py-faster-rcnn | Faster R-CNN ( Python implementation

by rbgirshick Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(5)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | py-faster-rcnn Summary

py-faster-rcnn is a Python library. py-faster-rcnn has no bugs, it has no vulnerabilities and it has medium support. However py-faster-rcnn build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Faster R-CNN (Python implementation) -- see for the official MATLAB version

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Support

py-faster-rcnn has a medium active ecosystem.

It has 7892 star(s) with 4164 fork(s). There are 297 watchers for this library.

It had no major release in the last 6 months.

There are 647 open issues and 254 have been closed. On average issues are closed in 271 days. There are 26 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

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

Quality

py-faster-rcnn has 0 bugs and 303 code smells.

Security

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

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

There are 0 security hotspots that need review.

License

py-faster-rcnn has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

py-faster-rcnn releases are not available. You will need to build from source code and install.

py-faster-rcnn 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.

py-faster-rcnn saves you 1752 person hours of effort in developing the same functionality from scratch.

It has 3878 lines of code, 218 functions and 46 files.

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

Top functions reviewed by kandi - BETA

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

Return the roidb dictionary
Return roidb handler
Call build extensions
Set the cuda compiler to use
Load configuration from file
Recursively merge a b into b
Locate CUDA
Find a file in a search path
Append flipped images
Returns the widths of the image
Setup the image
Reshape the object
Forward the pixel to the given bottom
Create a config dictionary from a list
Get an imdb dataset
Adds a path to sys path
Turn on
Set proposal method
Parse arguments

Get all kandi verified functions for this library.

py-faster-rcnn Key Features

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

py-faster-rcnn Examples and Code Snippets

Popular issues

C++

Lines of Code : 25

License : Non-SPDX (NOASSERTION)

Copy

"Unknown layer type: Python"

WITH_PYTHON_LAYER := 1

cd $ROOT_DIR/py-faster-rcnn/caffe-fast-rcnn/
make clean
make -j8 && make pycaffe

fatal error: caffe/proto/caffe.pb.h: No such file or directory
#include "caffe/proto/caffe.pb.h"

Learning Chained Deep Features and Classifiers for Cascade in Object Detection,Cascade Loss

Jupyter Notebook

Lines of Code : 24

License : Permissive (MIT)

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layer {
  name: "loss_cls_cas_128"
  type: "SoftmaxWithCascadeLoss"
  bottom: "cls_score_cas_128"
  bottom: "labels"
  bottom: "bp_map_cas_128"
  top: "loss_cls_cas_128"
  top: "bp_map_cas_64"
  loss_weight: 1
  loss_weight: 0
  loss_param {
    hard

faster-rcnn-pure-c plus implement,Add the Roipooling layer and Smooth_L1_loss_layer

C++

Lines of Code : 20

License : No License

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  optional ROIPoolingParameter roi_pooling_param = 200;
  optional SmoothL1LossParameter smooth_l1_loss_param = 201;


// Message that stores parameters used by ROIPoolingLayer
message ROIPoolingParameter {
  // Pad, kernel size, and stride are all g

Compute precision - recall curve .

python

Lines of Code : 52

License : Permissive (MIT License)

Copy

def ap_per_class(tp, conf, pred_cls, target_cls):
    """ Compute the average precision, given the recall and precision curves.
    Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
    # Arguments
        tp:    True

Compute the area of a PR curve .

python

Lines of Code : 26

License : Permissive (MIT License)

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def compute_ap(recall, precision):
    """ Compute the average precision, given the recall and precision curves.
    Code originally from https://github.com/rbgirshick/py-faster-rcnn.
    # Arguments
        recall:    The recall curve (list).

Community Discussions

Trending Discussions on py-faster-rcnn

nvcc fatal : '--ptxas-options=-v': expected a number

Why add a small number on the bounding box?

1080Ti Check failed: error == cudaSuccess (8 vs. 0) invalid device function

faster-rcnn config file in tensorflow

How shared convolutional layers are fixed in training Faster R-CNN

Debugging c++ code behind python

How Faster RCNN library load training dataset for training?

In caffe, py-faster-rcnn, "scores" return a large matrix, why?

