Computer vision

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These notebooks train computer vision models on AI Runtime. They cover image classification, hyperparameter tuning with Ray Tune, and object detection.

Tutorial	Description
Image classification using convolutional neural network	This notebook provides a simple example of how to train a 2-D convolution neural network on serverless GPUs for image classification.
CIFAR-10 hyperparameter tuning with Ray Tune	This notebook runs concurrent fractional-GPU trials for a PyTorch image classifier and uses the asynchronous successive halving algorithm (ASHA) to stop underperforming configurations early.
Object detection using RetinaNet	This notebook demonstrates how to train an object detection model using RetinaNet on serverless GPU.
Object detection using YOLO11n	This notebook demonstrates how to train a YOLO11n object detection model on the COCO128 dataset using serverless GPU, with MLflow tracking and Model Serving deployment.

Tutorial	Description
Image classification using convolutional neural network	This notebook provides a simple example of how to train a 2-D convolution neural network on serverless GPUs for image classification.
CIFAR-10 hyperparameter tuning with Ray Tune	This notebook runs concurrent fractional-GPU trials for a PyTorch image classifier and uses the asynchronous successive halving algorithm (ASHA) to stop underperforming configurations early.
Object detection using RetinaNet	This notebook demonstrates how to train an object detection model using RetinaNet on serverless GPU.
Object detection using YOLO11n	This notebook demonstrates how to train a YOLO11n object detection model on the COCO128 dataset using serverless GPU, with MLflow tracking and Model Serving deployment.