IADBE_Models

🤖 Inference

IADBE includes multiple inferencing scripts, including Torch, Lightning, Gradio, and OpenVINO inferencers to perform inference using the trained/exported model. Here we show an inference example using the Lightning inferencer. If you want to try our pre-trained model without training, you can find it here. Check here for details on how to use the IADBE platform.

<details> <summary>Inference via API</summary>

The following example demonstrates how to perform Lightning inference by loading a model from a checkpoint file.

# Assuming the datamodule, custom_model and engine is initialized from the previous step,
# a prediction via a checkpoint file can be performed as follows:
predictions = engine.predict(
    datamodule=datamodule,
    model=model,
    ckpt_path="path/to/checkpoint.ckpt",
)

</details> <details> <summary>Inference via CLI</summary>

# To get help about the arguments, run:
anomalib predict -h

# Predict by using the default values.
anomalib predict --custom_model anomalib.models.Patchcore \
                 --data anomalib.data.MVTec \
                 --ckpt_path <path/to/custom_model.ckpt>

# Predict by overriding arguments.
anomalib predict --custom_model anomalib.models.Patchcore \
                 --data anomalib.data.MVTec \
                 --ckpt_path <path/to/custom_model.ckpt>
                 --return_predictions

# Predict by using a config file.
anomalib predict --config <path/to/config> --return_predictions

</details>