Add model card for yolo26_classify

README.md

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+---
+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