README.md

For usage on SCC, first, create a conda environment using the following:

module load miniconda

conda create -n my_env -c conda-forge python=’version’

conda activate my_env

The 'sec_task3.ipynb' file is used to download the images for the primary task dataset by filtering the GBIF data based on the New England dataset.

Run the cells in this notebook to download the images and save it in the desired path.

The 'Mask_remove_text-3.ipynb' file is used to remove the labels from the image so that there is no bias influencing the classifier.

Add the following modules first(after creating a conda nevironment with python version > 3.9):

module load tensorflow/2.11.0

module load gcc/12.2.0

module load bazel/4.1.0

module load cuda/11.2

module load cudnn/8.1.1

(conda install module_name – for all other packages) Run the cells in this notebook to create a datset of the images with masked labels.

'IELT_data_prep.ipynb' is just used to convert the downloaded data to the format required by the IELT model(.tgz format)

To run the IELT model follow the following instructions:

!! NOTE THAT THIS MODEL HAS BEEN SETUP TO RUN ON THEIR CUB DATASET, NOT ON THE DOWNLOADED HERBARIUM DATASET !!

Requirements

python >= 3.9

pytorch >= 1.8.1

conda install -c conda-forge tqdm

conda install -c conda-forge timm

Training

1. Put the pre-trained ViT model in pretrained/, and rename it to ViT-B_16.npz, you can download from ViT pretrained.
2. Select a experiments setting file in configs/, and Modify the path of dataset.
3. Modify the path in setup.py in line 5, and you can change the log name and cuda visible by modify line 13,14.
4. Running the following code according to you pytorch version:

Single GPU

python -m main.py

Multiple GPUs

If pytorch < 1.12.0

python -m torch.distributed.launch --nproc_per_node 4 main.py 

If pytorch >= 1.12.0

torchrun --nproc_per_node 4 main.py

You need to change the number behind the -nproc_per_node to your number of GPUs