Powering Computer Vision Models in the Autonomous Driving SpaceData Labeler offers a wide range of training datasets for training and validating autonomous vehicular models thus enabling them to develop a safe driving technique. Following are the uses cases of self-driving cars for which we offer Data Annotation services:
AddLane and Parking Area Detection
Data Labeler specializes in providing training datasets that can train your Computer Vision models to detect lanes and lane markings for identifying the drivable areas thereby enabling safe driving on roads and also helps to identify parking area.
Self-driving cars need to be trained to recognize various objects like pedestrians and traffic lights. They have to perform this object detection task in real-time so that they can quickly detect any approaching objects and avoid them to ensure smooth and safe driving. We build training datasets that help your autonomous vehicular models to recognize nearby objects accurately and help them understand their surroundings.
At Data Labeler, we have developed training datasets by annotating signboards and traffic lights using bounding boxes and polygons. These datasets help your autonomous vehicular models detect these semaphores accurately.
Agriculture Redefined with Data Annotation
Data Labeler provides best-in-class training datasets to enhance the capabilities of your AI and ML models in precision agriculture. Our team of expert annotators builds labeled and annotated images with bounding boxes and polygons that help train your Computer Vision models for precision agriculture at scale.
Our Data Annotation Services for Agriculture include the following
Pest & Disease diagnosis
Plant diseases are a major threat to food security directly affecting agricultural production. To tackle this threat, machine learning models have been developed that can detect diseases and monitor crop health. But, these models are most effective when they are trained using large volumes of high-quality training data.Data Labeler specializes in creating labeled datasets to train your smart detection systems to detect/identify pest attacks.
Machine Learning models have been developed for agriculture monitoring. These models are used for identifying suitable crops for harvesting which helps to reduce labor and improve production. Data Labeler provides quality training datasets that help you to train and validate your crop harvesting ML models.
Weeds are one of the major threats to crops as it is difficult to detect and differentiate them from crops. However, Computer Vision and ML models can be used to improve weed detection and discrimination. We help you to train your Computer Vision and ML algorithms to detect individual plants and differentiate between weeds and desired crops.
Machine learning can be used to optimize the efficiency of livestock monitoring.The ML models help farmers to monitor and trace their livestock automatically and individually. We provide training datasets to train your ML models to account for livestock individually and with accuracy thereby leading to effective livestock management.
Highly Accurate Training Datasets for Retail Space
Data Labeler creates world-class datasets to detect various types of objects in retail outlets. We help you understand how your products look, perform and compete on the physical shelf. We offer video annotations and image annotations using bounding boxes and polygonal annotations with semantic segmentation/instance segmentation.
Cashier-less/autonomous checkouts enable retailers to quickly implement frictionless shopping in retail outlets. AI models help to track products customers pick up and bill their accounts automatically thereby eliminating checkout lines.To train these AI/ML models, we provide high-quality labeled image datasets that helps them to detect objects and count products thereby enabling smooth and quicker cashier-less checkouts.
ML models are being developed that recognize shelf product conditions like space,availability, promotions, assortments, and others. This empowers retail companies by providing them with accurate insights on stock visibility and leads to better in-store product management. Our datasets can help train your ML models in analyzing shelf space usage, item location and pricing thereby allowing for optimal shelf space allocation.