| --- |
| library_name: onnx |
| license: apache-2.0 |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - anomaly-detection |
| pipeline_tag: image-classification |
| --- |
| |
|  |
|
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| # PaDiM: Optimized for AMD ROCm |
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| 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. |
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| 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). |
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| --- |
|
|
| ## Task Overview |
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| **Task:** Anomaly detection and localization |
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| **Dataset:** MVTec AD (15 object/texture classes, ~5,354 images total; trains/evaluates on `bottle` by default) |
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| **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`. |
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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: |
| |
| - 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). |
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| --- |
| |
| ## 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 |
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| | 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> ..."`. |
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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/padim)** |
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| 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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