Comments (7)
Hello, that is correct. All models in currently Anomalib are unsupervised, using only good data for training. Labeled anomalous images are only used in test phase, where you require ground truth annotation to tell how good the model is performing.
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Hello. You can still train without the labels, but you won't be able to test the model.
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Thanks for your reply.
Are you saying that I can train in a state where normal and abnormal are mixed?
I think good, bad is labels.
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Sorry I understood the question incorrectly. All models implemented in Anomalib only utilize normal data for training, so you should be able to train the model using your good data, which does not require labels. The abnormal data can be used as part of the test set and shouldn't be mixed with good data.
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hi to clarify, all models that exist in anomalib are unsupervised? Or in this case semi-supervised, I am not sure, but the learning is only done on the good images, that is the only data required, correct? Thank you, as I was confused if these were supervised(requiring masks for training, thus labeling for custom data) or not
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Is it possible, to test operation without using ground truth ?
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If you just need the result of anomaly detection but not localizaition, the ground truth is not required, you can just prepare some black image as the fake ground truth for test phase.
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Related Issues (20)
- No module named 'anomalib.config' HOT 1
- [Bug]: Training with PADiM does not care about "max_epochs" parameter HOT 1
- input_shape error when using notebook Fastflow demo HOT 1
- [Bug]: Trained model works well during test, but prediction does not HOT 1
- [Bug/Task]: Error in implementation of GANomaly's generator loss
- Hello, I would like to use it for industrial defect segmentation, but my defects are relatively small. Do you have any recommended commands, configuration files, and models
- [Bug]: EfficientAd strange anomaly map results HOT 1
- [Bug]: ImportError: cannot import name 'OpenVINOInferencer'
- `Trainer.fit` stopped: `max_epochs=1` reached. Epoch 0/0 ------------------ 2/2 0:00:15 • 0:00:00 6.37it/s pixel_AUROC: 0.000 pixel_F1Score: 0.000 HOT 5
- [Bug]: tilling with padim collapse HOT 4
- [Bug]: v1.1.0 FileNotFoundError: No such file or directory: 'tools\\benchmarking\\benchmark_params.yaml' HOT 2
- [Task]: Have you considered integrating the RD++ algorithm? HOT 2
- [Bug]: Can't download efficientAD pretrained weights HOT 9
- [Bug]: How to solve this problem self.image_threshold = AnomalyScoreThreshold().cpu() HOT 2
- [Bug]: EfficientAd is slower than other models in anomalib HOT 5
- [Task]: Missing modules may fail silently
- [Bug]: Error installing with pip version 24.1 HOT 1
- [Bug]: Issue with `self.model_size` Initialization in `EfficientAD` Class HOT 5
- [Bug]: RuntimeError: vstack expects a non-empty TensorList
- [Bug]: Training Fails on DataBricks Due to symlink not supported in DBFS HOT 2
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