| --- |
| library_name: onnx |
| license: apache-2.0 |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - image-classification |
| pipeline_tag: image-classification |
| --- |
| |
|  |
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| # EfficientNetV2: Optimized for AMD ROCm |
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| EfficientNetV2 (`tf_efficientnetv2_s`) is a convolutional image classification model trained on ImageNet-1k. This repository packages inference for image classification using **ONNX Runtime** with a FastAPI serving layer, 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 EfficientNetV2 found [here](https://github.com/huggingface/pytorch-image-models). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [efficientnetv2 AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/efficientnetv2) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline). |
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| --- |
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| ## Task Overview |
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| **Task:** ImageNet-1k classification (1000 classes), evaluated on the Imagenette 10-class validation subset |
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| **Dataset:** Imagenette validation set (10 classes, mapped to ImageNet-1k 1000-way ordering) |
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| **Output metrics:** Top-1 accuracy, Top-5 accuracy |
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| > **NPU note:** VitisAI EP accepts FP32 ONNX and performs quantization internally to run on NPU hardware; expect small Top-1/Top-5 differences vs CPU/GPU FP32. |
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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 (FP32 only), GPU (MIGraphX execution provider — FP32/FP16/BF16/INT8 via runtime quantization), and NPU (VitisAI execution provider, FP32 ONNX with internal quantization). |
| - All GPU precision benchmarks use a single FP32 ONNX model with runtime quantization via MIGraphX EP options — no pre-quantized model files required. |
| - No code changes required versus the upstream timm 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) | Runtime quantization, single FP32 ONNX model | |
| | ONNX Runtime | FP32 | VitisAI Execution Provider | AMD Ryzen AI NPU | 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 [efficientnetv2 on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/efficientnetv2). |
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| --- |
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| ## Model Details |
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| **Model Type:** Image classification (convolutional neural network) |
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| **Base Model:** `tf_efficientnetv2_s` (timm, ImageNet-1k pretrained) |
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| **Model Stats:** |
| - Export size: 384×384 (native eval resolution for `tf_efficientnetv2_s`), overridable via `EFFNET_IMG_SIZE` |
| - ONNX model size: ~85 MB |
| - Precision tested: FP32 (CPU/NPU); FP32, FP16, BF16, INT8 (GPU) |
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| --- |
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| ## Accuracy Pipeline |
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| Top-1 measures the fraction of images where the model's highest-confidence prediction matches the ground-truth class; Top-5 is looser, counting a match if the true class appears among the five highest-confidence predictions. Preprocessing matches the timm eval pipeline for `tf_efficientnetv2_s` (resize shorter side to 384, crop_pct=1.0, Inception mean/std normalization, bicubic interpolation). |
| |
| ### Metrics Explained |
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| | Metric | Description | |
| |--------|-------------| |
| | Top-1 | Fraction of images where the model's highest-confidence prediction matches the ground-truth class. | |
| | Top-5 | Fraction of images where the ground-truth class appears among the model's five highest-confidence predictions. | |
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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/efficientnetv2)** |
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| The GitHub repository includes: |
| - Benchmark, profile, and evaluation Makefile targets for CPU, GPU, and NPU |
| - Runtime quantization details for MIGraphX (FP16/BF16/INT8) |
| - Imagenette dataset staging and full-validation evaluation pipeline |
| - ONNX export scripts producing a device-agnostic FP32 model |
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