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library_name: onnx
license: apache-2.0
tags:
- foundation
- amd
- rocm
- anomaly-detection
pipeline_tag: image-classification
---
# PaDiM: Optimized for AMD ROCm
PaDiM (Patch Distribution Modeling) models each spatial patch of a CNN backbone's feature map as a multivariate Gaussian fit only on defect-free training images, then flags anomalies via Mahalanobis distance to that patch's distribution at inference time — no anomalous training examples are needed. This repository packages training, export, and inference for anomaly detection and localization using **PyTorch and 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 PaDiM found [here](https://arxiv.org/abs/2011.08785).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [padim AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/padim) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline).
---
## Task Overview
**Task:** Anomaly detection and localization
**Dataset:** MVTec AD (15 object/texture classes, ~5,354 images total; trains/evaluates on `bottle` by default)
**Output metrics:** Image AUROC, Pixel AUROC, optimal threshold, F1 (pixel-level)
> **Model variants:** PaDiM has no `MODEL_SIZE` variants — configurable knobs are backbone architecture (`resnet18` default / `wide_resnet50_2`) and covariance mode (diagonal default / full), both passed via `ARGS`.
---
## 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, auto-quantized internally).
- Unlike models that ship pretrained weights, PaDiM must be trained (fitting Gaussian parameters per class) before export, benchmark, profile, or eval can run.
- GPU and NPU targets carry a first-run compilation/tuning cost (MIGraphX kernel tuning, VitisAI graph compilation) that can take 30+ minutes; subsequent runs are faster.
- NPU inference is auto-quantized internally by VitisAI — expect some AUROC drop relative to CPU/GPU FP32.
| 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) | First-run kernel tuning can take 30+ minutes |
| ONNX Runtime | Auto | VitisAI Execution Provider | AMD Ryzen AI NPU | Auto-quantized internally |
---
## Getting Started
For setup instructions, evaluation scripts, and custom configuration options, see the [padim on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/padim).
---
## Model Details
**Model Type:** Anomaly detection and localization (patch distribution modeling over a CNN backbone)
**Base Model:** ResNet-18 backbone (default) — Wide ResNet-50-2 also supported
**Model Stats:**
- Backbone: `resnet18` (default) — `wide_resnet50_2` also supported
- Covariance mode: diagonal (default, fastest) — full covariance also supported (~1-2% better AUROC, ~55x slower Mahalanobis)
- ONNX export levels: backbone (11 MB), full (11 MB, recommended), base/mahalanobis (132 MB)
- Default trained/evaluated class: `bottle` (of 15 MVTec AD classes)
---
## Accuracy Pipeline
Higher AUROC means the model ranks anomalous samples above normal ones more consistently — 1.0 is a perfect ranking, 0.5 is random chance. The PaDiM paper reports ~96.7% Image AUROC / 96.0% Pixel AUROC with ResNet18, and ~97.5%/97.5% with Wide ResNet-50-2, on MVTec AD; on-device numbers noticeably below that suggest a training/data issue or (for NPU) quantization-induced accuracy loss.
### Metrics Explained
| Metric | Description |
|--------|-------------|
| Image AUROC | Probability that a randomly chosen anomalous image scores higher (via its max per-image patch anomaly score) than a randomly chosen normal image — measures whole-image anomaly classification skill, independent of any threshold choice. |
| Pixel AUROC | Same ranking measure computed pixel-by-pixel against the ground-truth defect masks — captures localization quality. |
| Optimal threshold | The pixel anomaly score cutoff that maximizes pixel-level F1 on the test set. Used only to binarize the heatmap for saved visualizations. |
| F1 (pixel-level) | Harmonic mean of pixel precision and recall at the optimal threshold — a single fixed-operating-point score, unlike AUROC which integrates over every threshold. |
No measured on-device results are included in the source README yet — by default only the `bottle` class is trained/evaluated; additional classes can be trained and evaluated with `make train-cpu ARGS="--classes <class1> <class2> ..."`.
---
## 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/padim)**
The GitHub repository includes:
- Training and ONNX export scripts (four export levels trading off latency vs. pipeline coverage)
- MVTec AD dataset staging across all 15 classes
- Image/Pixel AUROC + F1 evaluation pipeline with anomaly visualization overlays
- Benchmarking and reproduction instructions for CPU, GPU, and NPU
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