This is the implementation for CVPR 2023 paper Change-Aware Sampling and Contrastive Learning for Satellite Images.
Authors: Utkarsh Mall, Bharath Hariharan, Kavita Bala
This codebase is built with and tested with python3.9, and torch 1.7.1. We highly recommend creating environment using anaconda.
conda create --name cacoenv python=3.9.13 -y After activating the environment:
conda activate cacoenv
Install the following packages:
conda install cudatoolkit=11.0 -y
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
pip install matplotlib pytorch-lightning==1.1.8 pytorch-lightning-bolts==0.3.0 scikit-learn rasterio lmdb pandas
pip installjupyter progressbar opencv-python
Optionally also install
pip install wandb gym
If running into error due to some library mismatch, please refer to requirements.txt for the correct versions.
Use the notebook for quick inference of on our pretrained model.
cd notebooks python -m jupyter notebook --ip 0.0.0.0 --no-browser
The notebook also provides instructions on how to download pretrained models.
The pretrained models can be found at this link
In order to retrain the model, we need to download the CACo data and then run training.
A sample training procedure is provided in sample_training.sh
bash sample_training.sh
The data can be found at this link
Refer to the sample_training.sh for downloading and placing data.
To train a resnet-18 model with 10k data and caco loss run the following
python3 main_pretrain.py --data_dir ../data/clean_10k_geography/ --base_encoder resnet18 --batch_size 256 --data_mode caco --max_epochs 1000 --schedule 600 800 -d gereric_description
- Code to download custom dataset using CACo sampling.
- Setup for evaluating on other datasets, BigEarthNet, DynmamicEarthNet, OSCD, and Change Events.
- 1m training set.
This repository follows the stucture from SeCo and uses same versions of libraries used.