yolo | air installation and testing of mobile applications | iOS library

 by   berty Go Version: v2.102.0 License: Non-SPDX

kandi X-RAY | yolo Summary

kandi X-RAY | yolo Summary

yolo is a Go library typically used in Mobile, iOS, React Native, Xcode applications. yolo has no bugs, it has no vulnerabilities and it has low support. However yolo has a Non-SPDX License. You can download it from GitHub.

Yolo is one-stop realtime feed of ready-to-install releases and tests for your apps. We're using it at berty to make releases and test branches ready to download and use on a developer's device within minutes after passing our CI. We created Yolo to implement the critical features missing from Apple's TestFlight.
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            kandi-support Support

              yolo has a low active ecosystem.
              It has 56 star(s) with 12 fork(s). There are 10 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 8 open issues and 155 have been closed. On average issues are closed in 157 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of yolo is v2.102.0

            kandi-Quality Quality

              yolo has 0 bugs and 305 code smells.

            kandi-Security Security

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

            kandi-License License

              yolo has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              yolo releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              It has 17690 lines of code, 670 functions and 125 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed yolo and discovered the below as its top functions. This is intended to give you an instant insight into yolo implemented functionality, and help decide if they suit your requirements.
            • Icon icon list
            • Draw an icon
            • reducer reducer to update state
            • Refreshes the new state
            • set previous value
            • tick callback
            Get all kandi verified functions for this library.

            yolo Key Features

            No Key Features are available at this moment for yolo.

            yolo Examples and Code Snippets

            Call the official API
            Godot img1Lines of Code : 79dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            # last build for the berty app for iOS
            curl -su :TOKEN "https://yolo.berty.io/api/build-list?project_id=https://github.com/berty/berty&artifact_kinds=1" | jq '.builds[0]'
            
            {
              "id": "https://buildkite.com/berty/berty-open/builds/535",
              "created_  
            CLI usage
            Godot img2Lines of Code : 33dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            $ yolo -h
            USAGE
              server [flags] 
            
            SUBCOMMANDS
              server        Start a Yolo Server
              dump-objects
              info
            
            FLAGS
              -v false  increase log verbosity
            
            $ yolo server -h
            USAGE
              server
            
            FLAGS
              -auth-salt ...             salt used to generate authenticati  

            Community Discussions

            QUESTION

            Is there an R function to help turn State abbreviations into full names? Or Vice Versa?
            Asked 2022-Apr-18 at 03:52

            I have two large-ish data frames I am trying to append...

            In df1, I have state codes, county codes, state names (Alabama, Alaska, etc.), county names, and years from 2010:2020.

            In df2, I have county names, state abbreviations (AL, AK), and data for the year 2010 (which I am trying to merge into df1. The issue lies in that without specifying the state name and simply merging df1 and df2, some of the data which I am trying to get into df1 is duplicated due to there being some counties with the same name...hence, I am trying to also join by state to prevent this, but I have state abbreviations, and state names.

            Is there any way in which I can make either the state names in df1 abbreviations, or the state names in df2 full names? Please let me know! Thank you for the help.

            Edit: dput(df2)

            ...

            ANSWER

            Answered 2022-Apr-18 at 03:52

            Here's one way you could turn state abbreviations into state names using R's built in state vectors:

            Source https://stackoverflow.com/questions/71907258

            QUESTION

            Does Batch Size matter if I only care about deploying and not training?
            Asked 2022-Apr-11 at 17:09

            Gonna ask a newbie question...

            If I finished training my model, or I am using trained model like YOLO. And I want to put the model on a robot that has a 6GB VRAM. In this case, do I need to concern about the batch size at all?

            I am trying to find out if models like YOLO will fit in my GPU. Thank you

            ...

            ANSWER

            Answered 2022-Apr-11 at 17:09

            Generally speaking, batch size can be adjusted at any time without creating a problem. Each element of a batch is independent, but they are fed through the network together for efficiency reasons.

            Note that batch size does affect training quality, as the gradients from larger batches will average out and have less variance. But that is irrelevant when doing inference (actually using the model).

            You also asked what prevents you deploying a huge model on a small GPU, and the answer is simply performance. It is entirely possible to load part of a large model onto the GPU, run that part, load the next part, run it, and so on. You would need to balance the batch size and model part size, because if you only use a batch size of 1, the continual copying of model parameters will probably make it slower than running the whole model on the CPU.

            Source https://stackoverflow.com/questions/71788044

            QUESTION

            What does this syntax mean: `function (YOLO) ....`
            Asked 2022-Mar-05 at 03:43

            What does the code mean, and how to invoke it?

            ...

            ANSWER

            Answered 2022-Mar-05 at 03:43

            It's an Anonymous function.

            The usual way to use them is to either assign it to a variable, which would become the function's name:

            Source https://stackoverflow.com/questions/71358822

            QUESTION

            Boost serialization of interface map
            Asked 2022-Feb-28 at 15:10

            I have created a map of multiple different data types. s64, f64, Arrays, Images, etc. To do so, i used a map of type std::map> database;. I want to store it, and reload it from filesystem. But i heard that maps cant be sored in a one-liner. So i tried to store the data part std::unique_ptr test; of one pair first:

            ...

