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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.
54 lines
2.1 KiB
Bash
54 lines
2.1 KiB
Bash
#!/bin/bash
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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# Advanced Custom Components with Anomalib CLI
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# ---------------------------------------
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# This example shows how to use custom metrics and evaluators.
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# 1. Basic Metrics Setup
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# Create metrics with specific fields to compute
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echo "Training with basic metrics setup..."
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anomalib train \
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--model efficient_ad \
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--model.evaluator.test_metrics auroc f1_score \
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--model.evaluator.val_metrics auroc f1_score \
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--model.evaluator.metrics.auroc.fields pred_score gt_label \
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--model.evaluator.metrics.f1_score.fields pred_label gt_label \
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--trainer.default_root_dir results/basic_metrics
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# 2. Advanced Metrics Setup
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# Create a comprehensive set of metrics
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echo -e "\nTraining with comprehensive metrics..."
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anomalib train \
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--model efficient_ad \
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--model.evaluator.test_metrics auroc f1_score precision recall \
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--model.evaluator.val_metrics auroc f1_score precision recall \
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--model.evaluator.metrics.auroc.fields pred_score gt_label \
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--model.evaluator.metrics.f1_score.fields pred_label gt_label \
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--model.evaluator.metrics.precision.fields pred_label gt_label \
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--model.evaluator.metrics.recall.fields pred_label gt_label \
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--model.evaluator.compute_on_cpu true \
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--trainer.default_root_dir results/advanced_metrics
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# 3. Complete Training Pipeline with Custom Metrics
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# Initialize components and run training
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echo -e "\nRunning complete training pipeline..."
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anomalib train \
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--model efficient_ad \
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--model.teacher_out_channels 384 \
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--data.category bottle \
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--data.train_batch_size 32 \
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--data.eval_batch_size 32 \
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--data.num_workers 8 \
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--model.evaluator.test_metrics auroc f1_score precision recall \
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--model.evaluator.val_metrics auroc f1_score precision recall \
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--model.evaluator.compute_on_cpu true \
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--trainer.max_epochs 20 \
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--trainer.accelerator auto \
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--trainer.devices 1 \
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--trainer.gradient_clip_val 0.1 \
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--trainer.enable_checkpointing true \
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--trainer.default_root_dir results/complete
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