Description: I have created a machine learning model for object detection using ML.net Model Builder (MLModel1.zip). The model was successfully tested using the automatically generated project by ...

In response to the challenges of small object ... the YOLOv11n model was improved. Specifically, an EfficiBackbone module was designed for the backbone part, the C3K2 was improved using the RipViT ...

and implementing Python-based object detection, counting, and positional tracking. By using pre-built pipelines and GPIO components, you can create customized AI-driven solutions for applications ...

Abstract: The YOLOv5 object detection model is used in this paper to detect dogs, while the ML.Net is used to classify dogs in residential areas. A Raspberry Pi is used to transmit live video capture ...

OS & Version: Windows 10 ML.NET Version: ML.NET v3.0.0-preview.23266.6.NET Version: .NET 7.0 public class ModelInput { [LoadColumn(0)] [ColumnName(@"Labels")] public ...

An object detection model is adaptable since it can be taught to recognize and find several objects. The process of creating item localizations often ... R-FCNs use a region-based detector. Instead of ...

The fields of machine learning and deep learning have resulted in a plethora of object recognition algorithms, such as SSD, SSP-net, SVM ... tiny objects remains. Creating a lightweight and reliable ...

In order to start using this scenario, Model ... object detection, question answering, and named entity recognition. Another point relates to updating the version of LightGBM supported in ML.NET ...