keras-yolo3 | A Keras implementation of YOLOv3 | Machine Learning library

by qqwweee Python Version: Current License: MIT

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

kandi X-RAY | keras-yolo3 Summary

keras-yolo3 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-yolo3 has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However keras-yolo3 build file is not available. You can download it from GitHub.

A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.

Support

Quality

Security

License

Reuse

Support

keras-yolo3 has a medium active ecosystem.

It has 7100 star(s) with 3499 fork(s). There are 198 watchers for this library.

It had no major release in the last 6 months.

There are 484 open issues and 227 have been closed. On average issues are closed in 73 days. There are 36 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-yolo3 is current.

Quality

keras-yolo3 has 0 bugs and 0 code smells.

Security

keras-yolo3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-yolo3 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-yolo3 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

keras-yolo3 releases are not available. You will need to build from source code and install.

keras-yolo3 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.

keras-yolo3 saves you 550 person hours of effort in developing the same functionality from scratch.

It has 1288 lines of code, 52 functions and 11 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed keras-yolo3 and discovered the below as its top functions. This is intended to give you an instant insight into keras-yolo3 implemented functionality, and help decide if they suit your requirements.

Create a YOLOv3 model
Layer normalization
Generate the body of the darknet
Darknet convolution layer
Generate keras model
Computes the boxes and scores for the given anchors
Wrapper for yolo evaluation
Construct a tiny yolo body
Compute the loss for the given anchors
Compute the intersection of two boxes
Yolo head
Bottleneck bottleneck generator
Generate random image data
Preprocess the true boxes
Detect video
Create a letterbox image
Detects the given image
Create a Tiny YolOv3 model
Wrapper function for data_generator
Return the default value of an attribute
Read anchors from file
Get the list of class names
Convert text file to text format
Detect image
Convert an annotation file to XML format
Return a stream of unique section names

Get all kandi verified functions for this library.

keras-yolo3 Key Features

No Key Features are available at this moment for keras-yolo3.

keras-yolo3 Examples and Code Snippets

Dataset

Python

Lines of Code : 73

License : Permissive (MIT)

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usage: data_preparer.py [-h] --dataset
                        {bosch_small_traffic_lights,vatsal_srivastava_traffic_lights,yolo_mark}
                        [--fliplr] [--scale] [--balance [B]] [--pick N]
                        [--resize H W] --in

Discussion

Jupyter Notebook

Lines of Code : 19

License : No License

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Bulat, A. and Tzimiropoulos, G., “How far are we from solving the
  2d & 3d face alignment problem? (and a dataset of 230,000 3d
  facial landmarks),” in International Conference on Computer
  Vision (2017).
Krafka, K., Khosla, A., Kellnhofer, P.

keras-yolo3在windows系统上,Quick Start,Usage

Python

Lines of Code : 17

License : No License

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usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
                     [--classes CLASSES] [--gpu_num GPU_NUM] [--image]
                     [--input] [--output]

positional arguments:
  --input        Video input path
  --output       V

Community Discussions

Trending Discussions on keras-yolo3

EDGE_TPU COMPILER ERROR: Didn't find op for builtin opcode 'RESIZE_NEAREST_NEIGHBOR' version '3' for custom YOLO

How to setup coremltools convert model output shape using yolo.h5

what exactly is the difference between warning and error in jupyter notebook?

QUESTION

EDGE_TPU COMPILER ERROR: Didn't find op for builtin opcode 'RESIZE_NEAREST_NEIGHBOR' version '3' for custom YOLO

Asked 2020-Oct-09 at 10:07

I have retrained my model on Darknet and use https://github.com/qqwweee/keras-yolo3 to convert my darknet weights to h5.

I have replace relu for leaky relu for quantization purpose. My model then has been converted to tflite model successfully by using tf-nightly.

However, I can't parse the model to edgetpu by resize nearest neighbor error. To my understanding, resize nearest neighbor is supported in https://coral.ai/docs/edgetpu/models-intro/#supported-operations So why did this error happened? Any way to fix?

Here is my tflite convert code:

...

ANSWER

Answered 2020-Jun-11 at 14:13

I'm from the coral team and I'm planning on investigating the yolov3 model myself, just haven't got the bandwidth to do so yet :) Here are some tips from what I've gathered:

Users have been able to successfully compile the model after changing the leaky_relu to relu, although accuracy may decrease. I know you've mention this, but I wanted to put this on the list for other users to reference.
Secondly, I suspect that you are using tf-nightly or some newer tf version for your conversion? If so, I suggest downgrading to maybe tf2.2, some newer version of the same ops are not yet supported by the compiler.
Try turning off MLIR converter also, released version of the edgetpu_compiler doesn't play well with MLIR

lmk if you found some success, would love to give this a shot also! fyi: I got yolov4 converted but the architect only allows 1/962 ops to run on the edgetpu so it's a bummer no go.

Source https://stackoverflow.com/questions/62321919

QUESTION

How to setup coremltools convert model output shape using yolo.h5

Asked 2020-May-01 at 09:59

I try to convert h5 to iOS mlModel.

I follow Quick Start to get h5 file by this link.

https://github.com/qqwweee/keras-yolo3

Then, I use coremltools to convert h5 to mlModel

this is my code

...

ANSWER

Answered 2020-Mar-15 at 19:51

1x1x255x13x13 is the same thing as 255x13x13 except that you have 5 dimensions instead of 3.

If you want the mlmodel to output 255x13x13, you'll have to fill in the output shape in the spec.description.output using coremltools.

Source https://stackoverflow.com/questions/60681890

QUESTION

what exactly is the difference between warning and error in jupyter notebook?

Asked 2020-Apr-03 at 00:13

I am new to the tensorflow and programing in general. I am following an instruction in github (https://github.com/experiencor/keras-yolo3) to learn object detection by YOLO-3. after running code below:

...

ANSWER

Answered 2020-Apr-02 at 21:50

Answer one : long story short, a deprecated function is an old one, replaced by something (hopefully) better, and still there for retro-compatibility. You can use it but will not get the latest development/support and, at some point, your code will not be functional anymore (since the faith of a deprecated function is to disappear in a future release).

Answer two :

Warning messages are typically issued in situations where it is useful to alert the user of some condition in a program, where that condition (normally) doesn’t warrant raising an exception and terminating the program. For example, one might want to issue a warning when a program uses an obsolete module.

https://docs.python.org/3/library/warnings.html

All in all, here, the interpreter just warms you that you are using a function that you will not be able to use in the future.

Source https://stackoverflow.com/questions/61001549

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

Vulnerabilities

No vulnerabilities reported

Install keras-yolo3

For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with --model model_file and --anchors anchor_file.
Download YOLOv3 weights from YOLO website.
Convert the Darknet YOLO model to a Keras model.
Run YOLO detection.

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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