Installation

We offer two possible installation approaches for our DeepACSA software. The first option is to download the DeepACSA Installer. The second option we describe is DeepACSA package installation via Github. We want to inform you that there are more ways to install the package. However, we do not aim to be complete and rather demonstrate an (in our opinion) user friendly way for the installation of DeepACSA. Moreover, we advise users with less programming experience to make use of the first option and download the Installer.

Download the DeepACSA Installer

  1. Got to the Zenodo webpage containing the DeepACSA executable, the pre-trained models and the example files using this link.

  2. Download the DeepACSAv0.3.2_Installer folder and extract its content on your Desktop. If your system does not support .7z files natively, use a program such as 7-Zip to extract the contents.

  3. Navigate to C:/Users/your_user/Desktop/Downloads and install DeepACSA v0.3.2 by double clicking the DeepACSAv0.3.2_Installer.exe.

  4. Navigate to C:/Users/your_user/Desktop/DeepACSA_example_v0.3.2/. Start with the testing procedure to check that everything works properly.

DeepACSA Installation Guide (v0.3.2)

Warning

Version 0.3.2 is NOT YET available via pip. The newest release must be installed from GitHub.

Overview

DeepACSA can be installed using a Conda environment and a local editable installation. This is the recommended approach for both users and developers.

Step 1: Install Anaconda (required)

If you do not have Anaconda or Miniconda installed:

Step 3: Clone the DeepACSA Repository

Open a terminal (or Git Bash) and run:

git clone https://github.com/PaulRitsche/DeepACSA.git
cd DeepACSA

Step 4: Create the Conda Environment

DeepACSA provides an environment file with all dependencies:

conda env create -f environment.yml

This will create the environment (typically named DeepACSA0.3.2).

Step 5: Activate the Environment

conda activate DeepACSA0.3.2

Step 6: Install DeepACSA Locally

Install the package in editable mode:

python -m pip install -e .

This allows local development and updates.

Step 7: Verify Installation

conda list

Ensure that deepacsa appears in the list of installed packages.

Step 8: Run DeepACSA

Option 2: Run GUI script directly

cd DeepACSA
python deep_acsa_gui.py

GPU setup

The processing speed of a single image or video frame analyzed with DeepACSA is highly dependent on computing power. While possible, model inference and model training using a CPU only will decrese processing speed and prolong the model training process. Therefore, we advise to use a GPU whenever possible.

The following instructions are relevant only for Windows users!

Before using a GPU it needs to be properly configured. First, install the appropriate GPU drivers on your system. The correct drivers for your GPU can be found on the official NVIDIA website.

After installing the drivers, you must install the required CUDA and cuDNN software packages. To use DeepACSA with TensorFlow version 2.10, install:

You may need to create an NVIDIA account to access these downloads.

As a next step, you need to be your own installation wizard. We refer to this video (up to date, watch from minute 9 to minute 13) or this older video (watch the entire video, but substitute CUDA and cuDNN versions accordingly). There are procedures at the end of each video testing whether a GPU is detected by tensorflow or not.

The following instructions are relevant only for MacOS users!

If you are using an Apple Silicon device (M1 or M2) and would like to enable GPU acceleration for model training and/or inference, please refer to this tutorial. It provides a detailed guide on enabling GPU support for TensorFlow on Apple Silicon devices.

If you run into problems with the GPU/CUDA setup, please open a discussion in the Q&A section of DeepACSA discussions and assign the label “Problem”.