The official repository for the paper "Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters"
APACHE-2.0 License
Isaac Corley1 · Caleb Robinson2 · Rahul Dodhia2 · Juan M. Lavista Ferres2 · Peyman Najafirad (Paul Rad)1
1University of Texas at San Antonio 2Microsoft AI for Good Research Lab
This is the official repository for the paper, "Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters" presented at the 2024 CVPR PBVS Workshop.
Remote sensing benchmark datasets, e.g. EuroSAT -- 64 x 64, commonly have small image sizes due to patches being extracted from large satellite tiles. However, we find that recently, evaluation is being performed at these small image sizes while being trained at larger image sizes.
Furthermore, we find that many pretrained geospatial foundation models are sensitive to the standard normalization used during inference. Blindly using ImageNet statistics can significantly degrade representation ability
Figure 3. t-SNE plots of EuroSAT test set embeddings extracted using a ResNet50 pretrained on ImageNet with different preprocessing. (left to right: 32 × 32 with normalization, 224 × 224 without normalization, 224 × 224 with normalization)
We have provided a sample script for extracting features using various models from the paper from your own folder of remote sensing imagery. Please modify the script to your use case (for best performance you will need the mean/std of your dataset). The extracted features will be saved to output_directory/model_features.npy
python embed.py --model resnet50_pretrained_moco --output-dir outputs --root path/to/your/folder --image-size 224 --batch-size 32 --workers 8 --device cuda:0
If this work inspired you to properly resize and normalize your images in benchmarking please consider citing our paper
@InProceedings{Corley_2024_CVPR,
author = {Corley, Isaac and Robinson, Caleb and Dodhia, Rahul and Ferres, Juan M. Lavista and Najafirad, Peyman},
title = {Revisiting Pre-trained Remote Sensing Model Benchmarks: Resizing and Normalization Matters},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2024},
pages = {3162-3172}
}