FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding

Paper: FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding Code: https://github.com/NJU-LHRS/FarSLIP

Introduction

We introduce FarSLIP, a vision-language foundation model for remote sensing (RS) that achieves fine-grained vision-language alignment. FarSLIP demonstrates state-of-the-art performance on both fine-grained and image-level tasks, including open-vocabulary semantic segmentation, zero-shot classification, and image-text retrieval. We also construct MGRS-200k, the first multi-granularity image-text dataset for RS. Each image is annotated with both short and long global-level captions, along with multiple object-category pairs.

Checkpoints

You can download all our checkpoints from Huggingface, or selectively download them through the links below.

Model name	Architecture	OVSS mIoU (%)	ZSC top-1 accuracy (%)	Download
FarSLIP-s1	ViT-B-32	29.87	58.64	FarSLIP1_ViT-B-32
FarSLIP-s2	ViT-B-32	30.49	60.12	FarSLIP2_ViT-B-32
FarSLIP-s1	ViT-B-16	35.44	61.89	FarSLIP1_ViT-B-16
FarSLIP-s2	ViT-B-16	35.41	62.24	FarSLIP2_ViT-B-16

Dataset

FarSLIP is trained in two stages.

In the first stage, we use the RS5M dataset. A quick portal to the RS5M dataset: link.
In the second stage, we use the proposed MGRS-200k dataset, which is available on Huggingface.

Examples from MGRS-200k

Usage / Testing

Below is a sample usage for zero-shot scene classification, taken directly from the official GitHub repository.

Zero-shot scene classification

Please refer to SkyScript for scene classification dataset preparation, including 'SkyScript_cls', 'aid', 'eurosat', 'fmow', 'millionaid', 'patternnet', 'rsicb', 'nwpu'.
Replace the BENCHMARK_DATASET_ROOT_DIR in tests/test_scene_classification.py to your own path.
Run testing (e.g. FarSLIP-s1 with ViT-B-32):

python -m tests.test_scene_classification --model-arch ViT-B-32 --model-name FarSLIP1 --force-quick-gelu --pretrained checkpoints/FarSLIP1_ViT-B-32.pt

*Comparison of zero-shot classification accuracies (Top-1 acc., %) of different RS-specific CLIP variants across multiple benchmarks.*

Citation

If you find our work is useful, please give us ⭐ in GitHub and consider cite our paper:

@article{li2025farslip,
title={FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding},
author={Zhenshi Li and Weikang Yu and Dilxat Muhtar and Xueliang Zhang and Pengfeng Xiao and Pedram Ghamisi and Xiao Xiang Zhu},
journal={arXiv preprint arXiv:2511.14901},
year={2025}
}

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