AveragePrecision

by Ao-Lee Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | AveragePrecision Summary

AveragePrecision is a Python library. AveragePrecision has no bugs, it has no vulnerabilities and it has low support. However AveragePrecision build file is not available. You can download it from GitHub.

AveragePrecision

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AveragePrecision has a low active ecosystem.

It has 80 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

AveragePrecision has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of AveragePrecision is current.

Quality

AveragePrecision has 0 bugs and 12 code smells.

Security

AveragePrecision has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

AveragePrecision code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

AveragePrecision 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

AveragePrecision releases are not available. You will need to build from source code and install.

AveragePrecision 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.

AveragePrecision saves you 82 person hours of effort in developing the same functionality from scratch.

It has 210 lines of code, 10 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Generate fake fake fake data
Generate image data
Generates a prediction box
Generate random boxes
Generate random file names
Calculates the average precision of each image
Calculate the VocAveragePrecision
Compute the intersection of the given boxes

Get all kandi verified functions for this library.

AveragePrecision Key Features

No Key Features are available at this moment for AveragePrecision.

AveragePrecision Examples and Code Snippets

No Code Snippets are available at this moment for AveragePrecision.

Community Discussions

Trending Discussions on AveragePrecision

mean average precision (mAP) in tensorflow

QUESTION

mean average precision (mAP) in tensorflow

Asked 2018-Mar-29 at 09:02

I need to calculate the mAP described in this question for object detection using Tensorflow.

Average precision(AP) is a typical performance measure used for ranked sets. AveragePrecision is defined as the average of the precision scores after each true positive, TP in the scope S. Given a scope S = 7,and a ranked list (gain vector) G = [1,1,0,1,1,0,0,1,1,0,1,0,0,..] where 1/0 indicate the gains associated to relevant/non-‐relevant items, respectively:

AP = (1/1 + 2/2 + 3/4 + 4/5) / 4 = 0.8875.

Mean Average Precision (mAP): average of the average precision value for a set of queries.

i got 5 One-Hot tensors with the predictions:

...

ANSWER

Answered 2018-Mar-29 at 09:02

I think you may need this one:

Source https://stackoverflow.com/questions/43286682

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

Vulnerabilities

No vulnerabilities reported

Install AveragePrecision

You can download it from GitHub.
You can use AveragePrecision 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

What if many detections have high IOUs to the same ground truth object? Are those detections all considered as true positive? No. In this case, only one detection is counted as TP, other detections are false positives. I have a multiclass detection scenario, and how to compute MAP among all classes? provoide the ground truth, the predicton and the total number of ground truth objects for each paticular class, compute AP for each class, and average the results.

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