traffic-sign-detection

 by   itheima1 Python Version: Current License: No License

kandi X-RAY | traffic-sign-detection Summary

kandi X-RAY | traffic-sign-detection Summary

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

traffic-sign-detection
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            kandi-support Support

              traffic-sign-detection has a low active ecosystem.
              It has 2 star(s) with 0 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              traffic-sign-detection has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of traffic-sign-detection is current.

            kandi-Quality Quality

              traffic-sign-detection has 0 bugs and 0 code smells.

            kandi-Security Security

              traffic-sign-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              traffic-sign-detection code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              traffic-sign-detection does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              traffic-sign-detection releases are not available. You will need to build from source code and install.
              traffic-sign-detection has no build file. You will be need to create the build yourself to build the component from source.
              It has 225 lines of code, 5 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 traffic-sign-detection and discovered the below as its top functions. This is intended to give you an instant insight into traffic-sign-detection implemented functionality, and help decide if they suit your requirements.
            • Squeeze a tensor .
            • Fire a fire module
            • Create a tensorflow model .
            • Return the mapper for the given value .
            Get all kandi verified functions for this library.

            traffic-sign-detection Key Features

            No Key Features are available at this moment for traffic-sign-detection.

            traffic-sign-detection Examples and Code Snippets

            No Code Snippets are available at this moment for traffic-sign-detection.

            Community Discussions

            QUESTION

            powerful IDE to reduce time in training datasets in yolo/darkflow
            Asked 2021-Apr-04 at 20:39

            I'm a beginner in Machine Learning.
            I've been learning about YOLO and DarkFlow from the following links with Ubuntu 20.04: darkflow and Tiny YOLO.

            I successfully executed the code, and got the results like this:

            Statistics:
            car: 436
            person: 73
            Dataset size: 2599
            Dataset of 2599 instance(s)
            Training statistics:
            Learning rate : 1e-05
            Batch size : 16
            Epoch number : 1000
            Backup every : 2000

            It's not bad, but the training's taking way too much time.
            But I wanna know if there is any powerful IDE or other tools that can help me reduce time.
            I searched in google and tried to find many ways to improve.
            I heard that there are many ways to make training faster,(including Azure AI ML service) but since I'm a beginner, I cannot decide which will be the perfect choice to run YOLO and DarkFlow.

            I would appreciate advices about robust development environments, especially the ones that would be suitable in my current specific condition.
            Thanks in advance!

            ++) Since I'm a mere sophomore, the level of the hardware that I can use is very limited. I would also appreciate tools that can help me overcome the limitations of my hardware!

            ...

            ANSWER

            Answered 2021-Apr-04 at 20:39

            The IDE usually won't decrease computing time, but is rather based around the compiler. Likely, you'll just have to deal with intensive training times if you can't get better hardware. However, you might look into using a gpu to do computations(if you have one) rather than the standard way it runs on the cpu. Here's a link as to how to achieve this in Tensorflow: https://stackoverflow.com/a/51307381/14392018 . The general rule is the more data and the more complex the ML/DL model(i.e. the more layers in a neural network), the longer it takes to compute 1 epoch on the dataset. Also, the more data you're working with, the higher the computational intensity.

            Source https://stackoverflow.com/questions/66945435

            QUESTION

            Python.h not found even after I used sudo install
            Asked 2021-Apr-04 at 10:58

            I've been studying darkflow from the following link; https://github.com/thtrieu/darkflow
            on Ubuntu 20.04
            I thought there was a problem in my flow file, so I tried to rebuild the build file by the following code

            ...

            ANSWER

            Answered 2021-Apr-04 at 10:58

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

            Vulnerabilities

            No vulnerabilities reported

            Install traffic-sign-detection

            You can download it from GitHub.
            You can use traffic-sign-detection 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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          • HTTPS

            https://github.com/itheima1/traffic-sign-detection.git

          • CLI

            gh repo clone itheima1/traffic-sign-detection

          • sshUrl

            git@github.com:itheima1/traffic-sign-detection.git

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