dinov2

DINOv2: Optimized for AMD ROCm

DINOv2 is a self-supervised Vision Transformer evaluated here as an ImageNet linear classifier (ViT-S/B/L/G with a 1000-class head). This repository packages evaluation and inference for image classification using ONNX Runtime, exported and validated for AMD ROCm (via the MIGraphX execution provider) so it runs efficiently on AMD GPUs, CPUs, and NPUs.

This is based on the implementation of DINOv2 found here. This repository contains configurations and scripts optimized for AMD® ROCm™ platforms. You can use the dinov2 AMD scripts to reproduce results or export with custom configurations. More details on model performance can be found here.

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Task Overview

Task: Image classification

Dataset: ImageNet validation (~50,000 images, 1,000 classes)

Output metrics: Top-1 accuracy, Top-5 accuracy

Model variants: Default is base (dinov2_vitb14_lc). Override with MODEL_SIZE=small|base|large|giant (hub names: dinov2_vits14_lc, dinov2_vitb14_lc, dinov2_vitl14_lc, dinov2_vitg14_lc).

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AMD ROCm Optimization

This model export has been adapted and validated for AMD Instinct™ / Radeon™ GPUs running ROCm, as well as AMD CPUs. Key points:

• Validated backends: ONNX Runtime across CPU (native CPU execution provider), GPU (MIGraphX execution provider, ROCm-based), and NPU (VitisAI execution provider).
• CPU execution path supported independently of GPU/NPU availability, for environments without ROCm-capable GPU or NPU hardware.

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Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the dinov2 on GitHub.

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Model Details

Model Type: Self-supervised Vision Transformer (ViT) image classifier with a linear-probe head

Base Model: facebookresearch/dinov2 (DINOv2 ViT-B/14 with linear classification head)

Model Stats:

• Model variant: base (default) — small, large, giant also supported
• Precision tested: FP32, FP16, BF16, INT8

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Accuracy Pipeline

Higher Top-1 accuracy means a larger fraction of images are classified with the correct label as the top prediction — 100% is perfect, 0% is chance-level for random guessing. Top-5 allows credit when the true class appears anywhere in the model's five highest-scoring labels; values above ~80% Top-1 on ImageNet val are considered strong for this linear-probe setup.

Metrics Explained

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Dig Deeper

Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?

📂 View the full project on GitHub

The GitHub repository includes:

• Makefile-driven benchmark, profile, and evaluation targets for CPU, GPU, and NPU
• Per-device precision configs (FP32/FP16/BF16/INT8) and ONNX export scripts
• ImageNet dataset staging and full-validation evaluation pipeline
• Additional DINOv2 model variants (small, large, giant)