If you are developing a computer vision system, you will need thousands, millions of images, videos, and sensor data to train machine learning models for computer vision.
– NLPC can provide both the Data Sets for Computer Vision and the annotation services to make your project a success.
The types of Data Sets for Computer Vision can include:
- 2D images and videos: These datasets can be sourced from scanners, cameras, or other imaging technologies.
- 3D images and videos: They’re also sourced from scanners, cameras, or other imaging technologies.
- Sensor data: It’s captured using remote technology such as satellites.
NLPC adaptable, agile teams do the work just like you would – if you had the time
The dream of Artificial General Intelligence is all around us. We want computers to understand what we write, the intent of our spoken word. As per 2023, we are beginning to have short but meaningful conversations with Large Language Models such as ChatGPT. When we dream big, when we imagine things, there is no limit to what we can turn real.
Some companies could well manage the acquisition of Data Sets for Computer Vision and they have probably squeezed all open sources available. But so have the competition and you need to specialize your own system and increase accuracy. There are only 24 hours in a day, and your ML engineering team is already stretched. Our professionally managed workforce, engineers many of them, understand your needs and will set up the correct workflow to help you accurately and quickly process the high-volume, routine tasks and time-sensitive data that powers your business.
Obtaining Data Sets for Computer Vision may not be considered glamorous work, but we make it fun through our distributed professionals all over the world – making 24/7 data deliveries a reality from our centers in Europe, Japan and Latin America.
We know what it needs to get done, and we think the details matter.
NLPC staff has accumulated years of experience in ML and data projects. We will support you and provide the confidence so you know that the collection of operational Data Sets for Computer Vision is done right, on time, and on budget.
We work so you can
- Focus on your business in creating the technology of the future while our team works on your important but time-consuming tasks with the same attention-to-detail that you would to it.
- We collect the Data Sets for Computer Vision you require and work on labeling quickly, providing the highest levels of predictability and control, with online reporting and monitoring tools so you know when your data is available.
- Smooth communication with an ML engineer at our end and a designated Project Manager ensures that new needs, requirements or use cases and guidelines are added quickly and efficiently to support the achievement of your goals.
How do machines understand the visual world?
We humans understand what things are from a very early age and by association. Before we begin to reason, we have seen enough samples of objects so we can recognize similar ones. A machine requires examples (thousands, millions) of images, texts, sounds.
Let’s say you’ve collected tons of training data in the form of images, videos, and sensor data for your computer vision model. For the data to be useful, it must be labeled or annotated so the machine knows how to differentiate a vehicle from a pedestrian, an apple from an orange or a curry dish from a Spanish paella. Machines understand visual data by learning from labeled or tagged examples.
Labeled data refers to a dataset that has been annotated or labeled with tags, categories, or other relevant information that indicates the meaning or significance of the data. For example, in a dataset of images, each image might be labeled with a description of its contents, such as “person,” “vehicle,” or “truck.”
“Labeled data is used in supervised machine learning algorithms, where the algorithm is trained on a dataset with known labels, in order to learn how to classify new, unseen data. This is in contrast to unsupervised machine learning algorithms, which do not use labeled data and instead rely on patterns in the data itself to identify similarities or clusters.”
Labeled data needs careful crafting and understanding of the process. It is more time-consuming to create than unlabeled data. However, it is an essential resource for most machine learning tasks, as it allows algorithms to learn directly from the railguards and wisdom of human experts.
- For algorithms, an image is just a series of pixels with no shape. Pixels do contain values that represent the colors, but they lack the values that correspond to the object. By marking and annotating the images, machines are trained to understand that specific sets of pixels are specific target objects.
- Labeled data is best performed by trained workforces, the so-called human-in-the-loop (HITL). HITL can make use of both existing machine workflows and human intelligence to build models for computer vision. Human judgment is applied to teach, fine-tune, and assess a certain machine learning model.
- A computer vision example can be the case of autonomous cars. The training data sets are labeled using data labeling tools and techniques to identify what is the road, what is another vehicle, what is a registration plate, a road sign, etc.