cvat | Powerful and efficient Computer Vision Annotation Tool | Data Labeling library

 by   openvinotoolkit TypeScript Version: v1.7.0 License: Non-SPDX

kandi X-RAY | cvat Summary

kandi X-RAY | cvat Summary

cvat is a TypeScript library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Data Labeling, Deep Learning applications. cvat has no bugs, it has no vulnerabilities and it has medium support. However cvat has a Non-SPDX License. You can download it from GitHub.

CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Try it online

            kandi-support Support

              cvat has a medium active ecosystem.
              It has 6542 star(s) with 1745 fork(s). There are 156 watchers for this library.
              It had no major release in the last 12 months.
              There are 309 open issues and 1554 have been closed. On average issues are closed in 123 days. There are 9 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of cvat is v1.7.0

            kandi-Quality Quality

              cvat has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              cvat 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

              cvat releases are available to install and integrate.
              cvat saves you 15613 person hours of effort in developing the same functionality from scratch.
              It has 30404 lines of code, 1841 functions and 781 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed cvat and discovered the below as its top functions. This is intended to give you an instant insight into cvat implemented functionality, and help decide if they suit your requirements.
            • creates a new thread
            • Get a copy of shapes for the specified frame .
            • Create an XmlDumper for a file .
            • dump a track as an interpolation
            • get old db paths
            • Generate task data objects .
            • dump annotations as image .
            • Save old tracks to db .
            • Migrates task data to new video data
            • Update the local repository .
            Get all kandi verified functions for this library.

            cvat Key Features

            No Key Features are available at this moment for cvat.

            cvat Examples and Code Snippets

            No Code Snippets are available at this moment for cvat.

            Community Discussions


            Build confusion matrix for instance segmantation (mask r-cnn from detectron2)
            Asked 2022-Mar-18 at 16:54

            I've trained a mask r-cnn on corn images (I cannot show examples because they are confidential), but they are basically pictures of corn kernels scattered over a flat surface.

            There are different kinds of corn kernels I want to be able to segment and classify. I understand the AP metrics are the best way of measuring the performance of an instance segmentation algorithm and I know a confusion matrix for this kind of algorithm doesn't usually make sense.

            But for his specific case, where I have 4 classes of very similar objects, I would like to be able to set a fixed AP value, like AP50/AP75 and build a confusion matrix for that.

            Would it be possible? How would I do it?

            I used detectron2 library to train and get predictions. Here is the code I use to load my trained model from disk, generate predictions in the validation set, and visualize the results:



            Answered 2022-Mar-18 at 16:54

            I was able to do it, I built the confusion matrix function from scratch:



            Crop the labeled are into a separate image in Computer Vision Annotation (CVAT)?
            Asked 2021-Dec-11 at 08:42

            I have a set of frames in which different animals are visible. I have annotated them using CVAT's polygon feature. Now, all I need to do is cropping the annotation part and extracting the features as a CSV file.

            I can extract the features using VGG16 in MATLAB using a code like below:



            Answered 2021-Dec-11 at 08:42

            First, I uploaded the COCO results of CVAT with the output images to Roboflow and cropped the images based on the defined rectangle box in COCO (JSON) file.

            After that, as I had three different classes, I created three different folders and named them like my classes. Then, I copied cropped images of each class to the relevant folder.

            Finally, using MATLAB, I ran the following code to extract the features using vgg16:



            Data annotation for mask rcnn
            Asked 2021-Nov-19 at 04:45

            Is it mandatory to annotate images using polygon shapes for mask rcnn? I read the and the research paper as well. It seems that matterport's implementation can take bounding box as well as polygon as annotations. Although I am not certain. So should I consider bounding box annotation for my dataset? or polygon annotation?

            Currently I have annotated some images using bounding box on Intel's CVAT.



            Answered 2021-Nov-19 at 04:45

            If you have a look COCO dataset, you can see it has 2 types of annotation format - bounding box and mask(polygon). Therefore, Mast RCNN is to predict 3 outputs - Label prediction, Bounding box prediction, Mask prediction. So, if you want Semantic Segmentation, you should have the polygon annotations for your dataset, but if you want only object detection, bounding box annotations are enough.



            Image annotation tool that support annotation using existing CNN
            Asked 2020-Dec-06 at 02:36

            I have trained a YoloV4 CNN. It's pretty good already. I want more images as training data but there is no point of manually annotate most of the stuff because CNN can do it for me. I could review and re-correct if there are any issues. Is there a image annotation tool/service that can do that? I'm currently using Supervisely. I also tried CVAT and VoTT Couldn't find such feature.



            Answered 2020-Dec-06 at 02:36

            I created a simple python project to generate supervisely project using darknet. It's available on github.




            Training out false positives in object detection
            Asked 2020-Oct-22 at 07:59

            This is my first foray into the world of object recognition. I have successfully trained a model on yolo with images that I have found on Google and annotated myself in CVAT.

            My questions are as follows.

            a) How do I train the model to ignore some special variant that I am specifically NOT interested in detecting? Say I am getting false positives because something looks similar to one of my objects, and I want to train so that these are not detected. Does it simply work to include images that contain the unwanted object into the training set, but don't annotate the unwanted object?

            b) If so, am I right in assuming that if I train on annotated images that have somehow missed occasional instances of desired objects, is that effectively telling the training engine that I'm not interested in that object? In other words, is it therefore BAD if images don't have every single instance of desired objects annotated?

            c) If I happen to include an image in my training set with an empty annotation file, and there are desired objects in that image, that effectively disincentivizes the training engine to find those in future?

            Thanks for any thoughts.



            Answered 2020-Oct-22 at 07:59

            a) This is true. The model will consider space inside bounding boxes as positive for a certain class during training, and space outside the boxes for the class negative for that class.

            b) See a, this is indeed the case.

            c) Empty annotation files will be used during training, but the model will train on that image as a 'background' class, so these are negatives too.

            So, in short, annotate all instances of objects of a certain class and maybe add 'background images' as negative examples to disincentivize those.



            Unable to create superuser in cvat
            Asked 2020-Feb-26 at 02:14

            I am able to build and run cvat tool. But when I trying to create a superuser then it is giving me below error.

            ImportError: No module named 'gitdb.utils.compat'

            I am running below command for creating a superuser.

            docker exec -it cvat bash -ic 'python3 ~/ createsuperuser'

            Does anyone have any idea or suggestion for the above problem?



            Answered 2020-Feb-26 at 02:14

            It seems the newer version of gitdb does not work with cvat (default version is 4.0.2), you can follow Furkan Kirac answer but with gitdb version is 0.6.4:


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


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            Install cvat

            You can download it from GitHub.


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