SuperAnnotate
Labelbox
V7
CloudFactory
Hive
Dataloop AI
Playment
Segments.ai
PyTorch
TensorFlow
Keras
Scikit-learn
NumPy
OpenCV
Pandas
MLKit
SuperAnnotate is the leading platform for building, fine-tuning, iterating, and managing your AI models faster with the highest-quality training data. With advanced annotation and QA tools, data curation, automation features, native integrations, and data governance, we enable enterprises to build datasets and successful ML pipelines. Partner with SuperAnnotate’s expert and professionally managed annotation workforce that can help you quickly deliver high-quality data for building top-performing models.
SuperAnnotate PyTorchNo SuperAnnotate videos yet. You could help us improve this page by suggesting one.
Based on our record, PyTorch seems to be a lot more popular than SuperAnnotate. While we know about 133 links to PyTorch, we've tracked only 1 mention of SuperAnnotate. 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.
Ok, so I tried comparing 4 of the better data annotation tools like dLabel.org, CVAT.com, SuperAnnotate.com and Labelbox.com . I tried them all as thoroughly as I could and I probably missed some things so apologies in advance for that! Let me know what I missed in the comment. Btw, I'm Amir and I've worked most of my data-labeling career at dLabel.org. Source: almost 4 years ago
To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / 1 day ago
With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / 15 days ago
Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / about 1 month ago
8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 3 months ago
Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 3 months ago
Labelbox - Build computer vision products for the real world
TensorFlow - 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.
V7 - Pixel perfect image labeling for industrial, medical, and large scale dataset creation. Create ground truth 10 times faster.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
CloudFactory - Human-powered Data Processing for AI and Automation
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
