Industry and Recycling
Today is already tomorrow
« Ingedata brought great project management skills to our project. I was a bit worried about our tight deadlines, but the quality of the project architecture secured the annotation workflows and ensured swift deployment and delivery. We could use Ingedata's annotations to retrain our AI models and hit our model accuracy targets. »
Marion Rosenstiehl Program & Product Manager @SuezIndustry 4.0, the fourth industrial phase, is changing the way manufacturing companies do business
This major shift is now in full swing: the manufacturing industry is powered by artificial intelligence tools and systems; smart machines are streamlining processes in smart factories.
From steam to electricity to computers, each industry era has led to a radical overhaul of the way companies work. Industry 4.0 is no exception: it is revolutionizing the traditional control system and enabling manufacturing companies to be more secure, productive, and profitable.
Our offer
BOUNDING BOXES
POLYGONS
SEMANTIC SEGMENTATION
Bounding Boxes
Bounding Boxes is one of the most common segmentation techniques, as it outlines the shape of an object by defining its X and Y coordinates. They are fast to draw but might include some background that will lower the accuracy with which your model is detected.
Polygons
Polygons are a very popular segmentation technique that accurately outlines a specific area of interest and exclude elements of background that would lower the performance of your Computer Vision model. While not as accurate as Semantic Segmentation, they are much faster to draw, making them a strong option to train your model with both accurate and high-volume annotations.
![pylon-annot[1]](https://www.ingedata.ai/wp-content/uploads/2022/01/pylon-annot1.jpg "pylon-annot[1]")
Semantic Segmentation
Semantic Segmentation is the perfect technique to annotate images used in training higher performance Computer Vision models. It will provide pixel-precise accuracy; provided that the segmentation rules to process shadows and/or blurry areas are precisely defined.
BOUNDING BOXES
Bounding Boxes
Bounding Boxes is one of the most common segmentation techniques, as it outlines the shape of an object by defining its X and Y coordinates. They are fast to draw but might include some background that will lower the accuracy with which your model is detected.
POLYGONS
Polygons
Polygons are a very popular segmentation technique that accurately outlines a specific area of interest and exclude elements of background that would lower the performance of your Computer Vision model. While not as accurate as Semantic Segmentation, they are much faster to draw, making them a strong option to train your model with both accurate and high-volume annotations.
SEMANTIC SEGMENTATION
Semantic Segmentation
Semantic Segmentation is the perfect technique to annotate images used in training higher performance Computer Vision models. It will provide pixel-precise accuracy; provided that the segmentation rules to process shadows and/or blurry areas are precisely defined.
They trust us
We offer high-end services with our unique data production methodology and, more importantly, our wonderful team of 500. As a people company, Ingedata thrives on bringing talents from developing countries on the international AI scene.
Our expertise
Recycling prevents waste of natural resources and energy and reduces environmental impact
Recycling is an innovative and environmentally friendly way to manage waste. Every year, hundreds of millions of tons of waste are generated by industries around the world.
Today, many recycling processes are controlled by computers and rely on computer vision. At Ingedata, we annotate huge amounts of specific product shapes or variations across brands and geographies, as packaging is typically different in different markets.
This new generation of intelligent sorting machines allows for maximum efficiency with stand-alone equipment and continuous functions. More and more of our waste can be sorted automatically, without the need for human intervention.
The applications are unlimited and cover all products rejected by our modern societies. All types of waste are concerned: refrigerators, automobile parts, textile (bags, flags, etc.), plastics (cans, bottles), metals (copper, cans) ... All can be sorted by AI to create more value from existing waste.
Client stories
Our Case Studies
Learn more about our approach and explore our case studies
The path to success
Why trust us?
With more than 100 projects in the field of ML model training, we are recognized for our remarkable know-how in production management and our guarantee of quality.
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Externalizing your data can represent a significant risk in terms of loss, deterioration, or theft. At Ingedata, your projects are designed and carried out in-house, from our secure production centers.
Ingedata's annotators have degrees ranging from bachelor's to engineer's or doctorate in your field. All our teams work from our production centers and are trained in the specific requirements of preparing data for machine learning.
Accelerate the optimization of your algorithm by using data prepared specifically for you. We collect, enrich, and categorize your data to build your own datasets.
We fit seamlessly into your current production mode, take over any coordination and adapt our team to your specific constraints in terms of data volume and quality.
