Pytorch-DIOU-YOLOv3 | Pytorch复现YOLOv3，使用最新的DIOU loss训练

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 | Pytorch-DIOU-YOLOv3 Summary

Pytorch-DIOU-YOLOv3 is a Python library. Pytorch-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.

Pytorch复现YOLOv3，使用最新的DIOU loss训练

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Quality

Security

License

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Support

Pytorch-DIOU-YOLOv3 has a low active ecosystem.

It has 56 star(s) with 22 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 9 open issues and 1 have been closed. On average issues are closed in 21 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Pytorch-DIOU-YOLOv3 is 0.1.0

Quality

Pytorch-DIOU-YOLOv3 has 0 bugs and 70 code smells.

Security

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

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

There are 8 security hotspots that need review.

License

Pytorch-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

Pytorch-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.

Pytorch-DIOU-YOLOv3 saves you 816 person hours of effort in developing the same functionality from scratch.

It has 1873 lines of code, 60 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Detects the video using cv2
Transform an image into a numpy array
Process an image
Draws images
Performs a multi - threaded multi - thread read
Random crop
Preprocess an image
Parse image annotation
Evaluate the model
Predict boxes
Takes an image and returns a list of bounding boxes
Runs the yolo output
Batch normalization
Find the indices of the first layer in the model
Prepare the weight matrix
Stack a residual block
Conv2D convolution layer
Draws the plot function for the given dictionary
Adjust axes limits
Draw text inside an image
Conv2D convolutional layer
Find the first layer in base_model
Read lines from txt file
Set the weight matrix
This function adds the weight of the convolutional
Generate one batch
Compute the precision of the given precision
Returns a list of class names
Forward the forward method

Get all kandi verified functions for this library.

Pytorch-DIOU-YOLOv3 Key Features

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

Pytorch-DIOU-YOLOv3 Examples and Code Snippets

Pytorch-DIOU-YOLOv3,仓库文件介绍

Python

Lines of Code : 12

License : No License

Copy

train.py            训练yolov3，用的是ciou loss。
2_pytorch2keras.py  将pytorch模型导出为keras模型。给兄弟仓库兄弟版：https://github.com/miemie2013/Keras-DIOU-YOLOv3使用。
demo.py             用pytorch模型进行预测。对视频进行预测的话需要解除注释。
eval.py             对pytorch模型评估。跑完这个脚本后需要再跑mAP/main.p

Pytorch-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 Pytorch-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 Pytorch-DIOU-YOLOv3

You can download it from GitHub.
You can use Pytorch-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 .

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