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Installation

In our experiments, we used the following setup:

  • Ubuntu 20.04.2 LTS
  • Python 3.6.11
  • CUDA 10.2
  • Pytorch 1.5.1

In order to reimplement our development environment, pelase, follow the below-mentioned instructions.

I. Setup Code Environment

NOTE: In case you have multiple CUDA versions installed, please, make sure to initialise the appropriate system CUDA version before running any command.

# <xx.x> - CUDA version number
module load cuda-xx.x

  1. Setup a conda environment:

    • With Conda [Recommended]:
    # Create a conda environment with dependencies from the environment.yml file
conda env create --name smvit -f environment.yml
# Activate the environment
conda activate smvit
    • With PIP [Not Tested]:
    # Create a conda environment
conda create -n smvit python=3.6.11
# Activate the environment
conda activate smvit
# Install dependencies from the requirements.txt file
pip install -r requirements.txt

  2. Install the Apex library for mixed-precision training:

    • With conda [Our choice]:
    ## Both commands are necessary
# May throw warnings, but it is okay
conda install -c conda-forge nvidia-apex
# Answer 'Yes' when numpy package upgrade is inquired
conda install -c conda-forge nvidia-apex=0.1
    • From source [Recommended]:
    git clone https://github.com/NVIDIA/apex
cd apex
# May throw unexpected system-specific errors
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

  3. [Additional] In case of runtime errors related to numpy or scikit-learn packages, force downgrade numpy to the '1.15.4' version:

    pip install numpy==1.15.4

II. Download Pre-trained Models

U2-Net:

NOTE: This model is required for every experiment (training from scratch, fine-tuning, inference).

Download the pre-trained model from the U2-Net author's Google Drive link ("u2net.pth" - 176.3 MB).
P.S. Original shared link was taken from here.

Place the model in the following path:

sm-vit/
|–– U2Net/
|   |–– model/
|   |   |–– pre_trained/
|   |   |   |–– u2net.pth

SM-ViT (ours):

NOTE: This model is required for fine-tuning and inference.

Download a pre-trained model for the dataset of interest from our moodel zoo.

Place the model in the following path:

sm-vit/
|–– models/
|   |–– pre_trained/
|   |   |–– <model_name>.bin

Vanilla ViT-B/16:

NOTE: This model is required for training from scratch only.

Download the pre-trained model from the ViT author's Google Cloud link ("ViT-B_16.npz" - 393.7 MB).
P.S. Original shared link was taken from here.

wget https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz

Place the model in the following path:

sm-vit/
|–– models/
|   |–– pre_trained/
|   |   |–– ViT-B_16.npz