Pretrained Models

Overview

This repository provides pretrained PatchCore models that achieve state-of-the-art performance on MVTec AD, with up to 99.6% image-level AUROC, 98.4% pixel-level AUROC, and >95% PRO score. Each model is trained on one of the 15 MVTec AD subdatasets and can be directly loaded for inference without retraining.

Available Model Configurations

WideResNet50 Baseline

Standard single-backbone model optimized for 224x224 images

Image AUROC: 99.2%
Pixel AUROC: 98.1%
PRO Score: 94.4%

Ensemble Model

Multi-backbone ensemble with WR101/ResNext101/DenseNet201

Image AUROC: 99.6%
Pixel AUROC: 98.2%
PRO Score: 94.9%

Model Structure

Pretrained models are organized in the following directory structure:

models/
├── IM224_WR50_L2-3_P01_D1024-1024_PS-3_AN-1/
│   └── models/
│       ├── mvtec_bottle/
│       │   ├── nnscorer_search_index.faiss
│       │   └── patchcore_params.pkl
│       ├── mvtec_cable/
│       ├── mvtec_capsule/
│       └── ... (15 categories total)
├── IM224_Ensemble_L2-3_P001_D1024-384_PS-3_AN-1/
│   └── models/
│       └── ... (same 15 categories)

Model Files

Each category model contains two files:

nnscorer_search_index.faiss - FAISS index for nearest neighbor search
patchcore_params.pkl - Serialized PatchCore parameters and memory bank

MVTec AD Categories

Pretrained models are available for all 15 MVTec AD object and texture categories: Object Categories:

bottle
cable
capsule
hazelnut
metal_nut
pill
screw
toothbrush
transistor
zipper

Texture Categories:

carpet
grid
leather
tile
wood

Loading Pretrained Models

Evaluation Script

Use the provided evaluation script to load and test pretrained models:

datapath=/path/to/mvtec/data
loadpath=/path/to/pretrained/models
modelfolder=IM224_WR50_L2-3_P001_D1024-1024_PS-3_AN-1
savefolder=evaluated_results/$modelfolder

datasets=('bottle' 'cable' 'capsule' 'carpet' 'grid' 'hazelnut' \
          'leather' 'metal_nut' 'pill' 'screw' 'tile' 'toothbrush' \
          'transistor' 'wood' 'zipper')

dataset_flags=($(for dataset in "${datasets[@]}"; do echo '-d '$dataset; done))
model_flags=($(for dataset in "${datasets[@]}"; do echo '-p '$loadpath'/'$modelfolder'/models/mvtec_'$dataset; done))

python bin/load_and_evaluate_patchcore.py --gpu 0 --seed 0 $savefolder \
  patch_core_loader "${model_flags[@]}" --faiss_on_gpu \
  dataset --resize 366 --imagesize 320 "${dataset_flags[@]}" mvtec $datapath

Loading Individual Models

To load a single category model:

python bin/load_and_evaluate_patchcore.py --gpu 0 --seed 0 results \
  patch_core_loader -p models/IM224_WR50_L2-3_P01_D1024-1024_PS-3_AN-1/models/mvtec_bottle \
  --faiss_on_gpu \
  dataset --resize 366 --imagesize 320 -d bottle mvtec /path/to/mvtec

Model Configurations

WideResNet50 Baseline (IM224)

Configuration:

Backbone: Wide ResNet-50
Feature layers: layer2, layer3
Input size: 224x224 (resized from 256x256)
Coreset sampling: 10%
Embedding dimensions: 1024 → 1024
Patch size: 3
Nearest neighbors: 1

Model ID: IM224_WR50_L2-3_P01_D1024-1024_PS-3_AN-1

Ensemble Model (IM224)

Configuration:

Backbones: Wide ResNet-101, ResNeXt-101, DenseNet-201
Feature layers:
- WR101: layer2, layer3
- ResNeXt101: layer2, layer3
- DenseNet201: denseblock2, denseblock3
Input size: 224x224
Coreset sampling: 1%
Embedding dimensions: 1024 → 384
Patch size: 3
Nearest neighbors: 1

Model ID: IM224_Ensemble_L2-3_P001_D1024-384_PS-3_AN-1

Higher Resolution Models (IM320)

Models trained on 320x320 images achieve similar or better performance:

WR50 (IM320): 99.3% Image AUROC, 97.8% Pixel AUROC
Ensemble (IM320): 99.6% Image AUROC, 98.2% Pixel AUROC

Higher resolution models (320x320) require more GPU memory but can provide better localization accuracy.

Inference Requirements

Hardware

GPU Memory: 11GB recommended (varies with image size)
GPU: CUDA-compatible GPU for FAISS acceleration
CPU: Any modern multi-core processor

Software Dependencies

pip install torch torchvision
pip install faiss-gpu  # or faiss-cpu
pip install timm
pip install scikit-learn
pip install Pillow

See requirements.txt for complete dependency list.

Model Performance

For detailed per-category performance metrics, see the Performance Benchmarks page.

Training Custom Models

To train your own PatchCore model with the same configuration:

python bin/run_patchcore.py --gpu 0 --seed 0 --save_patchcore_model \
  --log_group IM224_WR50_L2-3_P01_D1024-1024_PS-3_AN-1_S0 \
  --log_project MVTecAD_Results results \
  patch_core -b wideresnet50 -le layer2 -le layer3 --faiss_on_gpu \
  --pretrain_embed_dimension 1024 --target_embed_dimension 1024 \
  --anomaly_scorer_num_nn 1 --patchsize 3 \
  sampler -p 0.1 approx_greedy_coreset \
  dataset --resize 256 --imagesize 224 -d bottle mvtec /path/to/mvtec

See Training Guide for complete training instructions.

Output Format

After evaluation, results are saved as:

evaluated_results/
├── results.csv           # Performance metrics for all categories
└── segmentation_images/  # Visual anomaly maps (if enabled)

The results.csv contains:

instance_auroc - Image-level AUROC
full_pixel_auroc - Pixel-level AUROC
full_pro - PRO score
anomaly_pixel_auroc - AUROC on anomalous pixels only
anomaly_pro - PRO on anomalous regions only

All pretrained models use ImageNet-pretrained backbone weights for feature extraction.

Get Started

Core Concepts

Training

Inference

Model Zoo

Overview

Available Model Configurations

WideResNet50 Baseline

Ensemble Model

Model Structure

Model Files

MVTec AD Categories

Loading Pretrained Models

Evaluation Script

Loading Individual Models

Model Configurations

WideResNet50 Baseline (IM224)

Ensemble Model (IM224)

Higher Resolution Models (IM320)

Inference Requirements

Hardware

Software Dependencies

Model Performance

Training Custom Models

Output Format

​Overview

​Available Model Configurations

WideResNet50 Baseline

Ensemble Model

​Model Structure

​Model Files

​MVTec AD Categories

​Loading Pretrained Models

​Evaluation Script

​Loading Individual Models

​Model Configurations

​WideResNet50 Baseline (IM224)

​Ensemble Model (IM224)

​Higher Resolution Models (IM320)

​Inference Requirements

​Hardware

​Software Dependencies

​Model Performance

​Training Custom Models

​Output Format

Overview

Available Model Configurations

Model Structure

Model Files

MVTec AD Categories

Loading Pretrained Models

Evaluation Script

Loading Individual Models

Model Configurations

WideResNet50 Baseline (IM224)

Ensemble Model (IM224)

Higher Resolution Models (IM320)

Inference Requirements

Hardware

Software Dependencies

Model Performance

Training Custom Models

Output Format