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Overview

PatchCore models can be saved after training and loaded later for evaluation or inference on new data. The saved model includes the FAISS nearest-neighbor search index and model configuration parameters.

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Model Structure

When a PatchCore model is saved using --save_patchcore_model, it creates the following structure: 
models/
└── mvtec_bottle/
    ├── nnscorer_search_index.faiss  # FAISS index with training features
    └── patchcore_params.pkl          # Model configuration parameters
The .faiss file contains the memory bank of patch features from training data. The .pkl file stores backbone architecture, layer names, dimensions, and other hyperparameters.

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Loading a Single Model

To load a pretrained PatchCore model, use the load_from_path() method: 
import torch
import patchcore.patchcore
import patchcore.common

# Set device
device = torch.device("cuda:0")

# Initialize FAISS nearest neighbor method
nn_method = patchcore.common.FaissNN(
    on_gpu=True,      # Use GPU for FAISS searches
    num_workers=8     # Number of threads for FAISS
)

# Create PatchCore instance and load from path
patchcore_model = patchcore.patchcore.PatchCore(device)
patchcore_model.load_from_path(
    load_path="results/models/mvtec_bottle",
    device=device,
    nn_method=nn_method
)
1

Initialize the device

Set up the PyTorch device (CPU or CUDA) where the model will run.
2

Configure FAISS method

Create a FaissNN instance with GPU acceleration if available. This handles nearest-neighbor searches in the feature space.
3

Load model parameters

Call load_from_path() which automatically reads both the FAISS index and pickled parameters.

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What Gets Loaded

The load_from_path() method restores the complete model state: 
{
    "backbone.name": "wideresnet50",
    "layers_to_extract_from": ["layer2", "layer3"],
    "input_shape": [3, 224, 224],
    "pretrain_embed_dimension": 1024,
    "target_embed_dimension": 1024,
    "patchsize": 3,
    "patchstride": 1,
    "anomaly_scorer_num_nn": 1
}
These parameters fully specify the model architecture and feature extraction configuration.
The FAISS index contains:
  • Coreset-subsampled patch features from training images
  • Optimized data structure for fast nearest-neighbor search
  • Typically 10% of original features after greedy coreset sampling

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Loading Model Ensembles

PatchCore supports ensemble models for improved performance. When multiple models are saved, they use a prepend naming scheme: 
import os
import gc

load_path = "results/models/mvtec_bottle"
device = torch.device("cuda:0")

# Count the number of ensemble models
n_models = len([x for x in os.listdir(load_path) if ".faiss" in x])

loaded_models = []
for i in range(n_models):
    nn_method = patchcore.common.FaissNN(on_gpu=True, num_workers=8)
    patchcore_instance = patchcore.patchcore.PatchCore(device)
    
    # Load with ensemble-specific prepend
    patchcore_instance.load_from_path(
        load_path=load_path,
        device=device,
        nn_method=nn_method,
        prepend=f"Ensemble-{i+1}-{n_models}_"
    )
    loaded_models.append(patchcore_instance)
Each ensemble member requires its own FAISS index in GPU memory. Ensure sufficient GPU memory is available when loading multiple models.

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Memory Management

When working with multiple models or datasets, proper memory management is crucial: 
import gc
import torch

# Clear GPU cache before loading
torch.cuda.empty_cache()

# Load and use model
patchcore_model.load_from_path(...)
scores, masks, _, _ = patchcore_model.predict(test_dataloader)

# Clean up after use
del patchcore_model
gc.collect()
torch.cuda.empty_cache()

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Implementation Details

The load_from_path() method (source:patchcore.py:256): 
def load_from_path(
    self,
    load_path: str,
    device: torch.device,
    nn_method: patchcore.common.FaissNN,
    prepend: str = "",
) -> None:
    LOGGER.info("Loading and initializing PatchCore.")
    with open(self._params_file(load_path, prepend), "rb") as load_file:
        patchcore_params = pickle.load(load_file)
    patchcore_params["backbone"] = patchcore.backbones.load(
        patchcore_params["backbone.name"]
    )
    patchcore_params["backbone"].name = patchcore_params["backbone.name"]
    del patchcore_params["backbone.name"]
    self.load(**patchcore_params, device=device, nn_method=nn_method)
    
    self.anomaly_scorer.load(load_path, prepend)
This method:
  1. Loads pickled parameters from patchcore_params.pkl
  2. Reconstructs the backbone network from the saved name
  3. Initializes all forward modules (feature aggregator, preprocessing, etc.)
  4. Loads the FAISS index into the anomaly scorer

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Next Steps

Run Evaluation

Evaluate loaded models on test datasets and compute metrics

Batch Prediction

Run inference on multiple images using DataLoaders

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