KopiKat generates a new, visually realistic duplicate of the original image, maintaining all critical data annotations. It alters the environment of the original images, for instance, adjusting factors like weather, seasons, and lighting conditions to add variety to datasets. This is crucial for fields such as object detection, neural network training, and transfer learning.
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KopiKat's answer
Our goal with Kopikat is to strengthen practical applications, especially in scenarios where collecting an extensive dataset proves to be difficult. Kopikat is ideally designed for datasets containing up to 5,000 images, a common feature of numerous real-world AI initiatives. It equips engineers with the ability to enhance mean average precision (mAP), broaden and vary datasets—a critical edge in fields like object detection, neural network training, and transfer learning.
KopiKat's answer
KopiKat's operation is remarkably simple and efficient for its users. All a user has to do is upload one image from their dataset. KopiKat then produces numerous images showcasing different scenarios, like alterations in illumination or weather, all the while preserving the annotations consistently. This attribute considerably expands the diversity of the dataset without requiring extra images, and creates a comprehensive, superior-quality model that introduces diversity beyond what traditional data augmentation techniques can offer. This method has demonstrated an improvement of over 5% in mean average precision (mAP), without any alterations to the AI model.
Based on our record, Supervisely seems to be more popular. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Another annotation tool that integrates prediction and training within the application is supervisely supervisely.com., unfortunately it's pretty expensive unless you are satisfied with the community version. I saw that they have an integration for owl-vit, which might be helpful for annotation of animals. https://ecosystem.supervisely.com/apps/serve-owl-vit. Source: about 1 year ago
Hello world. This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. It will teach you the main ideas of how to use Keras and Supervisely for this problem. This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start. - Source: dev.to / over 1 year ago
If they were videos, I would have suggested trying supervise.ly as it has a very good tracking functionality. Source: almost 2 years ago
Hi, I'm exactly in the same boat like you are. I looked around for a while and the better solutions I found was supervise.ly and CVAT for video annotation. The pricetag on supervisely is pretty high, so I analyzed CVAT for a couple days and was positively surprised. Source: almost 2 years ago
Under the WPI Photo Ambum section of the page for FRC field photos (https://www.firstinspires.org/robotics/frc/playing-field#WPIPhotos), they have a section of machine learning imagery. However, this link goes to supervise.ly, the website they use for machine learning. I created an account to attempt to download the images, however, whenever I try to 'clone' the project, it stalls at 0% and gives me an error... Source: almost 2 years ago
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