Best Computer Vision Software for PyTorch

Compare the Top Computer Vision Software that integrates with PyTorch as of June 2025

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This a list of Computer Vision software that integrates with PyTorch. Use the filters on the left to add additional filters for products that have integrations with PyTorch. View the products that work with PyTorch in the table below.

What is Computer Vision Software for PyTorch?

Computer vision software allows machines to interpret and analyze visual data from images or videos, enabling applications like object detection, image recognition, and video analysis. It utilizes advanced algorithms and deep learning techniques to understand and classify visual information, often mimicking human vision processes. These tools are essential in fields like autonomous vehicles, facial recognition, medical imaging, and augmented reality, where accurate interpretation of visual input is crucial. Computer vision software often includes features for image preprocessing, feature extraction, and model training to improve the accuracy of visual analysis. Overall, it enables machines to "see" and make informed decisions based on visual data, revolutionizing industries with automation and intelligence. Compare and read user reviews of the best Computer Vision software for PyTorch currently available using the table below. This list is updated regularly.

1

Lightly

Lightly

Lightly selects the subset of your data with the biggest impact on model accuracy, allowing you to improve your model iteratively by using the best data for retraining. Get the most out of your data by reducing data redundancy, and bias, and focusing on edge cases. Lightly's algorithms can process lots of data within less than 24 hours. Connect Lightly to your existing cloud buckets and process new data automatically. Use our API to automate the whole data selection process. Use state-of-the-art active learning algorithms. Lightly combines active- and self-supervised learning algorithms for data selection. Use a combination of model predictions, embeddings, and metadata to reach your desired data distribution. Improve your model by better understanding your data distribution, bias, and edge cases. Manage data curation runs and keep track of new data for labeling and model training. Easy installation via a Docker image and cloud storage integration, no data leaves your infrastructure.

1 Rating

Starting Price: $280 per month

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2

Voxel51

Voxel51

Voxel51 is the company behind FiftyOne, the open-source toolkit that enables you to build better computer vision workflows by improving the quality of your datasets and delivering insights about your models. Explore, search, and slice your datasets. Quickly find the samples and labels that match your criteria. Use FiftyOne’s tight integrations with public datasets like COCO, Open Images, and ActivityNet, or create your own datasets from scratch. Data quality is a key limiting factor in model performance. Use FiftyOne to identify, visualize, and correct your model’s failure modes. Annotation mistakes lead to bad models, but finding mistakes by hand isn’t scalable. FiftyOne helps automatically find and correct label mistakes so you can curate higher-quality datasets. Aggregate performance metrics and manual debugging don’t scale. Use the FiftyOne Brain to identify edge cases, mine new samples for training, and much more.

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3

Segments.ai

Segments.ai

Segments.ai is an advanced data labeling platform that allows users to label data from multiple sensors simultaneously, improving the speed and accuracy of labeling for robotics and autonomous vehicle (AV) applications. It supports 2D and 3D labeling, including point cloud annotation, and enables users to label moving and stationary objects with ease. The platform leverages smart automation tools like batch mode and ML-powered object tracking, streamlining workflows and reducing manual labor. By fusing 2D image data with 3D point cloud data, Segments.ai offers a more efficient and consistent labeling process, ideal for high-volume, multi-sensor projects.

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4

PaliGemma 2

Google

PaliGemma 2, the next evolution in tunable vision-language models, builds upon the performant Gemma 2 models, adding the power of vision and making it easier than ever to fine-tune for exceptional performance. With PaliGemma 2, these models can see, understand, and interact with visual input, opening up a world of new possibilities. It offers scalable performance with multiple model sizes (3B, 10B, 28B parameters) and resolutions (224px, 448px, 896px). PaliGemma 2 generates detailed, contextually relevant captions for images, going beyond simple object identification to describe actions, emotions, and the overall narrative of the scene. Our research demonstrates leading performance in chemical formula recognition, music score recognition, spatial reasoning, and chest X-ray report generation, as detailed in the technical report. Upgrading to PaliGemma 2 is a breeze for existing PaliGemma users.

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5

Voyager SDK

Axelera AI

The Voyager SDK is purpose‑built for Computer Vision at the Edge and enables customers to solve their AI business requirements by effortlessly deploying AI on edge devices. Customers use the SDK to bring their applications into the Metis AI platform and run them on Axelera’s powerful Metis AI Processing Unit (AIPU), whether the application is developed using proprietary or standard industry models. The Voyager SDK offers end‑to‑end integration and is API‑compatible with de facto industry standards, unleashing the potential of the Metis AIPU, delivering high‑performance AI that can be deployed quickly and easily. Developers describe their end‑to‑end application pipelines in a simple, human‑readable, high‑level declarative language, YAML, with one or more neural networks and corresponding pre‑ & post‑processing tasks, including sophisticated image processing operations.

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