Bounding Box Annotation for Visual Perception AI Model Training

Bounding Box Annotation for object detection in machine learning and AI. Anolytics provides the images annotation services for computer vision based AI model training. It can annotate the object of interest in the images to accuracy to make it easily recognizable to machines through computer vision. Bounding Box can make the different types of objects detected by the visual perception based models like self-driving cars and autonomous vehicles to recognize the objects and move accordingly.

Bounding Box Annotation for Self-driving Cars

Self-driving cars or autonomous vehicles use the computer vision technology to detect or recognize the objects on the road and bounding box annotation makes such objects recognizable. Anolytics can create huge amount of data sets created for developing the AI model for self-driving cars. It can annotate all types of objects like streets like lanes, traffic, potholes, signals, and other objects with best accuracy.

Bounding Box for Retail and Fashion

Apart from self-driving cars, Bounding Box annotation is also necessary to develop the AI model for retail or fashion industry. It can make the fashion accessories and items sold in the retail shops recognizable with best level of accuracy for right prediction when used in the real-life.

Anolytics, is specialized in image annotating services with expertise in bounding box annotation to making AI models to detect or recognize the different types of objects accurately. Anolytics is working with team of highly-skilled and experienced annotators to create the high-quality training data sets at low cost while ensuring the privacy and security of the client’s data sets.

bounding box annotation

Anolytics

Anolytics provides image, text, audio and video annotation services for computer vision and machine learning. Companies working on AI-based machine learning technologies who want to build a high-quality model may acquire high-quality annotated data with total confidentiality and anonymity, as well as cost-effective pricing.

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