gt111lk/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.

Inference via API

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",
)

Inference via CLI

# 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