This repository will contain a modified and extended version of the avalanche framework which allow for sequence wise evaluation, class imbalanced rehearsal set selection, comprehensive logging, strategic memory filling. The commits made to the repository are designed such that they help understand the changes made to the original package.
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2 years ago | |
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avalanche | 2 years ago | |
data | 2 years ago | |
literature_notes | 2 years ago | |
results | 2 years ago | |
scripts | 2 years ago | |
.gitignore | 2 years ago | |
README.md | 2 years ago |
This Repository contains a folder called avalanche which is basically a fork from the avalanche framework at the 18.8.2021 (checkout bcba08cf4c
) by ContinualAI found here:
https://github.com/ContinualAI/avalanche
The camera trap data and the way it was handled is described. The corresponding jupyter notbook is found in scripts/jupyter-notebooks/data_stream_creation.ipynb The scripts/jupyter-notebooks/introduction_to_avalanche.ipynb was written to explain the avalanche framework and the extensions I made. Finally two scripts for evaluations were used: analyzing_results.ipynb & metric_results:dictionary.ipynb
After cloning the package, create a new envionrment with python 3.8:
conda create --name nameofenvironement python=3.8
Then activate the environment conda activate nameofenvironement
Next, navigate the avalanche folder containing the setup.py script. Use the following commad to get dependencies and install avalanche the environment in developer mode, meaning that changes made in the avalanche folder are seen by the environment: pip install -e .
Next, you need to install pytorch, torchvision and the appropriate cudatoolkit. Check your CUDA Version with nvidia-smi and then get the command here:
https://pytorch.org/get-started/locally/ (conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch)
Lastly, use this command to use the envionrment in a jupyter notebook:
python -m ipykernel install --user --name=nameofenvironement