| --- |
| library_name: onnx |
| license: agpl-3.0 |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - image-classification |
| pipeline_tag: image-classification |
| --- |
| |
|  |
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| # YOLO26-cls: Optimized for AMD ROCm |
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| 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. |
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| 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). |
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| ## Task Overview |
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| **Task:** Image classification |
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| **Dataset:** ImageNet-1000 label space (a sample set — the imagenette subset — is staged under `datasets/samples/` for visual evaluation) |
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| **Output metrics:** Throughput (inferences/sec), latency (mean/P95/P99 ms), per-operator profiling breakdown |
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| > **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. |
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| ## AMD ROCm Optimization |
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| 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: |
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| - 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. |
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| | 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 | — | |
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| --- |
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| ## Getting Started |
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| 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). |
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| --- |
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| ## Model Details |
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| **Model Type:** Image classification (single-pass CNN classifier) |
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| **Base Model:** YOLO26-cls (Ultralytics), size `n` (nano) default |
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| **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) |
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| --- |
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| ## Accuracy Pipeline |
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| 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. |
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| --- |
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| ## Dig Deeper |
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| Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples? |
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| 📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/yolo26_classify)** |
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| 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 |
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