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39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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"""Example showing how to use the MVTecAD dataset with Anomalib.
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MVTecAD is a widely-used dataset for anomaly detection, containing multiple
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categories of industrial objects with various types of defects.
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"""
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from anomalib.data import MVTecAD
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# 1. Basic Usage
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# Load a specific category with default settings
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datamodule = MVTecAD(
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root="./datasets/MVTecAD",
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category="bottle",
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)
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# 2. Advanced Configuration
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# Customize data loading and preprocessing
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datamodule = MVTecAD(
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root="./datasets/MVTecAD",
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category="bottle",
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train_batch_size=32,
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eval_batch_size=32,
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num_workers=8,
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val_split_mode="from_test", # Create validation set from test set
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val_split_ratio=0.5, # Use 50% of test set for validation
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)
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# 3. Using Multiple Categories
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# Train on multiple categories (if supported by the model)
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for category in ["bottle", "cable", "capsule"]:
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category_data = MVTecAD(
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root="./datasets/MVTecAD",
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category=category,
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)
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# Use category_data with your model...
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