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การเข้าถึงหน้านี้ต้องได้รับการอนุญาต คุณสามารถลอง ลงชื่อเข้าใช้หรือเปลี่ยนไดเรกทอรีได้
การเข้าถึงหน้านี้ต้องได้รับการอนุญาต คุณสามารถลองเปลี่ยนไดเรกทอรีได้
These examples train computer vision models on AI Runtime. They cover image classification, hyperparameter tuning with Ray Tune, and object detection.
| Example | Interface | Description |
|---|---|---|
| Image classification using convolutional neural network | Notebook | This notebook provides a simple example of how to train a 2-D convolution neural network on AI Runtime for image classification. |
| CIFAR-10 hyperparameter tuning with Ray Tune | Notebook | 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 | Notebook | This notebook demonstrates how to train an object detection model using RetinaNet on AI Runtime. |
| Object detection using YOLO11n | Notebook | This notebook demonstrates how to train a YOLO11n object detection model on the COCO128 dataset using AI Runtime, with MLflow tracking and Model Serving deployment. |
| ResNet-50 fine-tuning on Imagenette | CLI | Use Ray Train to fine-tune ResNet-50 with one distributed data parallel worker per A10 GPU across two nodes and log metrics to MLflow. |
| Image classification fine-tuning with DINOv3 | CLI | Fine-tune a DINOv3 vision transformer on Oxford Flowers-102 on one A10 GPU and package the selected model for inference. |
| Video action classification | CLI | Train a recurrent classifier on one A10 GPU to recognize actions from video frame features produced by ResNet-18. |
| Batch image understanding with Qwen3-VL-8B and vLLM | CLI | Use Ray Data and vLLM to caption and classify images on an 8x H100 node with structured outputs. |