Supervisely VS Activeloop

Compare Supervisely VS Activeloop and see what are their differences

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Supervisely

Supervisely helps people with and without machine learning expertise to create state-of-the-art...

Activeloop

Data lake for machine and deep learning. The fastest dataset management tool for computer vision.

Landing page //
2023-08-06

Landing page //
2021-09-20

About

Activeloop provides an optimized format for unstructured data, so users can stream their machine learning datasets while training ML models in PyTorch and TensorFlow. Activeloop acts as a data lake for deep learning on unstructured data and offers in-browser dataset visualization, querying, and version control. On top of those features, Activeloop integrates with experimentation and labeling tools to allow rapid iteration on computer vision datasets.

Activeloop supports the following use cases:

Machine Learning teams can apply Activeloop's data infrastructure to ship their models fast in the following use cases:

Supervisely

Website: supervisely.com
Pricing URL: Official Supervisely Pricing
$ Details: -
Platforms: -
Release Date: -

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Activeloop

Website: activeloop.ai
Pricing URL: Official Activeloop Pricing
$ Details: $450.0 / Monthly (Growth Plan for up to 10 users)
Platforms: AWS GCP Python
Release Date: 2019 July

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Supervisely features and specs

Comprehensive Toolset
Supervisely offers a wide range of tools for image annotation, data management, and deep learning model training, providing a one-stop solution for computer vision projects.
Collaborative Platform
It supports team collaboration with features for sharing projects, annotating data, and reviewing work, making it easier for teams to work together.
High Customizability
Supervisely allows users to create custom plugins and automation scripts, offering flexibility to tailor the platform according to specific project needs.
Extensive Dataset Support
The platform supports a wide variety of data formats and types, including images, videos, and 3D data, making it versatile for different applications.
Integrated Machine Learning
Supervisely integrates machine learning capabilities, enabling users to train models directly on the platform and test them using their own annotated data.

Possible disadvantages of Supervisely

Cost
Supervisely can be expensive, particularly for small teams or individual users, as it primarily targets enterprise customers.
Complexity
Due to the breadth of features and tools, there may be a steep learning curve for new users, making it more challenging to get started quickly without adequate training.
Performance Issues
Some users may experience performance issues, particularly when handling very large datasets or running multiple simultaneous tasks.
Cloud Dependency
While a cloud-based platform offers accessibility advantages, it also means that users are dependent on internet connectivity and may face latency or downtime problems.
Limited Offline Features
Supervisely's offline functionality is limited, which can be a drawback for users who need to work in environments with restricted or unreliable internet access.

Activeloop features and specs

No features have been listed yet.

Analysis of Supervisely

Overall verdict

Overall, Supervisely is a good platform for computer vision projects due to its versatility and ease of use. It offers a complete ecosystem that caters to various stages of the machine learning pipeline, making it an efficient choice for both beginners and experienced practitioners.

Why this product is good

Supervisely is considered a robust platform for its comprehensive suite of tools designed for computer vision tasks. It provides capabilities for data labeling, neural network training, and deployment. Its user-friendly interface, collaborative features, and support for a wide range of formats and integrations make it appealing to both individual developers and enterprise teams.

Recommended for

Data scientists looking for a comprehensive tool for computer vision.
Companies needing a collaborative environment for AI projects.
Researchers who require a platform with extensive format support and integrations.
Developers wanting an easy-to-use interface for data annotation and model training.

Analysis of Activeloop

Overall verdict

Activeloop is a solid choice for teams working with large-scale AI/ML datasets, particularly those involving unstructured data like images, video, and audio, offering a specialized data infrastructure (Deep Lake) that streamlines dataset versioning, storage, and streaming for machine learning workflows.

Why this product is good

Deep Lake format enables efficient storage and streaming of large unstructured datasets directly to ML training pipelines without full downloads
Built-in version control for datasets, similar to Git, making it easier to track changes and collaborate on data
Native integrations with popular ML frameworks like PyTorch and TensorFlow, plus support for vector search and LLM-based applications
Cloud-agnostic storage options allowing flexibility across AWS, GCP, and other providers
Strong focus on performance optimization for data loading, reducing bottlenecks in training large models
Growing ecosystem with support for multimodal data types, useful for computer vision and generative AI projects

Recommended for

ML engineers and data scientists working with large-scale image, video, or audio datasets
Teams building computer vision or multimodal AI applications
Organizations needing dataset version control integrated into their ML pipeline
Developers building retrieval-augmented generation (RAG) or LLM applications requiring vector storage
Startups and enterprises looking to optimize data loading performance for deep learning training
Teams seeking an alternative to traditional data lakes for AI-specific workloads

Supervisely videos

+ Add

🛠️Basic annotation overview - Supervisely

Activeloop videos

+ Add

Activeloop Product Demo Video

Category Popularity

0-100% (relative to Supervisely and Activeloop)

Supervisely

Activeloop

Image Annotation

100 100%

Image Annotation

0% 0

Machine Learning

0 0%

Machine Learning

100% 100

Data Labeling

100 100%

Data Labeling

0% 0

Data Science Tools

0 0%

Data Science Tools

100% 100

User comments

Share your experience with using Supervisely and Activeloop. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Supervisely should be more popular than Activeloop. 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.

Supervisely mentions (6)

Way to label yolov7 images fast
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 3 years ago
65 Blog Posts to Learn Data Science
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 3 years ago
Bounding Box for Text Annotation
If they were videos, I would have suggested trying supervise.ly as it has a very good tracking functionality. Source: almost 4 years ago
CVAT alternatives for video frame annotation
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 4 years ago
Accessing 2022 Machine Learning Imagery from WPI's Photo Album
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: about 4 years ago

Activeloop mentions (4)

[P] I built a Chatbot to talk with any Github Repo. 🪄
This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input. Source: about 3 years ago
[D] NLP has HuggingFace, what does Computer Vision have?
u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :). Source: about 4 years ago
[P] Database for AI: Visualize, version-control & explore image, video and audio datasets
Hub, our open-source package, lets you stream datasets while training to PyTorch/TensorFlow. Check out how we achieved 95% GPU utilization while training on ImageNet at 50% less cost. We're building the Database for AI, with everything it should contain. If there's an adjacent feature that would make it more useful for your workflow, do let us know! Source: over 4 years ago
[P] Database for AI: Visualize, version-control & explore image, video and audio datasets
I'm Davit from Activeloop (activeloop.ai). Source: over 4 years ago