Keras-DIOU-YOLOv3 | Keras上700行代码复现YOLOv3！使用DIOU loss。支持将模型导出为pytorch模型。

by miemie2013 Python Version: 0.1.0 License: No License

X-Ray Key Features Code Snippets(2)Community Discussions Vulnerabilities Install Support

kandi X-RAY | Keras-DIOU-YOLOv3 Summary

Keras-DIOU-YOLOv3 is a Python library. Keras-DIOU-YOLOv3 has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Keras上700行代码复现YOLOv3！使用DIOU loss。支持将模型导出为pytorch模型。

Support

Quality

Security

License

Reuse

Support

Keras-DIOU-YOLOv3 has a low active ecosystem.

It has 52 star(s) with 14 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 0 have been closed. On average issues are closed in 115 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Keras-DIOU-YOLOv3 is 0.1.0

Quality

Keras-DIOU-YOLOv3 has 0 bugs and 0 code smells.

Security

Keras-DIOU-YOLOv3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Keras-DIOU-YOLOv3 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Keras-DIOU-YOLOv3 does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Keras-DIOU-YOLOv3 releases are available to install and integrate.

Build file is available. You can build the component from source.

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

Top functions reviewed by kandi - BETA

kandi has reviewed Keras-DIOU-YOLOv3 and discovered the below as its top functions. This is intended to give you an instant insight into Keras-DIOU-YOLOv3 implemented functionality, and help decide if they suit your requirements.

Draws a plot function for the given dictionary
Adjust axes limits
Evaluate the model
Detects the given image
Compute training image
Transform an image
Compute the precision of the given precision
Generates a single batch image
Parse annotation
Randomly crop a bounding box
Preprocess image
Detects the video in the given video
Draws boxes
Predict boxes
Process an image
Yolo loss function
Layer loss layer
Concatenate two boxes
Decode anchors
Draw text inside the image
Batch normalization
Combine two convolutions
Read lines from txt file
Returns the list of class names
Stack a single residual block
Forward a sequence of sequential blocks

Get all kandi verified functions for this library.

Keras-DIOU-YOLOv3 Key Features

No Key Features are available at this moment for Keras-DIOU-YOLOv3.

Keras-DIOU-YOLOv3 Examples and Code Snippets

Keras-DIOU-YOLOv3,仓库文件介绍

Python

Lines of Code : 16

License : No License

Copy

train.py            训练yolov3，用的是ciou loss。
1_lambda2model.py   将训练模型中yolov3的所有部分提取出来。
2_keras2pytorch.py  将keras模型导出为pytorch模型。
demo_kr.py          用keras模型进行预测。对视频进行预测的话需要解除注释。
demo_pt.py          用pytorch模型进行预测。对视频进行预测的话需要解除注释。
evaluate_kr.py

Keras-DIOU-YOLOv3,训练

Python

Lines of Code : 7

License : No License

Copy

train_path = 'annotation/coco2017_train.txt'
val_path = 'annotation/coco2017_val.txt'
classes_path = 'data/coco_classes.txt'

xxx/xxx.jpg 18.19,6.32,424.13,421.83,20 323.86,2.65,640.0,421.94,20 
xxx/xxx.jpg 48,240,195,371,11 8,12,352,498,14
# image_p

Community Discussions

No Community Discussions are available at this moment for Keras-DIOU-YOLOv3.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install Keras-DIOU-YOLOv3

You can download it from GitHub.
You can use Keras-DIOU-YOLOv3 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 .

Find more information at: