---
license: apache-2.0
tags:
  - vision
  - image-classification
  - remote-sensing
  - lora
  - peft
  - domain-adaptation
  - vision-transformer
  - continual-learning
datasets:
  - fmow
  - sentinel-2
pipeline_tag: image-classification
---

# ExPLoRA: Parameter-Efficient Extended Pre-Training

**[Paper](https://arxiv.org/abs/2406.10973)** | **[Code](https://github.com/samar-khanna/ExPLoRA)** | **[Website](https://samar-khanna.github.io/ExPLoRA/)** | **[Video](https://slideslive.com/39039614)**

This repository contains pre-trained checkpoints from the ICML 2025 paper:  
_"ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts"_

## Overview

ExPLoRA is a parameter-efficient method for adapting pre-trained Vision Transformers (ViT) to new domains using LoRA-based extended pre-training. Instead of training the full architecture, ExPLoRA freezes most of the backbone and trains low-rank adapters and a small subset of ViT blocks during self-supervised pre-training on target domain data.

<p align="center">
  <img src="https://samar-khanna.github.io/ExPLoRA/static/images/explora_arch.svg" width="600" style="background-color: white; padding: 10px; border-radius: 8px;"/>
</p>

---

## 📁 Checkpoints

> **Note:** All checkpoints have LoRA adapters **already merged** into the weights. The full checkpoints retain the separate `q_proj`, `k_proj`, `v_proj` layers (with merged LoRA) alongside the combined `qkv` weights for reference. The encoder-only checkpoints contain just the merged `qkv` weights, ready for downstream use.

### `explora_dinov2_fmow_rgb/`

ExPLoRA checkpoints using **DINOv2** self-supervised pre-training on fMoW high-resolution RGB satellite imagery.

| Description | ViT-B | ViT-L |
|-------------|:-----:|:-----:|
| DinoV2 teacher encoder & decoder weights + ExPLoRA adapters | [ViT-B/14](https://huggingface.co/samarkhanna/ExPLoRA/resolve/main/explora_dinov2_fmow_rgb/explora_dinov2_vit_base_fmow_rgb.pth) | [ViT-L/14](https://huggingface.co/samarkhanna/ExPLoRA/blob/main/explora_dinov2_fmow_rgb/explora_dinov2_vit_large_fmow_rgb.pth) |
| Encoder-only weights | [ViT-B/14](https://huggingface.co/samarkhanna/ExPLoRA/resolve/main/explora_dinov2_fmow_rgb/explora_dinov2_vit_base_fmow_rgb_encoder_only.pth) | [ViT-L/14](https://huggingface.co/samarkhanna/ExPLoRA/resolve/main/explora_dinov2_fmow_rgb/explora_dinov2_vit_large_fmow_rgb_encoder_only.pth) |

**Usage:**
```python
import torch

# Load encoder-only checkpoint (recommended for fine-tuning)
ckpt = torch.load("explora_dinov2_fmow_rgb/explora_dinov2_vit_large_fmow_rgb_encoder_only.pth", map_location="cpu")
state_dict = ckpt["model"]
```

### `explora_mae_multispectral/`

ExPLoRA checkpoints using **MAE** self-supervised pre-training on fMoW Sentinel-2 multispectral imagery.

| Description | ViT-L |
|-------------|:-----:|
| MAE encoder & decoder weights + ExPLoRA adapters | [ViT-L/16](https://huggingface.co/samarkhanna/ExPLoRA/resolve/main/explora_mae_multispectral/explora_mae_fmow_sentinel.pth) |
| Encoder-only weights | [ViT-L/16](https://huggingface.co/samarkhanna/ExPLoRA/resolve/main/explora_mae_multispectral/explora_mae_fmow_sentinel_encoder_only.pth) |

**Usage:**
```python
import torch

# Load encoder-only checkpoint (recommended for fine-tuning)
ckpt = torch.load("explora_mae_multispectral/explora_mae_fmow_sentinel_encoder_only.pth", map_location="cpu")
state_dict = ckpt["model"]
```

---

## Loading Checkpoints

These checkpoints are compatible with the [ExPLoRA codebase](https://github.com/samar-khanna/ExPLoRA). 

For **fine-tuning**, use the `finetune/finetune.py` script:
```bash
python finetune/finetune.py \
    --finetune path/to/explora_checkpoint.pth \
    --model vit_large_patch16 \
    --dataset_type rgb \
    ...
```

Reference scripts are also provided under `scripts/` in the codebase, and you can use these checkpoints there.

---

## Citation

If you find these checkpoints useful, please cite our paper:

```bibtex
@inproceedings{khanna2025explora,
  title={Ex{PL}o{RA}: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts},
  author={Samar Khanna and Medhanie Irgau and David B. Lobell and Stefano Ermon},
  booktitle={Forty-second International Conference on Machine Learning},
  year={2025},
  url={https://openreview.net/forum?id=OtxLhobhwb}
}
```

## License

Apache 2.0