Sinapsis Anomaly Detection

Monorepo with packages to provide anomaly detection training, inference and export for computer vision.

🐍 Installation • 📦 Packages • 🌐 Webapp • 📙 Documentation • 🔍 License

🐍 Installation

This monorepo currently consists of the following packages for anomaly detection:

• sinapsis-anomalib

Install using your package manager of choice. We encourage the use of uv

Example with uv:

  uv pip install sinapsis-anomalib --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-anomalib --extra-index-url https://pypi.sinapsis.tech

Important

Templates in each package may require extra dependencies. For development, we recommend installing the package with all the optional dependencies:

with uv:

  uv pip install sinapsis-anomalib[all] --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-anomalib[all] --extra-index-url https://pypi.sinapsis.tech

Tip

You can also install all the packages within this project:

  uv pip install sinapsis-anomaly-detection[all] --extra-index-url https://pypi.sinapsis.tech

📦 Packages

Packages summary

• Sinapsis Anomalib
  • AnomalibTorchInference
    Run anomaly detection inference using PyTorch models.
  • AnomalibOpenVINOInference
    Perform optimized inference using OpenVINO-accelerated models.
  • AnomalibTrain
    Train custom anomaly detection models with Anomalib.
  • AnomalibExport
    Export trained models for deployment in different formats.

Tip

Use CLI command sinapsis info --all-template-names to show a list with all the available Template names installed with Sinapsis Anomaly Detection.

Tip

Use CLI command sinapsis info --example-template-config TEMPLATE_NAME to produce an example Agent config for the Template specified in TEMPLATE_NAME.

For example, for AnomalibTorchInference use sinapsis info --example-template-config AnomalibTorchInference to produce the following example config:

agent:
  name: my_test_agent
templates:
- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}
- template_name: AnomalibTorchInference
  class_name: AnomalibTorchInference
  template_input: InputTemplate
  attributes:
    model_path: '/path/to/model.pt'
    transforms: null
    device: cuda

🌐 Webapp

The webapp offers an interface for anomaly detection on images using pretrained models. Upload images and visualize results (labels, bboxes, or masks) based on the provided agent configuration.

Important

To run the app you first need to clone this repository:

git clone [email protected]:Sinapsis-ai/sinapsis-anomaly-detection.git
cd sinapsis-anomaly-detection

Note

If you'd like to enable external app sharing in Gradio, export GRADIO_SHARE_APP=True

Note

Model training is performed when starting the webapp if an exported model does not exist in the MODEL_PATH location.

🐳 Docker

IMPORTANT This docker image depends on the sinapsis-nvidia:base image. Please refer to the official sinapsis instructions to Build with Docker.

1. Build the sinapsis-anomalib image:

docker compose -f docker/compose.yaml build

2. Start the app container:

docker compose -f docker/compose_apps.yaml up sinapsis-anomalib-gradio -d

3. Check the status:

docker logs -f sinapsis-anomalib-gradio

3. The logs will display the URL to access the webapp, e.g.:

NOTE: The url can be different, check the output of the logs

Running on local URL:  http://127.0.0.1:7860

4. To stop the app:

docker compose -f docker/compose_apps.yaml down

Webapp Configuration

Customize the webapp behavior by updating the environment fields in docker/compose_apps.yaml:

For custom inference agent:

AGENT_CONFIG_PATH: "/app/configs/inference/custom_torch_demo_agent.yml"

For custom training agent:

TRAINING_CONFIG: "/app/configs/custom_train_export_agent.yaml"

For custom inference model path:

MODEL_PATH: "/app/artifacts/exported_models/weights/torch/custom_model.pt"

For custom test data:

TEST_DIR: "/app/artifacts/data/custom_test_data"

💻 UV

To run the webapp using the uv package manager, please:

1. Create the virtual environment and sync the dependencies:

uv sync --frozen

2. Install the wheel:

uv pip install sinapsis-anomaly-detection[all] --extra-index-url https://pypi.sinapsis.tech

3. Run the webapp:

uv run webapps/anomalib_gradio_app.py

4. The terminal will display the URL to access the webapp, e.g.:

NOTE: The url can be different, check the output of the terminal

Running on local URL:  http://127.0.0.1:7860

Webapp Configuration

Customize the webapp behavior by exporting the following variables with your custom values before running the app:

For custom inference agent:

export AGENT_CONFIG_PATH="packages/sinapsis_anomalib/src/sinapsis_anomalib/configs/inference/custom_torch_demo_agent.yml"

For custom training agent:

export TRAINING_CONFIG="packages/sinapsis_anomalib/src/sinapsis_anomalib/configs/custom_train_export_agent.yaml"

For custom inference model path:

export MODEL_PATH="artifacts/exported_models/weights/torch/custom_model.pt"

For custom test data:

export TEST_DIR="artifacts/data/custom_test_data"

📙 Documentation

Documentation for this and other sinapsis packages is available on the sinapsis website

Tutorials for different projects within sinapsis are available at sinapsis tutorials page

🔍 License

This project is licensed under the AGPLv3 license, which encourages open collaboration and sharing. For more details, please refer to the LICENSE file.

For commercial use, please refer to our official Sinapsis website for information on obtaining a commercial license.