yolo-v3 | You Only Look Once by Pytorch | Machine Learning library

by ne7ermore Python Version: Current License: MIT

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

kandi X-RAY | yolo-v3 Summary

yolo-v3 is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. yolo-v3 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

YOLO-v3 implemention from "YOLOv3: An Incremental Improvement".

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Quality

Security

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Support

yolo-v3 has a low active ecosystem.

It has 33 star(s) with 17 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. On average issues are closed in 126 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of yolo-v3 is current.

Quality

yolo-v3 has 0 bugs and 0 code smells.

Security

yolo-v3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

yolo-v3 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

yolo-v3 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

yolo-v3 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.

yolo-v3 saves you 340 person hours of effort in developing the same functionality from scratch.

It has 815 lines of code, 39 functions and 6 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed yolo-v3 and discovered the below as its top functions. This is intended to give you an instant insight into yolo-v3 implemented functionality, and help decide if they suit your requirements.

Train DarkNet
Calculate the average accuracy of detection
Predict a prediction
Compute area
Compute the intersection of two boxes
Load classes from file
Predict the given prediction
Finds the intersection of two boxes
Convert tensor to image
Compute detections for each layer
Compute the transformer

Get all kandi verified functions for this library.

yolo-v3 Key Features

No Key Features are available at this moment for yolo-v3.

yolo-v3 Examples and Code Snippets

No Code Snippets are available at this moment for yolo-v3.

Community Discussions

Trending Discussions on yolo-v3

ERROR: Could not build wheels for opencv-python which > use PEP 517 and cannot be installed directly

Is it possible to train YOLO (any version) for a single class where the image has text data. (find region of equations)

How to calculate output sizes after a convolution layer in a configuration file?

QUESTION

ERROR: Could not build wheels for opencv-python which > use PEP 517 and cannot be installed directly

Asked 2020-Oct-04 at 14:23

hy, i have a very equal problem to the problem shown here ERROR: Could not build wheels for scipy which use PEP 517 and cannot be installed directly.

I'm using a jetson Nano (ubuntu 18.04).

Error

Building wheel for opencv-python (PEP 517) ... error ERROR: Command errored out with exit status 1: command: /home/christopher/heartkillayolotest2/heartkillayolotest2/bin/python /home/christopher/heartkillayolotest2/heartkillayolotest2/lib/python3.6/site-packages/pip/_vendor/pep517/_in_process.py build_wheel /tmp/tmpwort0shc cwd: /tmp/pip-install-g68zdlf0/opencv-python Complete output (9 lines): File "/tmp/pip-build-env-1b_l6sbo/overlay/lib/python3.6/site-packages/skbuild/setuptools_wrap.py", line 560, in setup cmkr = cmaker.CMaker(cmake_executable) File "/tmp/pip-build-env-1b_l6sbo/overlay/lib/python3.6/site-packages/skbuild/cmaker.py", line 95, in init self.cmake_version = get_cmake_version(self.cmake_executable) File "/tmp/pip-build-env-1b_l6sbo/overlay/lib/python3.6/site-packages/skbuild/cmaker.py", line 82, in get_cmake_version "Problem with the CMake installation, aborting build. CMake executable is %s" % cmake_executable) Traceback (most recent call last): Problem with the CMake installation, aborting build. CMake executable is cmake ----------------------------------------
ERROR: Failed building wheel for opencv-python Failed to build opencv-python ERROR: Could not build wheels for opencv-python which use PEP 517 and cannot be installed directly

I tried this to solve the problem but it did not help:

(heartkillayolotest2)christopher@ccz:~/heartkillayolotest2/heartkillayolotest2$ pip3 install --upgrade pip Requirement already up-to-date: pip in ./lib/python3.6/site-packages (20.2.3)

and

(heartkillayolotest2) christopher@ccz:~/heartkillayolotest2/heartkillayolotest2/yolo-v3$ pip3 install --upgrade pip Requirement already up-to-date: pip in /home/christopher/heartkillayolotest2/heartkillayolotest2/lib/python3.6/site-packages (20.2.3)

Unfortunatelly the error still occurs.

Can somebody help me?

...

ANSWER

Answered 2020-Sep-21 at 09:37

The problem is with cmake and python. Python need skbuild and cython for this job.

Source https://stackoverflow.com/questions/63869389

QUESTION

Is it possible to train YOLO (any version) for a single class where the image has text data. (find region of equations)

Asked 2020-Jul-24 at 05:04

I am wondering if YOLO (any version, specially the one with accuracy, not speed) can be trained on the text data. What I am trying to do is to find the Region in the text image where any equation is present.

For example, I want to find the 2 of the Gray regions of interest in this image so that I can outline and eventually, crop the equations separately.

I am asking this questions because : First of all I have not found a place where the YOLO is used for text data. Secondly, how can we customise for low resolution unlike the (416,416) as all the images are either cropped or horizontal mostly in (W=2H) format.

I have implemented the YOLO-V3 version for text data but using OpenCv which is basically for CPU. I want to train the model from scratch.

Please help. Any of the Keras, Tensorflow or PyTorch would do.

Here is the code I used for implementing in OpenCv.

...

ANSWER

Answered 2020-Jul-24 at 05:04

Being an object detector Yolo can be used for specific text detection only, not for detecting any text that might be present in the image.

For example Yolo can be trained to do text based logo detection like this:

I want to find the 2 of the Gray regions of interest in this image so that I can outline and eventually, crop the equations separately.

Your problem statement talks about detecting any equation (math formula) that's present in the image so it can't be done using Yolo alone. I think mathpix is similar to your use-case. They will be using OCR (Optical Character Recognition) system trained and fine tuned towards their use-case.

Eventually to do something like mathpix, OCR system customised for your use case is what you need. There won't be any ready ready made solution out there for this. You'll have to build one.

Proposed Methods:

Note: Tesseract as it is can't be used because it is a pre-trained model trained for reading any character. You can refer 2nd paper to train tesseract towards fitting your use case.

To get some idea about OCR, you can read about it here.

EDIT:

So idea is to build your own OCR to detect something that constitutes equation/math formula rather than detecting every character. You need to have data set where equations are marked. Basically you look for region with math symbols(say summation, integration etc.).

Some Tutorials to train your own OCR:

So idea is that you follow these tutorials to get to know how to train and build your OCR for any use case and then you read research papers I mentioned above and also some of the basic ideas I gave above to build OCR towards your use case.

Source https://stackoverflow.com/questions/63030000

QUESTION

How to calculate output sizes after a convolution layer in a configuration file?

Asked 2019-Jun-05 at 07:54

I'm new to convolutional neural networks and wanted to know how to calculate or figure out the output sizes between layers of a model given a configuration file for pytorch similar to those following instructions in this link.

Most of the stuff I've already looked at hasn't been very clear and concise. How am I supposed to calculate the sizes through each layer? Below is a snippet of a configuration file that would be parsed.

...

ANSWER

Answered 2019-Jun-05 at 00:05

In short, there is a common formula for output dims calculation:

You can find explanation in A guide to receptive field arithmetic for Convolutional Neural Networks.

In addition, I'd like to recommend amazing article A guide to convolution arithmetic for deep learning.

And this repo conv_arithmetic with convolution animations.

Source https://stackoverflow.com/questions/56450969

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

Vulnerabilities

No vulnerabilities reported

Install yolo-v3

You can download it from GitHub.
You can use yolo-v3 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.