YOLOv12 & YOLO26: Custom Object Detection & Web Apps 2026

In this lecture, you’ll learn how to use YOLO26 for object detection, instance segmentation, pose estimation, image classification, and oriented bounding box (OBB) object detection.

In this lecture, you will learn how to annotate and label a custom dataset using Roboflow for object detection tasks. We will cover image upload, creating classes, drawing bounding boxes, organizing data, and exporting the dataset in YOLO format. By the end of this lecture, you will have a fully prepared dataset ready for training your YOLO26 object detection model.

In this lecture, you will learn how to train a YOLO26 object detection model on a custom pothole dataset. We will cover dataset configuration, training setup, model parameters, and performance monitoring. You will also learn how to evaluate the trained model and run inference to detect potholes in new images and videos.

In this lecture, you will learn how to annotate and label a custom dataset for instance segmentation using Roboflow. We will cover creating classes, drawing masks, organizing data, and exporting it in YOLO26 format. By the end, your dataset will be fully prepared for training a YOLO26 instance segmentation model.

In this lecture, you will learn how to train a YOLO26 instance segmentation model on a custom pothole dataset. The lecture covers dataset configuration, model setup, training, performance evaluation, and running inference to detect and segment potholes in images and videos. By the end, you will be able to build a custom instance segmentation model using YOLO26.

In this lecture, we’ll learn how to train the YOLO26 object detection model on a custom dataset for African wildlife detection. We’ll also analyze the results in detail and test the fine-tuned model on random images.

In this lecture, we’ll learn how to train/ fine-tune the YOLO26 instance segmentation model on a custom dataset for package segmentation. We’ll also analyze the results and test the fine-tuned model on random images.

In this lecture, we’ll learn how to train/ fine-tune the YOLO26 pose estimation model on a custom dataset for human activity recognition.

In this lecture, we’ll learn how to train/ fine-tune the YOLO26 classification model on a custom dataset for plant classification.

YOLO26 provides a versatile set of options for exporting your trained models to different formats, enabling deployment across various platforms and devices. In this lecture, we’ll walk you through the model export process, showing how to achieve maximum compatibility and performance.

Learn to create real-time vehicle intensity heatmaps from YOLO26 detections. Filter vehicles, map traffic intensity, apply blur and color maps, and overlay on video for traffic visualization.