AgarVision is a machine learning-powered tool designed to automate the counting of colonies on agar plates. This project aims to assist microbiologists and researchers by providing accurate, efficient, and reproducible colony counts, leveraging custom-trained models and a user-friendly interface.

• Automatic Colony Detection: Utilizes state-of-the-art machine learning models to detect and count colonies with high accuracy.
• Streamlit Interface: Offers an interactive UI for easy visualization and manipulation of results.
• Customizable: Supports training on custom datasets to improve accuracy specific to various colony types and agar backgrounds.

Ensure you have Python 3.8 or later installed on your machine. You can download it from Python's official website.

git clone https://github.com/001TMF/AgarVision.git
cd AgarVision

pip install -r requirements.txt

or

To create a Conda environment with all the necessary dependencies, follow these steps:

1. Clone the BindRMSD repository to your local machine:

   git clone https://github.com/001TMF/AgarVision.git
   cd AgarVision

2. Create the Conda environment from the environment.yaml file:

   conda env create -f environment.yaml #not yet finished 

3. Activate the environment:

   conda activate AgarVision

To use AgarVision, follow these instructions:

# Launch the Streamlit Application and witness the magic of AgarVision
streamlit run streamlit/streamlit_ui.py

# Train the model on new data like a true data whisperer
python scripts/train.py

# Evaluate the model’s performance and pray for good metrics
python scripts/validate.py

Let’s be real—our current model identifies colonies like a toddler identifies fine art. But fear not! We're training a new, robust version on an exciting dataset that promises to significantly improve its performance. Coming soon to a petri dish near you!

Contributions to BindRMSD are welcome and appreciated. To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.