Software Alternatives, Accelerators & Startups

Segments.ai VS KopiKat

Compare Segments.ai VS KopiKat and see what are their differences

Segments.ai logo Segments.ai

Multi-sensor labeling platform for robotics and autonomous driving

KopiKat logo KopiKat

Generative image data augmentation tool preserving annotations. Enhance the precision of AI models without modifying the network structure.
  • Segments.ai Homepage
    Homepage //
    2024-04-12

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 Landing page
    Landing page //
    2023-07-18

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

$ Details
freemium €800.0 / Monthly (Includes 3,600 hours/yr of labeling usage)
Platforms
AWS Azure Python TensorFlow Hugging Face 🤗
Release Date
2020 January

KopiKat

Website
kopikat.co
Pricing URL
-
$ Details
freemium
Platforms
Web
Release Date
2023 April

Segments.ai features and specs

  • Image Segmentation: Semantic Segmentation / Instance Segmentation / Panoptic Segmentation
  • Image Vector Labeling: Bounding Boxes / Polygons / Polylines / Keypoints
  • Point Cloud Segmentation: Semantic Segmentation / Instance Segmentation / Panoptic Segmentation
  • Point Cloud Vector Labeling: Cuboids / Polygons / Polylines / Keypoints
  • ML-powered labeling tools: SuperPixel 2.0 / Autosegment
  • Multi-sensor fusion: 2D and 3D overlay / 3D to 2D projections
  • Powerful Python SDK: Yes
  • Unlimited sized Point Clouds: Unlimited

KopiKat features and specs

  • API: Yes
  • Free Trial: Yes

Segments.ai videos

3D point cloud labeling platform for autonomous vehicles and robotics | Segments ai

KopiKat videos

Enhance Your Images with Kopikat | SaaS Reviews

More videos:

  • Review - KopiKat.co: 10x Your Machine Learning Data in Minutes - #OpenCV Weekly Episode 101
  • Review - Summer Wine Brewery - Kopikat : Imperial Mocha Stout - HopZine Beer Review

Category Popularity

0-100% (relative to Segments.ai and KopiKat)
Image Annotation
100 100%
0% 0
AI
75 75%
25% 25
Data Labeling
100 100%
0% 0
Developer Tools
0 0%
100% 100

Questions and Answers

As answered by people managing Segments.ai and KopiKat.

What's the story behind your product?

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.

What makes your product unique?

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.

User comments

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