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The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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"""Getting Started with Anomalib Training using the Python API.
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This example shows the basic steps to train an anomaly detection model
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using the Anomalib Python API.
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"""
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# 1. Import required modules
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from anomalib.data import MVTecAD
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from anomalib.engine import Engine
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from anomalib.models import EfficientAd
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# 2. Create a dataset
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# MVTecAD is a popular dataset for anomaly detection
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datamodule = MVTecAD(
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root="./datasets/MVTecAD", # Path to download/store the dataset
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category="bottle", # MVTec category to use
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train_batch_size=32, # Number of images per training batch
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eval_batch_size=32, # Number of images per validation/test batch
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num_workers=8, # Number of parallel processes for data loading
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)
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# 3. Initialize the model
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# EfficientAd is a good default choice for beginners
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model = EfficientAd()
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# 4. Create the training engine
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engine = Engine(max_epochs=10) # Train for 10 epochs
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# 5. Train the model
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engine.fit(datamodule=datamodule, model=model)
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