object-detector | Object Detection Framework using HOG as descriptor | Machine Learning library

by bikz05 Python Version: Current License: MIT

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

kandi X-RAY | object-detector Summary

object-detector is a Python library typically used in Artificial Intelligence, Machine Learning applications. object-detector has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However object-detector build file is not available. You can download it from GitHub.

Object Detection Framework using HOG as descriptor and Linear SVM as classifier.

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object-detector has a low active ecosystem.

It has 387 star(s) with 218 fork(s). There are 38 watchers for this library.

It had no major release in the last 6 months.

There are 25 open issues and 5 have been closed. On average issues are closed in 106 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of object-detector is current.

Quality

object-detector has 0 bugs and 0 code smells.

Security

object-detector has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

object-detector code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

object-detector is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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object-detector releases are not available. You will need to build from source code and install.

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

Installation instructions are not available. Examples and code snippets are available.

object-detector saves you 75 person hours of effort in developing the same functionality from scratch.

It has 194 lines of code, 3 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Returns a list of detections based on confidence score .
Calculates the overlap between two two sets of points .

Get all kandi verified functions for this library.

object-detector Key Features

No Key Features are available at this moment for object-detector.

object-detector Examples and Code Snippets

No Code Snippets are available at this moment for object-detector.

Community Discussions

Trending Discussions on object-detector

Step size in YOLOv4 Model

Object Detector Training using TensorFlow

Is it possible to use polygon data annotation to perform tensorflow object detection?

QUESTION

Step size in YOLOv4 Model

Asked 2020-Sep-07 at 17:53

So I was reading this guide about object detector training. It is instructed to change step size to 80% and 90% of the maximum batch size. Can someone explain what this step size is? And how it impacts the training. And also why specifically 80 and 90 percent.

Thanks

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ANSWER

Answered 2020-Sep-07 at 17:53

The step mentioned in the guide determines the number of iterations at which scales will be applied. Check What does scale and step values in .cfg file of YOLO means?

Try not to call it step size, since the step size is something completely different from the step.

The step size is the learning rate, is a hyperparameter that defines how quickly the model adapts to the problem, learn more about learning Rate and its impact on training on the cited links.

Source https://stackoverflow.com/questions/63781432

QUESTION

Object Detector Training using TensorFlow

Asked 2020-Mar-04 at 16:51

I am hoping someone on here has experience in training object detection models with tensorflow. I am a complete newbie, trying to learn. I ran through a few of the tutorials on the tensorflow site and am now going to try a real world example. I am following the tutorial here. I am at the point where I need to label the images.

My plan is to try to detect scallops, but the images I using have several scallops. Some I wouldn't really be able to tell were scallops are other than the fact I have context that they are likely a scallop because they are next to a mound of other scallops.

My questions are:

Am I better off cutting them out and treating them individually? Or labeling images that have several scallops
When labeling the scallops there are many that might look just like a round rock if I didn't have context of seeing other scallops. Should I still label them?
I am guessing I will also need to find some images with differing backgrounds???.

I know I can experiment to see how the models perform, but labeling these images is a labour intensive task, so I am hoping I can borrow from someones experience who has attempted something similar in the past. Example of one of the images that I am part way through labeling:

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ANSWER

Answered 2020-Mar-04 at 16:51

1) Good question! The answer is easy, you should label the images as the model would see them at inference time. There's no reason to "lie" to your model (by not labeling something), you'll only confuse it. Be truthful, if you see a scallop, label it. If you don't label something, it's like a negative example, which will confuse the model. ==> A: multiple scallops

2) Seems like the model will take images of (many) scallops as input, so it's not a problem that it learns that 'round objects next to a mound of scallops are likely also a scallop', it's even a good thing, because they often are. So, again, be truthful, label everything.

3) That depends, how will you use the model at inference time? Will the images all have the same background then? If yes, you don't need different backgrounds, if no, you do need them.

Source https://stackoverflow.com/questions/60529002

QUESTION

Is it possible to use polygon data annotation to perform tensorflow object detection?

Asked 2020-Feb-19 at 07:10

My problem is not exactly annotate data using polygon, circle or line, it's how to use these annotated data to gerenate a ".tfrecord" file and perform an object detection. The tutorials I saw use rectangle annotation, like these: taylor swift detection raccon detection

It would be a great one for me if the objects I want to detect (pipelines) were not too close.

Example of rectangle drawn in PASCAL VOC format:

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ANSWER

Answered 2020-Feb-19 at 07:10

You can go for instance segmentation instead of object detection if your objects are very close to each other, there you can use polygons to generate masks and bounding boxes to train the model.

here is the well presented and easy to use the repository for mask-rcnn(kind of instance segmentation)

https://github.com/matterport/Mask_RCNN

check this for lite weight mask-rcnn

Source https://stackoverflow.com/questions/60280848

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

Vulnerabilities

No vulnerabilities reported

Install object-detector

You can download it from GitHub.
You can use object-detector 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

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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