            ANSWER

            Answered 2022-Feb-28 at 15:10

            I spent a large amount of time making things self-contained. Among the many changes:

            1. you should not have the base class serializing the derived (that's classic OOP), instead Boost expects derived classes to serialize their base_object<> (allowing static polymorphism and, incidentally, type registration)

            2. of course, the base class should serialize its data members (type)

            3. the base class SHOULD have a virtual destructor (otherwise deleting through unique_ptr's destructor will be unspecified

              Up till here:

            Source https://stackoverflow.com/questions/71291890

            QUESTION

            try and except do not work with tensorflow exceptions
            Asked 2022-Feb-14 at 16:16

            You'll need this notebook to reproduce the error which downloads the files below and runs the exact same code following the description.

            • labels.csv: each row contains x0, y0, x1, y1 text coordinates, and other columns not affecting the outcome.
            • yolo-train-0.tfrecord: Contains 90% of the examples found in labels.csv. Each example contains all labels/rows corresponding to the image in the example.

            I'm experiencing a recurring error that happens when iterating over a tfrecord dataset. After 2000-4000 iterations that successfully read batches from the dataset, I get the following error:

            ...

            ANSWER

            Answered 2022-Feb-14 at 16:16

            Wrapping the transform_targets_for_output method with a try-except-raise clause and applying tf.data.experimental.ignore_errors to the dataset seems to actually work:

            Source https://stackoverflow.com/questions/71113392

            QUESTION

            How to upload an Image File modified by OpenCV using FileSystemStorage in Django?
            Asked 2022-Feb-13 at 16:57

            I am taking an uploaded image from the user and then sending it to a YOLO model which then returns me an image.

            I want to store that returned image in my Local Directory and then display it on the user interface.

            This is the Code of views.py that takes in an image and sends it to the Yolo Model,

            ...

            ANSWER

            Answered 2022-Feb-13 at 16:57

            You can use the imwrite function of cv2 library to store your files in the local directory, i.e.,

            In your case, simply do this,

            Source https://stackoverflow.com/questions/71100904

            QUESTION

            Exporting Yolov5 bboxes to Pascal format
            Asked 2022-Jan-26 at 14:19

            There have been already some similar style questions asked before (1, 2) However, none have mentioned the new Yolov5 style annotations.

            Is there a simple function that takes in a normalized Yolov5 bounding box like:-

            ...

            ANSWER

            Answered 2021-Nov-17 at 18:39

            There is no direct way to convert the normalized Yolo format to another format like Pascal VOC, because you need to know the size of the image to do the conversion. (Just like you need to know the size of the image to convert it to the normalized yolo format in the first place.) You will also want to provide some mapping to convert the class numbers to class names.

            I am working on a Python package to simplify these kinds of conversions called PyLabel. I have a sample notebook that will convert Yolo v5 annotations to VOC format here https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb. You can open it in Colab using this link.

            The core code will be something like this:

            Source https://stackoverflow.com/questions/69994481

            QUESTION

            convert boundingPoly to yolo format
            Asked 2021-Dec-21 at 05:08

            Yolov5 doesn't support segmentation labels and I need to convert it into the correct format.

            How would you convert this to yolo format?

            ...

            ANSWER

            Answered 2021-Dec-21 at 00:34

            After our back and forth in the comments I have enough info to answer your question. This is output from the Google Vision API. The normalizedVertices are similar to the YOLO format, because they are "normalized" meaning the coordinates are scaled between 0 and 1 as opposed to being pixels from 1 to n. Still, you need to do some transformation to put into the YOLO format. In the YOLO format, the X and Y values in the 2nd and 3rd columns refer to the center of the bounding box, as opposed to one of the corners.

            Here is a code snipped that will sample at https://ghostbin.com/hOoaz/raw into the follow string in YOLO format '0 0.5080664305 0.5624289849999999 0.9786587390000001 0.56914843'

            Source https://stackoverflow.com/questions/70420807

            QUESTION

            TypeError: load() missing 1 required positional argument: 'Loader'
            Asked 2021-Nov-23 at 17:20

            I am trying to run this github repo found at this link: https://github.com/HowieMa/DeepSORT_YOLOv5_Pytorch After installing the requirements via pip install -r requirements.txt. I am running this in a python 3.8 virtual environment, on a dji manifold 2g which runs on an Nvidia jetson tx2.

            The following is the terminal output.

            ...

            ANSWER

            Answered 2021-Nov-11 at 05:39

            QUESTION

            Why are the two results different in YOLOv5?
            Asked 2021-Oct-24 at 21:48

            I wanted to know the number of vehicles in the picture using yolov5 However, the result of the model was different from detect.py

            0. img

            1. model_result

            ...

            ANSWER

            Answered 2021-Oct-24 at 21:48

            It seems it is an image processing issue.

            Indeed with your example, the model loaded from torch hub gives a different output than detect.py. Looking at the source code of detect.py I see that there is some good image pre-processing. From the model hub, I really don't know what's happening to the input. From the model hub, the resulting image is this:

            With their pre-processing, this is basically the image that you are feeding into the model. Would not expect any detections from this to be honest.

            But then I tried doing the pre-processing myself (noted as well in their tutorial)

            Source https://stackoverflow.com/questions/69694574

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

            Vulnerabilities

            No vulnerabilities reported

            Install yolo

            Or grab the last Docker image available on the official Docker Registry. You can start from the official deployment configuration available in ./deployments/yolo.berty.io.

            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://github.com/berty/yolo.git

          • CLI

            gh repo clone berty/yolo

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            git@github.com:berty/yolo.git

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