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Anomalib in 15 Minutes

Anomalib in 15 Minutes#

This section will walk you through the steps to train a model and use it to detect anomalies in a dataset.

Installation#

Installation is simple and can be done in two ways. The first is through PyPI, and the second is through a local installation. PyPI installation is recommended if you want to use the library without making any changes to the source code. If you want to make changes to the library, then a local installation is recommended.

pip install anomalib

# Use of virtual environment is highy recommended
# Using conda
yes | conda create -n anomalib_env python=3.10
conda activate anomalib_env

# Or using your favorite virtual environment
# ...

# Clone the repository and install in editable mode
git clone https://github.com/openvinotoolkit/anomalib.git
cd anomalib
pip install -e .

Training#

Anomalib supports both API and CLI-based training. The API is more flexible and allows for more customization, while the CLI training utilizes command line interfaces, and might be easier for those who would like to use anomalib off-the-shelf.

# Import the required modules
from anomalib.data import MVTec
from anomalib.models import Patchcore
from anomalib.engine import Engine

# Initialize the datamodule, model and engine
datamodule = MVTec()
model = Patchcore()
engine = Engine()

# Train the model
engine.fit(datamodule=datamodule, model=model)

# Get help about the training arguments, run:
anomalib train -h

# Train by using the default values.
anomalib train --model Patchcore --data anomalib.data.MVTec

# Train by overriding arguments.
anomalib train --model Patchcore --data anomalib.data.MVTec --data.category transistor

# Train by using a config file.
anomalib train --config <path/to/config>

Inference#

Anomalib 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.

Lightning Inference
# Assuming the datamodule, 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",
)

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

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

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

# Predict by using a config file.
anomalib predict --config <path/to/config> --return_predictions
Torch Inference
Python code here.

CLI command here.
OpenVINO Inference
Python code here.

CLI command here.
Gradio Inference
Python code here.

CLI command here.

Hyper-Parameter Optimization#

Anomalib supports hyper-parameter optimization using wandb and comet.ml. Here we show an example of hyper-parameter optimization using both comet and wandb.

# To perform hpo using wandb sweep
anomalib hpo --backend WANDB  --sweep_config tools/hpo/configs/wandb.yaml

# To perform hpo using comet.ml sweep
anomalib hpo --backend COMET  --sweep_config tools/hpo/configs/comet.yaml

# To be enabled in v1.1

Experiment Management#

Anomalib is integrated with various libraries for experiment tracking such as comet, tensorboard, and wandb through lighting loggers.

To run a training experiment with experiment tracking, you will need the following configuration file:

# Place the experiment management config here.

By using the configuration file above, you can run the experiment with the following command:

# Place the Experiment Management CLI command here.

# To be enabled in v1.1

Benchmarking#

Anomalib provides a benchmarking tool to evaluate the performance of the anomaly detection models on a given dataset. The benchmarking tool can be used to evaluate the performance of the models on a given dataset, or to compare the performance of multiple models on a given dataset.

Each model in anomalib is benchmarked on a set of datasets, and the results are available in src/anomalib/models/<model_name>README.md. For example, the MVTec AD results for the Patchcore model are available in the corresponding README.md file.

To run the benchmarking tool, run the following command:

anomalib benchmark --config tools/benchmarking/benchmark_params.yaml

# To be enabled in v1.1

Reference#

If you use this library and love it, use this to cite it:

@inproceedings{akcay2022anomalib,
  title={Anomalib: A deep learning library for anomaly detection},
  author={
    Akcay, Samet and
    Ameln, Dick and
    Vaidya, Ashwin and
    Lakshmanan, Barath
    and Ahuja, Nilesh
    and Genc, Utku
  },
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={1706--1710},
  year={2022},
  organization={IEEE}
}
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