Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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