Check failed: status == CUDNN_STATUS_SUCCESS (3 vs. 0) CUDNN_STATUS_BAD_PARAM for FASTER RCNN Library

Confused about the lambda expression in python

QUESTION

nvcc fatal : '--ptxas-options=-v': expected a number

Asked 2019-Jun-10 at 10:15

Getting the nvcc fatal : '--ptxas-options=-v': expected a number error when I try to build a Windows port of Faster-RCNN. You may reach the setup file (which is a Python script) directly from here.

Software Environment:

...

ANSWER

Answered 2019-Jun-09 at 14:07

This configuration line is no longer correct with CUDA 10.1:

Source https://stackoverflow.com/questions/56513796

QUESTION

Why add a small number on the bounding box?

Asked 2019-Jan-06 at 04:36

I found that on the implementation of Fast(er) RCNN, there is always a small value added to the width and height of the bounding box. Why add a small number to the width and height?

For example, in Fast RCNN, cfg.EPS (default is 1e-14) is added:

...

ANSWER

Answered 2018-Dec-28 at 16:47

I don’t know what is happening in the first case, but in the second case looks like the left and right locations are both inside the bounding box. The number of pixels spanned must therefore include both left and right locations. This is why 1 is added.

Source https://stackoverflow.com/questions/53957709

QUESTION

1080Ti Check failed: error == cudaSuccess (8 vs. 0) invalid device function

Asked 2018-Feb-07 at 07:56

I got this sentence in py-faster-Rcnn

...

ANSWER

Answered 2018-Feb-07 at 07:56

Install CUDA 8.0 and that should resolve the issue.

From this you can find out that GeForce GTX 1080 Ti GPU belongs to Pascal GPU Micro architecture with compute capability version 6.1 which is supported by CUDA 8.0 or higher.

CUDA SDK 8.0 support for compute capability 2.0 – 6.x (Fermi, Kepler, Maxwell, Pascal), last version with support for Compute Capability 2.x (Fermi)

CUDA SDK 7.5 support for compute capability 2.0 – 5.x (Fermi, Kepler, Maxwell)

Source https://stackoverflow.com/questions/48618123

QUESTION

faster-rcnn config file in tensorflow

Asked 2018-Jan-23 at 15:56

I am using Google API for object detection in tensorflow to train and infer on a custom dataset.

I would like to adjust the parameters of the config file to better suit my samples (e.g. no. of region proposals, size of ROI bbox, etc.). To do so, I need to know what each parameter does. Unfortunately, the config files (found here ) do not have comments or explanations. Some, such as "num classes" are self-explanatory, but others are tricky.

I found this file with more comments , but wasn't able to 'translate' it to my format.

I would like to know one of the following: 1. explanation of each parameter for google's API config file or 2. 'translation' from the official faster-rcnn to google's API config or at least 3. thorough review of faster-rcnn with technical details of the parameters (the official article doesn't provide all the details)

Thank you for your kind help !

Example of a config file:

...

ANSWER

Answered 2018-Jan-23 at 15:56

I found two sources that shed some light on the config file: 1. The folder protos inside tensorflow github covers all configuration options with some comments on each options. You should checkout faster_rcnn.proto , eval.proto and train.proto for the most common 2. This blog post by Algorithmia covers thoroughly all steps to download, prepare and train faster RCNN on Google's Open Images dataset. 2/3-way through, there is some discussion on the configuration options.

Source https://stackoverflow.com/questions/48382398

QUESTION

How shared convolutional layers are fixed in training Faster R-CNN

Asked 2017-Dec-13 at 06:54

I am looking through the code in the library. In the paper, (page 6, second column, first para), it is stated as convolutional layers are fixed (during training in third and fourth steps) and tuned the RPN layers and Fast RCNN layers.

Which portion of the code is taking care of it?

I looked at the code and Solver.cpp is the one controlling Forward/Backward. I don't see implementation of fixing convolutional layers there.

Then all prototxt files have similar implementations for layers.

How this fixing convolutional layers in training is implemented?

...

ANSWER

Answered 2017-Dec-13 at 06:29

When freezing a layer during fine-tuning, one usually sets

Source https://stackoverflow.com/questions/47766687

QUESTION

Debugging c++ code behind python

Asked 2017-Dec-12 at 07:32

I am trying to debug C++ code behind python for this library (faster rcnn).

I followed the instruction here.

Say I like to stop a break point at solver.cpp and line 188.

...

