OCLAVI - The real solution for object classification, object tracking, and generating training data for machine learning models
Our Image video annotation solution enables you to annotate bound boxes with precision and highest-quality which helps in building state-of-the-art computer vision models.
Allows for more flexibility in the shape of the annotated object compared to other techniques. This makes it a useful technique for annotating irregularly shaped objects or regions of interest.
The boundaries of the object can be defined more accurately, leading to better quality annotations. In addition, the flexibility of polygonal annotation enables the annotation of complex objects, such as objects with holes or concave shapes, which would be difficult or impossible to annotate using other techniques.
Accurate annotations are essential for training machine learning models that perform well on real-world data.
With OCLAVI, it will be more useful when annotating complex or irregularly shaped objects, where inaccuracies can easily occur.
Training Data Models
Our tool helps to create accurate and reliable training data for machine learning models. Define object boundaries in an image and generate high-quality training data for object detection, segmentation, and tracking models.
Training data models is a crucial application as it enables the creation of high-quality datasets for machine learning models, leading to better performance in various computer vision applications.
Use cases to explore
With OCLAVI, you can annotate object boundaries in images and videos, enabling more precise and accurate robotic control and manipulation.
You can also train machine learning models that can help robots better understand and navigate their surroundings, leading to more precise and efficient robotic control.
This tool is very useful in the agriculture industry to annotate images and videos of crops and fields, enabling better crop management and yield optimization.
Use this tool to annotate images of crops, allowing farmers to monitor the growth and health of their crops over time. This information can be used to identify issues such as disease, pest infestations, or nutrient deficiencies, enabling farmers to take timely corrective action.
A valuable tool for improving the efficiency and productivity of agricultural practices, leading to better crop yields and more sustainable farming practices.
Our tool can be used in the development of Autonomous Vehicles (AVs) to enable more accurate and precise perception of the environment.
Annotate the boundaries of objects in images and videos captured by AV sensors such as cameras and Lidar to train machine learning models that can detect and classify objects in the AV's environment, such as other vehicles, pedestrians, and traffic signs.
The boundaries of lanes and road markings, enables AVs to accurately determine their position on the road and navigate safely and efficiently.