- January 7, 2020
- Artificial Intelligence, NLP
Today, there are many different techniques for object detection, each with its own advantages and disadvantages. Some techniques are faster but less accurate, while others are more accurate but slower. In this article, we will discuss about “DAT” – Datamoo’s new annotation tool and its features.
The current state-of-the-art object detection technique is Mask R-CNN, which was proposed in 2016 by Kaiming He et al. Mask R-CNN is an extension of the well-known Faster R-CNN object detection model. It not only predicts the class of an object but also its precise boundaries. There are many other deep learning-based object detection algorithms, such as R-FCN, SSD, and YOLO.
How Does Object Detection Work?
Most object detection models are based on a technique called region proposal. In a region proposal algorithm, a given image is divided into several regions, and each region is classified as either containing an object or not. The process of classifying the regions can be done in several ways, but the most popular method is using a deep convolutional neural network (CNN).
What is the problem with the existing tools
There are several different tools available for either text or image annotation, but most tools require some technical expertise to handle the application and are not open-source. Offline tools tend to be more specialized, while online tools are often more general-purpose. Some tools provide only the annotation functionality, limiting what you can do with your data.
To build an ML model, we need a better data management system that can handle the annotations and provide the necessary data in the right format. In the absence of such a system, it is difficult to achieve the best performance from the models.
How it affects the existing market scenario
The demand for ML models is growing rapidly, due to the introduction of data management systems. According to a study, the global machine learning (ML) market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8% in the forecast period. The data management systems provide the infrastructure needed to develop and deploy ML models, which will lead to increased competition and lower prices for ML models.
Our Solution
The use of ML models is growing in popularity since they offer several advantages over traditional methods. They are more accurate, faster, and easier to use. They can be deployed on a variety of platforms, including mobile devices, the web, and in the cloud.
If you’re working with data, you know that it can be tough to get everything organized and in one place. Our “five in one” tool can help you with
1) Seamless data upload/download
2) Data annotation (including labeling and auto-annotation)
3) Model selection (performed based on the business needs)
4) Model training, building, and testing
5) ML model evaluation (to find which models are performing the best)
Our tool offers the workflow configuration option, which allows admins/super admins to assign tasks to team members. This is a great way to keep track of who is responsible for what and make sure that everyone is on the same page optimizing efficiency and productivity. This all-in-one tool will save you time and hassle allowing you to get back on what’s more important – your business goals.
How does it solve the problem
Our simple and powerful approach to training custom object detection models is by using the Pytorch and YOLO architecture. YOLO is a powerful tool that allows you to quickly train custom models without having to write any complicated code. It is so unique that it doesn’t require a pool of object examples to train upon. Instead, it uses a single neural network to directly predict the bounding box coordinates of objects in an image. This makes YOLO both fast and accurate.
Our tool provides an easy-to-use interface to annotate different types of data, integrate with machine learning models to visualize and compare predictions, perform data annotation, model building, model testing, and receive analytics based on model performance, as a one-stop solution.
It further helps you build more robust ML models with auto-annotation (pre-labeling), which is the best solution to save time. The one-click endpoint makes it easy to use different approaches for image data (e.g., object detection, image classification) or text data (e.g., keyword extraction, sentence detection, sequence-to-sequence data extraction).
Technology used
Python,
ML algorithms such as Yolo, Spacy, etc
AWS,
Docker,
PostgreSQL.
Real time applications
In the automotive industry, our tool can be used to predict maintenance needs and improve safety. In healthcare, they are being used to develop personalized medicine and improve patient care. In the financial sector, they are used to detect fraud and improve risk management. In manufacturing industries, our tool can be used for quality control and product inspection. It is designed to detect anomalies and improve the quality of the products.
Lina
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Jayz Marcop
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