AutoSTR | Efficient Backbone Search for Scene Text Recognition | Computer Vision library

 by   AutoML-4Paradigm Python Version: Current License: No License

kandi X-RAY | AutoSTR Summary

kandi X-RAY | AutoSTR Summary

AutoSTR is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. AutoSTR has no bugs, it has no vulnerabilities and it has low support. However AutoSTR build file is not available. You can download it from GitHub.

We investigate how to obtain a strong feature sequence extractor for scene text recognition task by neural architecture search technology. The research paper can be found here ECCV. 2020.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              AutoSTR has a low active ecosystem.
              It has 64 star(s) with 15 fork(s). There are 10 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 3 open issues and 5 have been closed. On average issues are closed in 40 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of AutoSTR is current.

            kandi-Quality Quality

              AutoSTR has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              AutoSTR 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

              AutoSTR releases are not available. You will need to build from source code and install.
              AutoSTR 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.
              AutoSTR saves you 1941 person hours of effort in developing the same functionality from scratch.
              It has 4275 lines of code, 308 functions and 38 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of AutoSTR
            Get all kandi verified functions for this library.

            AutoSTR Key Features

            No Key Features are available at this moment for AutoSTR.

            AutoSTR Examples and Code Snippets

            No Code Snippets are available at this moment for AutoSTR.

            Community Discussions

            QUESTION

            Protobuf vs Flatbuffers vs Cap'n proto which is faster?
            Asked 2020-Apr-22 at 17:15

            I decided to figure out which of Protobuf, Flatbuffers and Cap'n proto would be the best/fastest serialization for my application. In my case sending some kind of byte/char array over a network (the reason I serialized to that format). So I made simple implementations for all three where i seialize and dezerialize a string, a float and an int. This gave unexpected resutls: Protobuf being the fastest. I would call them unexpected since both cap'n proto and flatbuffes "claims" to be faster options. Before I accept this I would like to see if I unitentionally cheated in my code somehow. If i did not cheat I would like to know why protobuf is faster (exactly why is probably impossible). Could the messages be to simeple for cap'n proto and faltbuffers to really make them shine?

            My timings:

            Time taken flatbuffers: 14162 microseconds
            Time taken capnp: 60259 microseconds
            Time taken protobuf: 12131 microseconds
            (obviously these are dependent on my machine but it is the relative time that matters)

            flatbuffer code:

            ...

            ANSWER

            Answered 2020-Apr-22 at 17:15

            In Cap'n Proto, you should not reuse a MessageBuilder for multiple messages. The way you've written your code, every iteration of your loop will make the message bigger, because you're actually adding on to the existing message rather than starting a new one. To avoid memory allocation with each iteration, you should pass a scratch buffer to MallocMessageBuilder's constructor. The scratch buffer can be allocated once outside the loop, but you need to create a new MallocMessageBuilder each time around the loop. (Of course, most people don't bother with scratch buffers and just let MallocMessageBuilder do its own allocation, but if you choose that path in this benchmark, then you should also change the Protobuf benchmark to create a new message object for every iteration rather than reusing a single object.)

            Additionally, your Cap'n Proto code is using capnp::messageToFlatArray(), which allocates a whole new buffer to put the message into and copies the entire message over. This is not the most efficient way to use Cap'n Proto. Normally, if you were writing the message to a file or socket, you would write directly from the message's original backing buffer(s) without making this copy. Try doing this instead:

            Source https://stackoverflow.com/questions/61347404

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

            Vulnerabilities

            No vulnerabilities reported

            Install AutoSTR

            You can download it from GitHub.
            You can use AutoSTR 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:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/AutoML-4Paradigm/AutoSTR.git

          • CLI

            gh repo clone AutoML-4Paradigm/AutoSTR

          • sshUrl

            git@github.com:AutoML-4Paradigm/AutoSTR.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Consider Popular Computer Vision Libraries

            opencv

            by opencv

            tesseract

            by tesseract-ocr

            face_recognition

            by ageitgey

            tesseract.js

            by naptha

            Detectron

            by facebookresearch

            Try Top Libraries by AutoML-4Paradigm

            AutoSF

            by AutoML-4ParadigmPython

            Interstellar

            by AutoML-4ParadigmPython

            S2E

            by AutoML-4ParadigmPython

            SNAG

            by AutoML-4ParadigmPython

            NSCaching

            by AutoML-4ParadigmPython