• Data Collection and Preparation: We gather and curate large datasets tailored to your business problem. This includes images or video data needed for training the AI model.
• Data Labeling: Using a range of advanced annotation techniques such as bounding boxes, polygons, point annotations, and semantic segmentation, we accurately label the data. Proper labeling is key to training robust AI models, and we ensure that every dataset is meticulously annotated.
• Model Selection and Training: We offer a wide variety of computer vision AI models, including but not limited to:
  • YOLO (You Only Look Once): Ideal for real-time object detection and tracking.
  • ResNet (Residual Networks): A powerful deep learning model for image classification.
  • Segmentation Models: Used for precise image segmentation tasks where the goal is to classify each pixel in an image into predefined categories.
• Based on your business needs, we choose the right model, train it using your data, and optimize its performance for the highest accuracy and speed.
• Model Integration: After training, we integrate the AI model directly into your existing software and systems, whether it’s a cloud-based application, mobile platform, or on-premises solution. Our integration services ensure that the AI works seamlessly with your workflow and meets your operational requirements.

• Object Detection Models: These models identify and classify objects in images or videos. They are highly effective in real-time applications such as quality control, security systems, and autonomous vehicles.
• Image Segmentation Models: These models break down an image into segments or regions. They are ideal for medical imaging, autonomous driving, and industrial inspection tasks where it is important to analyze each pixel of an image.
• Facial Recognition Models: Used for security, retail, and customer service applications, these models can identify and verify individuals by analyzing facial features.
• Activity Recognition Models: These models track and classify human actions, making them useful in areas such as sports analytics, health monitoring, and security surveillance.

At Appsbee, we understand that every project requires a unique approach, so we help you select the right AI model based on your specific goals and data characteristics.

• Bounding Boxes: Commonly used in object detection tasks, bounding boxes identify the location of objects in images by drawing a rectangular box around them.
• Polygon Annotation: For objects that don’t conform to a simple rectangular shape, we use polygon annotation, which provides more precise labeling for irregular shapes such as cars, people, or animals.
• Semantic Segmentation: This method classifies each pixel in an image into a particular class, making it useful for detailed analysis such as medical imaging or land-use classification.
• Point Annotations: Used for applications such as facial feature detection, where specific points like eyes, nose, and mouth need to be identified.

Our labeling experts choose the most appropriate technique based on your project's needs, ensuring that the labeled data is as accurate as possible for training the AI model.

• YOLO (You Only Look Once): A state-of-the-art, real-time object detection model that can quickly identify objects in images and videos. YOLO is perfect for use cases that require speed and efficiency, such as automated security monitoring or autonomous driving.
• ResNet (Residual Networks): An advanced deep learning model designed for high accuracy in image classification tasks. ResNet is widely used for applications like face recognition, scene analysis, and visual search engines.
• Faster R-CNN: A powerful object detection model that excels in recognizing objects in images, even under complex conditions.

By selecting the right model for each project, we ensure optimal results, allowing your business to benefit from cutting-edge computer vision AI.

• Manufacturing: Automate visual inspection for quality control by using AI to detect defects, ensure product consistency, and optimize production lines.
• Healthcare: AI models for medical imaging can assist in detecting diseases, identifying abnormalities, and automating diagnostics, leading to faster and more accurate healthcare outcomes.
• Retail: Enhance security and improve the customer experience through facial recognition, automatic checkout systems, and AI-powered inventory management.
• Agriculture: Use AI for crop monitoring, yield prediction, and pest detection to maximize agricultural output and sustainability.
• Autonomous Vehicles: Computer vision is crucial for self-driving cars to detect objects, read traffic signs, and understand road conditions in real-time.
• Security: Facial recognition and object detection can be used for monitoring public spaces and securing high-risk areas with real-time alerts and analytics.