Supervisely VS machine-learning in Python

Compare Supervisely VS machine-learning in Python 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...

machine-learning in Python

Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.

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2023-08-06

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2020-01-13

Supervisely

Website: supervisely.com
Pricing URL: Official Supervisely Pricing

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machine-learning in Python

Website: machinelearningmastery.com
Pricing URL: -

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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.

machine-learning in Python features and specs

Ease of Use
Python has a simple and clean syntax, which makes it accessible for beginners and efficient for experienced developers to implement fundamental concepts of machine learning quickly.
Rich Ecosystem
Python boasts a vast collection of libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch that provide extensive functionalities for machine learning tasks.
Community Support
Python has a large and active community that contributes to continuous improvement, support, and readily available resources like tutorials, forums, and documentation for troubleshooting.
Integration Capabilities
Python can easily integrate with other languages and technologies, enabling seamless deployment of machine learning models in diverse environments.
Visualization Tools
Python supports various visualization libraries like Matplotlib and Seaborn which are crucial for data analysis and understanding the performance of machine learning models.

Possible disadvantages of machine-learning in Python

Performance Limitations
Python is an interpreted language and can be slower compared to compiled languages like C++ or Java, which might be a consideration for performance-intensive tasks.
Global Interpreter Lock (GIL)
The GIL in Python can be a bottleneck for multi-threaded applications, limiting parallel execution and performance in CPU-bound machine learning tasks.
Dependency Management
Managing dependencies can be complex in Python projects, especially when handling different versions of libraries required for specific machine learning projects.
Memory Consumption
Python can require more memory for large datasets when compared with more memory-efficient languages, which might affect scalability and the ability to process very large datasets.

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.

Supervisely videos

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🛠️Basic annotation overview - Supervisely

machine-learning in Python videos

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Category Popularity

0-100% (relative to Supervisely and machine-learning in Python)

machine-learning in Python

Image Annotation

100 100%

Image Annotation

0% 0

Data Science And Machine Learning

71 71%

Data Science And Machine Learning

29% 29

Data Labeling

100 100%

Data Labeling

0% 0

Data Dashboard

0 0%

Data Dashboard

100% 100

User comments

Share your experience with using Supervisely and machine-learning in Python. For example, how are they different and which one is better?

Social recommendations and mentions

machine-learning in Python might be a bit more popular than Supervisely. We know about 7 links to it since March 2021 and only 6 links to Supervisely. 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

machine-learning in Python mentions (7)

Data science and cybersecurity with python project
After that you should probably look at some very basic ML tutorials. I just googled it, I have no idea if this is good https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 3 years ago
Ask HN: How can I learn ML in 6 months as a teenager?
Few different approaches based on search engine 'ml with python': Work though use cases / examples : https://www.databricks.com/resources/ebook/big-book-of-machine-learning-use-cases On-line class(es) / step by step projects: * https://bootcamp-sl.discover.online.purdue.edu/ai-machine-learning-certification-course * https://www.w3schools.com/python/python_ml_getting_started.asp *... - Source: Hacker News / over 3 years ago
Are these CS courses enough CS knowledge for ML engineer?
MLE: ALL OF THE ABOVE (this is important - pure machine learning skills generally won’t make you hireable unless you’re doing a PhD and/or are a genius) Plus: 1. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ 2. https://www.coursera.org/learn/machine-learning 3. https://www.3blue1brown.com/topics/neural-networks. Source: about 4 years ago
how to do i train an AI
Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 4 years ago
Python Data Science Project Ideas (+References)
Machine learning models Fine-tune existing machine learning models for improved accuracy, or create your own custom models. - Source: dev.to / over 4 years ago