KopiKat
Label Studio
Gretel AI Beta²
Generated Photos Datasets
Computer Vision Annotation Tool (CVAT)
Universal Data Tool
This Person Does Not Exist
AWS SageMaker Ground Truth
Segments.ai
Labelbox
Universal Data Tool
Supervisely
CrowdFlower
Amazon Mechanical Turk
Labeling AI
Dataloop AI
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.
Segments.ai is a fast and accurate data labeling platform for multi-sensor data annotation. You can obtain segmentation labels, vector labels, and more via the intuitive labeling interfaces for images, videos, and 3D point clouds.
Build your clever annotation workflow exactly how you want, with the flexibility you need to get the job done quickly and efficiently. Segments.ai is a self-serve platform with dedicated support from our core team of engineers when you need it.
Onboard your workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together.
Get started with a free trial today at https://segments.ai/join
KopiKat Segments.aiKopiKat'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.
Label Studio - Open Source Data Labeling Platform for AI Model Tuning
Labelbox - Build computer vision products for the real world
Gretel AI Beta² - Generate unlimited synthetic data in minutes
Universal Data Tool - Machine learning, data labeling tool, computer vision, annotate-images, classification, dataset
Generated Photos Datasets - Reduce bias in AI systems with synthetic face datasets
Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...