BetterSquirrelDetector🐿️

Welcome to the BetterSquirrelDetector repository! This repository contains scripts, data, and models that accompany a series of blog posts on data-centric AI and active learning. It builds upon the original SquirrelDetector project.

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Table of Contents

• Table of Contents
• Repository Structure
• Getting Started
• Contributing
• Collaborators

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Repository Structure

The repository is organized as follows:

• .dvc: Contains DVC files for tracking data and models.
• .labelstudio: Contains LabelStudio files for validation and test set images.
• annotations: Includes validation and test datasets.
• data: Data related to the project.
• models: Pretrained models for squirrel detection.
• src: Source code for various components of the project.

Inside the src folder:

• data: Scripts related to data handling and preparation.
• webserver: Code for the web server component.
• Dockerfile: Dockerfile for creating a containerized environment.
• _wsgi.py: Code for running a web server serving models from the MLflow Model Registry.
• docker-compose.yml: Configuration for Docker Compose.
• get_or_create_mlflow_experiment.py: Script for creating MLflow experiments.
• ls_model_server.py: Code for updating Label Studio API endpoint.
• model_wrapper.py: Wrapper for MLflow model registration.
• register_model.py: Script for registering the model wrapper.
• train_squirrel_detector.py: Script for training the squirrel detector.
• upload_model.py: Script for uploading models.

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Getting Started

To get started with this project, follow these steps:

1. Clone the repository:

git clone https://dagshub.com/yonomitt/BetterSquirrelDetector.git

2. To use MLflow Tracking:

MLFLOW_TRACKING_URI=https://dagshub.com/yonomitt/BetterSquirrelDetector.mlflow \
MLFLOW_TRACKING_USERNAME=your_username \
MLFLOW_TRACKING_PASSWORD=your_token \
python script.py

Contributing

Contributions to this project are welcome. To contribute, please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a feature branch.
3. Make your changes.
4. Submit a pull request.

Please ensure your code adheres to the project's coding guidelines.

Collaborators

• Yono Mittlefehldt
• Dean Pleban
• Anna Hyatt

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Thank You for visiting the BetterSquirrelDetector repository! We hope you find this project interesting and valuable. Happy coding! 💻🐿️