Integrate AI into your service with our computer vision models.
Running on cloud and on edge devices
plug & play to prod.
Train just dragging few images.
Nothing to know about AI
Our UI is designed to easily train the model for no-coders.
Versatile, industry-ready models
Using our ready-models your industry is well served.
An all-in-one solution
Our platform assists you from training to deployment.
By developers, for developers.
Our platform exposes three lines of code to seamlessly integrate our models into your stack, enabling rapid deployment to production.
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model = client.get_model(“your-model-name”)
predictions = model.infer(“your-image-path”)
Ready for Cloud & Local Use
embrace frugality
10x
Faster
Our models are able to process images ten times faster than mainstream solutions.
960kg
CO2 saved yearly
The data reflects the measurable energy savings obtained by switching to low-power hardware infrastructure.
How we derive this data
The data is based on a semantic segmentation task (pixel-level classification of an image). With a state-of-the-art open-source model (SegFormerB5), it is necessary to use a server with at least one V100/A100/H100 GPU on AWS, with average power consumption of 250W per GPU. With our technology, we create a more efficient model, which allows us to use less powerful hardware resources (NVIDIA T4 GPUs) with lower power consumption (70W).
Assuming you deploy a state-of-the-art model on a server with a V100 GPU running year-round with continuous requests (think of something like ChatGPT for visual processing), it would consume approximately 2200 kWh annually, emitting 1330 kg of CO₂. By switching to our optimized models, you can transition to a low-power T4 GPU, reducing energy consumption to just 600 kWh and CO₂ emissions to 370 kg. This shift saves 960 kg of CO₂ annually while maintaining top-tier performance.
Our models
outperform on every hardware, every task.
Instead of sticking an AI model on your tech infrastructure, we test scrupulously your edge devices or cloud performance requirements to compose the model you deserve.
Breaking vision boundaries
Segmentation and detection, what are the differences?
At FocoosAI, our research team is dedicated to keeping Anyma at the cutting edge of accuracy and efficiency. By continuously advancing neural network architecture, we ensure your models are not only powerful but also optimized for real-world applications. Our commitment to pioneering research is demonstrated in our published work and deep technical expertise, allowing Anyma to stay ahead with the most precise solutions across a growing range of computer vision tasks tailored to your unique needs.
Benchmark performances
Task
Deploy on
Task
Deploy on
Tasks
Segmentation and detection, what are the differences?
Task
Output
Goal
Object Detection
Output
Bounding boxes around objects with class labels.
Goal
Localize and classify objects.
Semantic Segmentation
Output
Pixel-wise classification, but all objects of the same class are merged.
Goal
Classify each pixel into a category.
Instance Segmentation
Output
Pixel-wise classification, with differentiation between instances.
Goal
Classify pixels and distinguish objects.
Deploy on
Segmentation and detection, what are the differences?
Our technology is designed to generate the best neural network architecture for any device, ensuring optimal performance and efficiency. We’ve already optimized models for NVIDIA GPUs and Intel CPUs, but if you’re using different hardware, reach out to us to get a model perfectly tailored to your setup.
Our secret? Anyma search engine
Our Neural Architecture Search Engine, it is able to find, among over 50 thousand possible alternatives, the most efficient and accurate one, for any hardware device and computer vision task.
Anyma
It’s all completely automated
Anyma collects hardware statistics from the target device and then evaluates application-oriented metrics such as accuracy, latency, and energy consumption to automatically identify the optimal solution.
Ready to use
Thanks to Anyma’s hardware-aware NAS, the best NN architecture identified, is deployable on the target device, without requiring complex optimization and compilation steps.
Ready for
Manufacturing
Autonomous inspection
Manufacturing
Automated quality control
Aerospace
Aerial images analysis
Smart city
Security
Manufacturing
Autonomous driving
Manufacturing
Traffic monitoring
Manufacturing
Urban monitoring
Challenge us!
Let’s prototype an AI feature faster than ever before.
FAQ
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Is Focoos’s solution compatible with and scalable in my current ecosystem?
Yes, Focoos AI’s platform integrates seamlessly with your existing tools and infrastructure. It supports a variety of data formats and works effortlessly with popular tools like Roboflow and Dataset Ninja. You can deploy Focoos AI’s models across public clouds, on-premises systems, or embedded devices without the hassle of switching platforms or disrupting your setup. Imagine the convenience of integrating it into your workflow—no extra steps, just plug and play!
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How does Focoos’s computer vision solution help reduce costs?
Focoos AI’s efficient computer vision models deliver high-quality results without requiring expensive, high-performance GPUs. Businesses can achieve great performance using affordable hardware, significantly lowering initial and ongoing costs. Additionally, our pre-optimized models are deployment-ready, saving you the time and effort of lengthy development and testing cycles.
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What steps does Focoos take in the event of a service outage?
Focoos AI’s platform is built on AWS cloud infrastructure, featuring robust load handling, redundancy, and auto-recovery mechanisms. These ensure that operations resume quickly in case of interruptions. Whether you’re training models or using the platform for real-time inference, our dedicated support team is always on hand to minimize downtime and maintain service continuity.
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How reliant will my business be on Focoos’s service?
Focoos AI provides the flexibility to suit your needs. You can use our scalable infrastructure to maximize AI potential without requiring significant internal resources. Alternatively, deploy our models on your own hardware, giving you greater control and autonomy. This approach minimizes dependence on our platform while still enabling access to cutting-edge AI technology.
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How does Focoos support sustainability?
Focoos AI’s computer vision models are optimized to use minimal computational resources, lowering energy consumption and extending the life of existing hardware. This efficiency reduce energy consumption significantly. By continuously improving our technology, we help businesses reduce their carbon footprint and achieve sustainability goals.
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What advantages does Focoos offer in terms of processing speed and application velocity?
Focoos AI’s Anima-powered models are designed for real-time data processing, enabling lightning-fast decisions in demanding environments. This speed helps businesses improve operational efficiency and stay competitive in fast-paced markets.
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Is Focoos’s solution compatible with existing hardware and infrastructure?
Absolutely. Focoos AI is designed to work with a wide range of hardware, from high-performance servers to edge devices. This means you can deploy our models without the need for costly hardware upgrades or replacements.
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Can Focoos’s solution perform efficiently on low-power or resource-limited devices?
Yes, Focoos AI’s solutions are optimized for low-power and resource-constrained environments. They deliver real-time performance, ensuring reliability even in scenarios with limited hardware capabilities.
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How can Focoos’s solution optimize long-term resource allocation?
By using Focoos AI’s efficient models, businesses can invest in less expensive hardware while still meeting performance requirements. This efficiency supports cost-effective long-term planning, making it easier to allocate resources strategically over time.