Data Annotation by EPIC Translations: Image Annotation Data for AI & ML

EPIC Translations provides image annotation services for deep learning and computer vision. All in one place! Our skillful team of image annotators makes sure your machine learning projects run quickly and efficiently following all your annotation requirements. Image annotation is a key technique used to train Machine Learning algorithms to learn to see the world as we do and this is heavily reliant on the accuracy of its training data. Invest in talented and experienced annotators to get precise and cost-efficiency annotations. We support a wide variety of image annotation services that will match your project’s needs, including 2D bounding boxes, 3D cuboids, lines and splines, polygons, semantic segmentation, image classification, pixel-precise/pixel-wise segmentation, and more. Image Annotation: Annotation with a bounding box is the most commonly used and simplest type of image annotation. It refers to a rectangle that has to be drawn around the edges of each object in an image. The goal is to help us detect and recognize the different classes of objects, it’s found in object classification, localization and detection. 3D bounding boxes show approximate depth of the target objects being annotated making sure to place anchor points at the object’s edges. Our annotators are ready to precisely draw a bounding box 2D or 3D around the object you want to annotate within your images. Polygons: Polygon annotation is important because not every object may fit precisely in a bounding box. They are a much more precise way to annotate objects by only including pixels that belong to them. Polygon annotation provides the flexibility to plot points on each vertex of the target object. This annotation method allows all of the object’s exact edges to be annotated, regardless of its shape. Perfect for objects like fruits, trees, landmarks, houses and much more. Our annotators have the high level of precision required for this kind of project. Image segmentation: Image segmentation takes image annotation to a new level by finding out accurately the exact boundary of the objects in the image. In image segmentation, each pixel is classified into a certain class. There are two types of segmentation techniques. Semantic segmentation: It assigns a general label to all instances of an object. For example, if the color blue is assigned to cars on an image, all cars will be blue within the image. Instance segmentation: It gives a unique label to every instance of a particular object in the image. For example, every car on an image will have different colors. Our annotators will meticulously annotate the specified objects in your images with pixel-wise accuracy. Lines and splines: Lines and splines annotation is the labeling of straight or curved lines on images. They are mainly used for lane and boundary recognition. As well, they are also often used for trajectory planning. From autonomous vehicles and drones to robotics in warehouses and more, lines and splines annotations are useful in a wide variety of use cases. WHAT DOES EPIC TRANSLATIONS BRING TO THE TABLE? Machine Learning Pipeline – That is, from Data Collection, Data Preprocessing, selection of algorithm that is well suited to deliver the best results based on the challenge, tuning and tweaking of hyperparameters, creation of a multialgorithm pipeline for comparison of different algorithms and how they perform, and finally the delivery of the algorithm to a real-world scenario by creating APIs that can manipulate and translate data to a given output. The creation of machine learning packed Microservices that can be used in real-world deployment of the already trained algorithms. Machine Learning Consultation- Based on our knowledge of the machine learning infrastructure we can offer a great deal of expertise on the subject matter. Sentiment Analysis – Natural Language Processing (NLP) being one of the disciplines of machine learning gives us a great opportunity to apply sentiment analysis especially in the understanding of text and its context. WHAT TYPES OF PROJECTS HAVE WE WORKED ON? Using Sentiment Analysis in the categorization of twitter data that we collected from tweepy (Twitter API) into either hate speech that could lead to political instability in a developing country or a neutral sentiment. The collection of data, labelling of data, development of machine learning algorithm that was used as a recommender system to advise potential tenants on places that they could rent out depending on a few preferences that they gave and its deployment using an API. The development of Reinforcement Learning algorithm that could predict the oil pressure rates based on various factors for use in oil production purposes. The collection of audio data, its synthesis, training and deployment into a personal “SIRI” or “CORTANA”. A machine learning project to demonstrate and predict the COVID-19 outbreak. A machine Learning project based on various crop diseases that was used to categorize crop diseases based on images and their cross relationship with the training data. WHAT TYPES OF SOFTWARE AND TOOLS DO WE USE FOR IMAGE ANNOTATION? Some of the software and tools we use are: 1. LabelImg 2. Labelme 3. Labelbox 4. CVAT 5. Dataloop 6. VGG 7. And more…