the reason for the delay was the depth of the Resnet module, (which given the computational cost of resnet is not a surprising factor at all, the only solution to the same is more palatalization so either ensure a bigger GPU for performing the task or reduce the depth of the Resnet network ).

            I must inform that the maskrcnn_benchmark has been deprecated and an updated version of the same is available in the form of detectron2. Consider moving your code for significant speed improvements in the architecture.

            BottleneckWithFixedBatchNorm is not the most expensive operation in the architecture and certainly not creating the bottleneck as all the operations instead of the name. The class isn't as computationally expensive and is computed in parallel even on a lower end CPU machine (at least in the inference stage).

            An example of tracking better the performance of each module can be found with the code taken from the path : maskrcnn_benchmark/modeling/backbone/



            pulling a git repo at a particular commit in python file
            Asked 2019-Mar-13 at 18:31

            I have a Python project where I am using the maskrcnn_benchmark project from facebook research.

            In my continuous integration script, I create a virtual environment where I install this project with thee following steps:



            Answered 2019-Mar-13 at 16:46

            You can use dependency_links



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


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