Supervisely VS TensorFlow

Compare Supervisely VS TensorFlow 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...

TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

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2023-08-06

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2023-06-19

Supervisely

Website: supervisely.com
Pricing URL: Official Supervisely Pricing
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TensorFlow

Website: tensorflow.org
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.

TensorFlow features and specs

Comprehensive Ecosystem
TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
Community and Support
TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
Flexibility
TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
Integrations
TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
Versatility
TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

Complexity
TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
Performance Overhead
Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
Verbose Syntax
The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
Compatibility Issues
Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
Mobile Deployment
While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

Supervisely videos

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🛠️Basic annotation overview - Supervisely

TensorFlow videos

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What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

Category Popularity

0-100% (relative to Supervisely and TensorFlow)

Supervisely

TensorFlow

Image Annotation

100 100%

Image Annotation

0% 0

Data Science And Machine Learning

21 21%

Data Science And Machine Learning

79% 79

Data Labeling

100 100%

Data Labeling

0% 0

21 21%

79% 79

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Supervisely and TensorFlow

Supervisely Reviews

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TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024

From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.

Source: www.labellerr.com

10 Python Libraries for Computer Vision

TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.

Source: clouddevs.com

25 Python Frameworks to Master

Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.

Source: kinsta.com

Top 8 Alternatives to OpenCV for Computer Vision and Image Processing

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...

Source: www.uubyte.com

PyTorch vs TensorFlow in 2022

There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...

Source: www.assemblyai.com

Social recommendations and mentions

TensorFlow 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: almost 2 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 2 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: over 2 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 3 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: almost 3 years ago

TensorFlow mentions (7)

Creating Image Frames from Videos for Deep Learning Models
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / about 2 years ago
Need help with a Tensorflow function
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 3 years ago
Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: almost 3 years ago
[Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 years ago
[Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago