File size: 4,474 Bytes
96cce16 f86104b 96cce16 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | ---
library_name: onnx
license: apache-2.0
tags:
- foundation
- amd
- rocm
- image-classification
pipeline_tag: image-classification
---
# EfficientNetV2: Optimized for AMD ROCm
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.
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).
---
## Task Overview
**Task:** ImageNet-1k classification (1000 classes), evaluated on the Imagenette 10-class validation subset
**Dataset:** Imagenette validation set (10 classes, mapped to ImageNet-1k 1000-way ordering)
**Output metrics:** Top-1 accuracy, Top-5 accuracy
> **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.
---
## 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 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.
| 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 |
---
## Getting Started
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).
---
## Model Details
**Model Type:** Image classification (convolutional neural network)
**Base Model:** `tf_efficientnetv2_s` (timm, ImageNet-1k pretrained)
**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)
---
## Accuracy Pipeline
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
| 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. |
---
## 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/efficientnetv2)**
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
|