| --- |
| library_name: onnx |
| license: apache-2.0 |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - image-classification |
| pipeline_tag: image-classification |
| --- |
| |
|  |
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| # ResNet-50: Optimized for AMD ROCm |
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| ResNet-50 is a 50-layer deep residual network for image classification over the 1000 ImageNet categories. 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 ResNet-50 found [here](https://huggingface.co/microsoft/resnet-50). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [resnet-50 AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/resnet-50) to reproduce results or export with custom configurations. |
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| --- |
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| ## Task Overview |
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| **Task:** Image classification |
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| **Dataset:** ImageNet-1000 label space (a small sample set is staged under `dataset/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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| --- |
|
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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 (native execution provider), GPU (MIGraphX execution provider, ROCm-based), and NPU (VitisAI execution provider). |
| - No code changes required versus the upstream `microsoft/resnet-50` export — only environment/runtime configuration differs. |
| - CPU fallback path supported for environments without a ROCm-capable GPU. |
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| | Runtime | Precision | Backend | Hardware | Notes | |
| |---|---|---|---|---| |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | CPU Execution Provider | AMD CPU | — | |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | MIGraphX Execution Provider | AMD Instinct™ / Radeon™ GPU (ROCm) | — | |
| | ONNX Runtime | FP32 | VitisAI Execution Provider | AMD Ryzen AI NPU | Accepts FP32 input; VitisAI quantizes internally | |
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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 [resnet-50 on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/resnet-50). |
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| --- |
|
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| ## Model Details |
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| **Model Type:** Image classification, ResNet-50 (50-layer deep residual network) |
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| **Base Model:** `microsoft/resnet-50` (ResNet-50, ImageNet-1000) |
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| **Model Stats:** |
| - Input resolution: 224×224 (`pixel_values`: `(batch, 3, 224, 224)`; `logits` output: `(batch, 1000)`) |
| - Precision tested: FP32, FP16, BF16, INT8 (CPU/GPU); FP32 auto-quantized (NPU) |
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| --- |
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| ## Accuracy Pipeline |
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| Accuracy evaluation is not yet implemented for this model. |
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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/resnet-50)** |
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| The GitHub repository includes: |
| - Benchmark and profile scripts for CPU, GPU, and NPU (including AI Analyzer profiling) |
| - ONNX export and pretrained weight download automation |
| - Sample-image evaluation with annotated top-prediction overlays |
| - `make metrics` aggregation into `METRICS_TABLE.md` |
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