ANSWER

Answered 2017-Dec-12 at 06:25

You probably has passed your breakpoint (before GDB got started). You should, per the instructions mentioned:

run python under gdb from the start

Try to run your python under GDB.

Read about Debugging with GDB.

You might want to use a python interpreter built with DWARF debug info, so compiled with -g (and of course your solver.cpp should also be compiled with -g). Since Python is free software recompiling it with -g should be pretty simple.

BTW, are you sure of the line position 188 of your breakpoint? Did you try to add more breakpoints in solver.cpp at other positions, or to break at function names?

Source https://stackoverflow.com/questions/47766364

QUESTION

How Faster RCNN library load training dataset for training?

Asked 2017-Nov-23 at 07:22

I use Faster RCNN library for Deep Learning and here is a discussion of how to train own dataset. That is one step ahead of me.

For me I like to understand how dataset for training is setup and how it is loaded for training?

When I look at the code, I saw this line of code

imdb = get_imdb(imdb_name) from train_faster_rcnn_alt_opt.py and it calls factory.py inside datasets folder.

factory.py has

...

ANSWER

Answered 2017-Nov-23 at 07:22

Now I understood. lib/datasets/factory.py has

Source https://stackoverflow.com/questions/47446924

QUESTION

In caffe, py-faster-rcnn, "scores" return a large matrix, why?

Asked 2017-Nov-21 at 14:41

I use the py-faster-rcnn demo to build further of my project with 20 classes. However, I am trying to gain the softmax, last layer probability of my classes.

For example:

...

ANSWER

Answered 2017-Nov-21 at 14:35

The raw scores the detector outputs include overlapping detections and very low score detections as well.
Note that only after applying non-maximal suppression (aka "nms") with NMS_THRESH=0.3 the function vis_detection only displays detections with confidence larger than CONF_THRESH=0.8.
So, if you want to look at the "true" objects, you need to check inside vis_detection and check only the detections it renders on the image.

Source https://stackoverflow.com/questions/47412482

QUESTION

Check failed: status == CUDNN_STATUS_SUCCESS (3 vs. 0) CUDNN_STATUS_BAD_PARAM for FASTER RCNN Library

Asked 2017-Nov-17 at 04:38

I am testing Faster Rcnn. Installation is fine. During the installation, I had one issue with cudnn5.1 and I followed the suggestion here and now installation is fine.

Now I test the demo code as

...

ANSWER

Answered 2017-Nov-17 at 04:38

Now it works for me. Since libcudnn5_5.1 is for CUDA7.5. Can check in cudnn's user guide at GPU and driver requirements. So I changed to cudnnv6.0 for CUDA8.0.

Then you may face the issue of

Source https://stackoverflow.com/questions/47342267

QUESTION

Confused about the lambda expression in python

Asked 2017-Sep-23 at 05:39

I understand the normal lambda expression, such as

...

ANSWER

Answered 2017-Sep-23 at 04:20

A lambda function:

Source https://stackoverflow.com/questions/46375959

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

Vulnerabilities

No vulnerabilities reported

Install py-faster-rcnn

We'll call the directory that you cloned Faster R-CNN into FRCN_ROOT.
Clone the Faster R-CNN repository
We'll call the directory that you cloned Faster R-CNN into FRCN_ROOT Ignore notes 1 and 2 if you followed step 1 above. Note 1: If you didn't clone Faster R-CNN with the --recursive flag, then you'll need to manually clone the caffe-fast-rcnn submodule: git submodule update --init --recursive Note 2: The caffe-fast-rcnn submodule needs to be on the faster-rcnn branch (or equivalent detached state). This will happen automatically if you followed step 1 instructions.
Build the Cython modules cd $FRCN_ROOT/lib make
Build Caffe and pycaffe cd $FRCN_ROOT/caffe-fast-rcnn # Now follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # If you're experienced with Caffe and have all of the requirements installed # and your Makefile.config in place, then simply do: make -j8 && make pycaffe
Download pre-computed Faster R-CNN detectors cd $FRCN_ROOT ./data/scripts/fetch_faster_rcnn_models.sh This will populate the $FRCN_ROOT/data folder with faster_rcnn_models. See data/README.md for details. These models were trained on VOC 2007 trainval.
Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16. VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience. ZF was trained at MSRA.

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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