Robovision.ai

Robovision.ai is a computer vision company that simplifies artificial intelligence development and makes it available to all stakeholders, regardless of their technical background. The collaborative AI platform provided by Robovision makes it easy for operators with basic expertise to build complex deep-learning models with minimal time and resources. The platform can be employed in various industries, including manufacturing, healthcare, and agriculture. Robovision focuses on collaborative intelligence between humans and machines, where the conventional analytics and decision-making of businesses could transform ideas into AI-based applications that can function without external oversight.

TLDR

Robovision.ai offers a collaborative AI platform that makes building deep learning models easy for operators with no technical background. The platform streamlines computer vision AI development for businesses in different fields, including manufacturing, healthcare, and agriculture. By offering an intuitive AI development platform, Robovision enables businesses to leverage the power of AI and achieve more efficient results. The Robovision platform offers integrated workflow management, effortless data upload and collection, flexible deployment solutions, and efficient model training, amongst other features. With Robovision, users can create sophisticated AI models with significantly diverse objectives.

Company Overview

Robovision.ai is a computer vision company that aims to make advanced AI accessible to everyone, regardless of their technical background. The company's mission is to help businesses accelerate their AI innovations by connecting different computer vision solutions to solve common issues. With a team of over 100 people, they strongly believe in collaborative intelligence between humans and machines.

The Robovision AI (RVAI) platform is an easy-to-use, collaborative AI platform that enables operators with no technical background to build their own deep learning-based solutions in limited time. Users can train their own AI models and easily retrain them if a new defect occurs, making it possible to automate quality control, object picking, and other automated actions.

The Robovision AI (RVAI) platform can be used across various industries, including agriculture, manufacturing, and healthcare. Robovision is the first AI company that has successfully used 3D deep learning to determine the growth of a tulip bulb, cut roses, or sort plants within agriculture. They have also partnered with ISO Group to develop the first fully automated machine that recognizes, picks, and potts plant cuttings.

In the manufacturing industry, Robovision helps companies improve their quality control and reduce waste and costs for their products. Their deep learning models can detect defects in real-time, from baking breads to inspection in the semiconductor industry. In healthcare, Robovision revolutionizes traditional analytics and clinical decision-making techniques by allowing medical experts and hospitals to transform their ideas into AI-based applications.

Robovision strives to eliminate the complexities that come with AI, making it accessible to all stakeholders. Their mission is to empower businesses to solve problems with the help of AI and make a positive impact on society.

Features

Full AI Life Cycle Coverage

Integrated Workflow Management

Robovision offers a Computer Vision AI platform that streamlines the entire process of developing, implementing, and adapting AI in today's ever-changing business environment. It covers the full AI life cycle with an integrated workflow management system that manages the creation and training of AI models. Companies can create projects of different types that are grouped by a computer vision problem they tackle with machine learning algorithms, ranging from standard to customized, depending on the aim of the model, or in other words, how companies want the machine to see.

Easy Data Upload and Collection

Once the project is set up, the next step is to collect and upload samples, which will form the input for training the model. The Robovision Platform enables data upload from local computer storage, cloud storage or Robovision Edge, allowing users to have flexibility and easy access to their data. Companies can easily manage their project pipelines which includes configuring custom user roles based on their access required, assigning members of a team to work together, access to different modules, and functionality of the platform.

Collaborative Annotation and Labeling

With the easy-to-use 2D and 3D labelling tools, domain experts can work with complex samples and apply their knowledge to complete labeling tasks. Moreover, for large and collaborative projects, Robovision enables assigning labellers to share the workload and let them work closely together with experts to deliver AI.

Efficient Model Training

Multiple Model Training Capabilities

Once the basics are set up and samples are collected and uploaded, the next step is to train the model with the dataset containing labelled samples. The Robovision Platform allows multiple models to be trained simultaneously by different members of the team. This makes it easy for companies to manage and scale AI operationally without any downtimes. With an advanced yet easy-to-use toolset, teams can take full control of their Vision AI initiatives in a collaborative working environment for both domain experts and data scientists.

Model Accuracy Check and Approvals

Robovision ensures that the models are accurate in their performance and functionalities. Once a trained model is developed, it is all left to check the accuracy of the model. Project leaders or team leads can approve or reject models before deployment. If there are any errors or inconsistencies, teams can use this feedback for further reiterations and training updates.

Real-Time Insights for On-Premises AI Solutions

Robovision Edge provides seamless and real-time connectivity for on-premises AI solutions. For latency-critical operations, the Robovision Edge platform add-on delivers real-time insights for on-premises AI solutions. The Edge device will communicate with the Robovision Platform when models need to be deployed and adapted. This feature provides a robust edge-to-cloud integration that enables businesses to work in dynamic environments where data is constantly changing.

