---
library_name: onnx
license: agpl-3.0
tags:
  - foundation
  - amd
  - rocm
  - image-classification
pipeline_tag: image-classification
---

![](assets/yolo26_classify.png)

# YOLO26-cls: Optimized for AMD ROCm

YOLO26-cls is a real-time image classification model that predicts a class over the 1000 ImageNet categories in a single forward pass. This repository packages inference for image classification using **ONNX Runtime**, exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs, CPUs, and NPUs.

This is based on the implementation of YOLO26 found [here](https://github.com/ultralytics/ultralytics).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [yolo26_classify AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/yolo26_classify) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline).

---

## Task Overview

**Task:** Image classification

**Dataset:** ImageNet-1000 label space (a sample set — the imagenette subset — is staged under `datasets/samples/` for visual evaluation)

**Output metrics:** Throughput (inferences/sec), latency (mean/P95/P99 ms), per-operator profiling breakdown

> **Model variants:** Default is **n** (nano). Override with `MODEL_SIZE=n/s/m/l/x` — note the classify download list ships `n/s/m/l` weights only, so `MODEL_SIZE=x` may not resolve to a hosted weight.

---

## AMD ROCm Optimization

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

- Validated backends: **ONNX Runtime** across CPU (FP32), GPU (MIGraphX Execution Provider — FP32/FP16/BF16/INT8), and NPU (VitisAI Execution Provider — BF16).
- No code changes required versus the upstream Ultralytics YOLO26-cls implementation — only environment/runtime configuration differs.

| Runtime | Precision | Backend | Hardware | Notes |
|---|---|---|---|---|
| ONNX Runtime | FP32 | CPU Execution Provider | AMD CPU | — |
| ONNX Runtime | FP32 / FP16 / BF16 / INT8 | MIGraphX Execution Provider | AMD Instinct™ / Radeon™ GPU (ROCm) | — |
| ONNX Runtime | BF16 | VitisAI Execution Provider | AMD Ryzen AI NPU | — |

---

## Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the [yolo26_classify on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/yolo26_classify).

---

## Model Details

**Model Type:** Image classification (single-pass CNN classifier)

**Base Model:** YOLO26-cls (Ultralytics), size `n` (nano) default

**Model Stats:**
- Input (`images`): `(1, 3, 224, 224)` float32
- Output (`output0`): `(1, 1000)` float32
- Model sizes: `n/s/m/l` ship pretrained classify weights (`x` may not resolve to a hosted weight)
- Precision tested: FP32 (CPU); FP32, FP16, BF16, INT8 (GPU); BF16 (NPU)

---

## Accuracy Pipeline

Accuracy evaluation is not yet implemented for this model. The bundled imagenette subset (10 ImageNet classes, staged under `datasets/samples/`) is used only for visual top-5 sanity checks via annotated overlays, not full ImageNet-1000 accuracy scoring.

---

## Dig Deeper

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

📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/yolo26_classify)**

The GitHub repository includes:
- Setup and prerequisites for ROCm environments
- Per-operator latency profiling scripts (including NPU AI Analyzer integration)
- Sample-image evaluation with annotated top-5 class overlays
- Benchmarking and reproduction instructions across CPU, GPU, and NPU