Flexible Deployment Solutions

Cloud Deployment

Robovision offers cloud deployment through an API endpoint that makes the model available for use on your system without any complicated configurations. Companies can easily implement their AI models using Robovision's flexible deployment options.

Edge Deployment

Robovision Edge offers a quick and easy way to connect trained models with on-site data for real-time inferences. This feature is tailored for latency-critical operations, where companies need quick access to insights without any downtime or latency.

Robovision API Integration

Robovision API enables companies to create integration with other software and third-party APIs quickly and easily. The API can be used to import and export datasets, control machines connected with edge devices, display results on a dashboard, or connect with a robotic brain to execute tasks.

Ease of use and Extensibility

No Coding Required

Robovision is an intuitive AI platform that allows users without a background in engineering or Artificial Intelligence to develop and maintain AI-based vision applications autonomously. Domain experts and business users can contribute their product knowledge and help develop better AI, without having to understand the ins and outs of it.

Predictive Labelling

Robovision AI facilitates the annotation process and greatly speeds up the annotation of large datasets with the platform's unique predictive labelling feature: by training a model on some already labelled data, it can then be used to automatically predict the labels for the rest of the dataset. This feature allows users to create high-quality AI models with minimal iteration times, which leads to more efficiency in the development and implementation process of AI models. Companies can also easily adapt, improve performance, and maximize their AI potential, all with minimal engineering support.

Built-In and Specific Algorithm Support

Robovision supports a large number of built-in algorithms for a wide range of applications that provide the flexibility needed to support high-end industries. The platform is also easily extensible with specific algorithms through the Robovision Algorithms SDK, which allows users to create customized algorithms and integrate them into their model pipelines.

With the Robovision platform, users can create and deploy AI models with much more diverse goals than only solving simple problems. The platform enables businesses to build the world's first fully automated machine that recognizes, picks, and pots plant cuttings, streamline the visual inspection process with Robovision and vision AI to achieve a higher production output, and more. Regardless of whether a company is just starting its AI adoption journey or looking to improve existing AI applications, Robovision helps to speed up vision AI development and operationalization. The platform provides benefits for businesses that are continuously discovering how to leverage vision AI and the Robovision Platform for more cost savings and revenue growth.

FAQ

Does Robovision offer a free trial for their AI tool?

Yes, Robovision offers a free trial for eligible customers. The trial lasts for 10 business days and gives access to the full functionality of Robovision AI. Customers will not be charged during the trial period and will receive a reminder before the trial ends. It is important to note that once the trial period is over, the Robovision AI deployment will be shut down and all data will be lost.

How much data is needed for a usable model?

The amount of data needed for a usable model depends on various factors, including lighting conditions, subject matter, and whether the data is obtained in real-world conditions or a controlled environment. In a controlled environment with identical lighting and singular subject matter, a few dozen images per class may be sufficient. However, in real-world conditions, it is recommended to have at least a hundred or more annotated input samples for a usable model. The recommended dataset size also varies depending on the algorithm type being used. For anomaly detection, it is best to train a different model for different types of the same product (for example, different colors).

What image types are supported by Robovision AI?

Robovision AI supports several image types, including JPEG, JPG, PNG, TIFF, BMP, and GIF (including a 4th transparency channel, if any). However, it is important to note that the EXIF orientation metadata is not supported. Also, file names or S3 object keys must contain only Latin (ASCII) characters, as there are known packing, rendering, and backup/restore issues with file names/object keys that contain non-Latin characters. The recommended image size is 1280x720 pixels or higher, and images will be resized to max and min side parameters that are configurable in the training settings section.

What is the difference between the training, validation, and testing sets?

During the training setup process, data is separated into training, validation, and testing sets. The training set is the largest part of the dataset and is used to train the model. The validation set is a separate set of samples reserved for tuning the model and tracking its performance on unseen data during the training process. Based on the performance on the validation set, the training can be stopped if the model stops improving or starts performing worse on unseen data. The test set is not used during training and is reserved for evaluation metrics run at the end of the project to get a sense of how well the model will perform in production. By default, the dataset split is 70% for training, 15% for validation, and 15% for testing. It is important to ensure that dataset samples are unrelated to ensure the process is not naive random split.

What are some common training parameters for Robovision AI?

There are several parameters used in the training process for Robovision AI, including whether to use a GPU (which can drastically speed up training), the maximum number of epochs (times the training data will be processed), batch size (the number of data samples in a single neural network weights update), early stopping patience (number of epochs with no model improvement after which training will be stopped), freeze backbone (whether to freeze the weights of the backbone during training), early stopping (which stops training when the model is no longer improving), learning rate (the magnitude of changes to the neural network weights), learning rate decay (which reduces the learning rate during training), whether to save the best model or not, and whether to use transfer learning to reuse weights from another model. The image input dimension can also be set to a fixed input resolution for all inputs. It is recommended to use default settings unless you have expertise in deep learning.