RemoteCLIP🛰️: A Vision Language Foundation Model for Remote Sensing

Fan Liu (刘凡)✉ * ,     Delong Chen (陈德龙)✉ * ,     Zhangqingyun Guan (管张青云)

Xiaocong Zhou (周晓聪) ,     Jiale Zhu (朱佳乐) ,    

Qiaolin Ye (业巧林) ,     Liyong Fu (符利勇) ,     Jun Zhou (周峻)

                               

* Equal Contribution

News

• 2024/04/26: The training dataset of RemoteCLIP (RET-3, SEG-4, DET-10) is released on 🤗HuggingFace, see [gzqy1026/RemoteCLIP].

• 2024/04/03: Our RemoteCLIP paper has been accepted by IEEE Transactions on Geoscience and Remote Sensing (TGRS) [doi].

• 2024/03/01: RemoteCLIP joined the leaderboard on paperswithcode.com

• 2023/12/01: You can now auto-label remote sensing datasets with RemoteCLIP using the autodistill-remote-clip extension in the Autodistill framework, thanks James Gallagher from Roboflow!

• 2023/11/07: To facilitate reproducing RemoteCLIP's SOTA image-text retrieval results, we have prepared a retrieval.py script for retrieval evaluation on RSITMD, RSICD, and UCM datasets. Please see the Retrieval Evaluation section for details.

• 2023/07/27: We make pretrained checkpoints of RemoteCLIP models (ResNet-50, ViT-base-32, and ViT-large-14) available! We converted the weights to the OpenCLIP format, such that loading and using RemoteCLIP is extremely easy! Please see the Load RemoteCLIP section for details. We also provide a Jupyter Notebook demo.ipynb. You can also , thanks Dr. Gordon McDonald from the University of Sydney!

• 2023/06/19: We propose RemoteCLIP, the first vision-language foundation model for remote sensing. The preprint of our RemoteCLIP paper is available on arxiv [2306.11029].

Introduction

Welcome to the official repository of our paper "RemoteCLIP: A Vision Language Foundation Model for Remote Sensing"!

General-purpose foundation models have become increasingly important in the field of artificial intelligence. While self-supervised learning (SSL) and Masked Image Modeling (MIM) have led to promising results in building such foundation models for remote sensing, these models primarily learn low-level features, require annotated data for fine-tuning, and are not applicable for retrieval and zero-shot applications due to the lack of language understanding.

In response to these limitations, we propose RemoteCLIP, the first vision-language foundation model for remote sensing that aims to learn robust visual features with rich semantics, as well as aligned text embeddings for seamless downstream application. To address the scarcity of pre-training data, we leverage data scaling, converting heterogeneous annotations based on Box-to-Caption (B2C) and Mask-to-Box (M2B) conversion, and further incorporating UAV imagery, resulting in a 12xlarger pretraining dataset.

RemoteCLIP can be applied to a variety of downstream tasks, including zero-shot image classification, linear probing, k-NN classification, few-shot classification, image-text retrieval, and object counting. Evaluations on 16 datasets, including a newly introduced RemoteCount benchmark to test the object counting ability, show that RemoteCLIP consistently outperforms baseline foundation models across different model scales.

Impressively, RemoteCLIP outperforms previous SoTA by 9.14% mean recall on the RSICD dataset and by 8.92% on RSICD dataset. For zero-shot classification, our RemoteCLIP outperforms the CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets.

Load RemoteCLIP

RemoteCLIP is trained with the ITRA codebase, and we have converted the pretrained checkpoints to OpenCLIP compatible format and uploaded them to [this Huggingface Repo], such that accessing the model could be more convenient!

• To load RemoteCILP, please first prepare an environment with OpenCLIP installation, for example, by running this command:

  # https://pypi.org/project/open-clip-torch/
  pip install open-clip-torch

• Then, download the pretrained checkpoint from huggingface, you can clone the repo with Git LFS, or download it automatically via huggingface_hub:

  from huggingface_hub import hf_hub_download
  
  for model_name in ['RN50', 'ViT-B-32', 'ViT-L-14']:
      checkpoint_path = hf_hub_download("chendelong/RemoteCLIP", f"RemoteCLIP-{model_name}.pt", cache_dir='checkpoints')
      print(f'{model_name} is downloaded to {checkpoint_path}.')

• Now, you can initialize a CLIP model with OpenCLIP, then load the RemoteCLIP checkpoint with a few lines of code:

  import torch, open_clip
  from PIL import Image
  
  model_name = 'ViT-L-14' # 'RN50' or 'ViT-B-32' or 'ViT-L-14'
  model, _, preprocess = open_clip.create_model_and_transforms(model_name)
  tokenizer = open_clip.get_tokenizer(model_name)
  
  ckpt = torch.load(f"path/to/your/checkpoints/RemoteCLIP-{model_name}.pt", map_location="cpu")
  message = model.load_state_dict(ckpt)
  print(message)
  
  model = model.cuda().eval()

• The following is an example of text-to-image retrieval with RemoteCILP:

  text_queries = [
      "A busy airport with many airplanes.", 
      "Satellite view of Hohai University.", 
      "A building next to a lake.", 
      "Many people in a stadium.", 
      "a cute cat",
      ]
  text = tokenizer(text_queries)
  image = preprocess(Image.open("assets/airport.jpg")).unsqueeze(0)
  
  with torch.no_grad(), torch.cuda.amp.autocast():
      image_features = model.encode_image(image.cuda())
      text_features = model.encode_text(text.cuda())
      image_features /= image_features.norm(dim=-1, keepdim=True)
      text_features /= text_features.norm(dim=-1, keepdim=True)
  
      text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1).cpu().numpy()[0]
  
  print(f'Predictions of {model_name}:')
  for query, prob in zip(text_queries, text_probs):
      print(f"{query:<40} {prob * 100:5.1f}%")

  You could get the following outputs:

  Predictions of RN50:
  A busy airport with many airplanes.      100.0%
  Satellite view of Hohai University.        0.0%
  A building next to a lake.                 0.0%
  Many people in a stadium.                  0.0%
  a cute cat                                 0.0%
  

  

  You can run the above code in demo.ipynb, and you can also

Retrieval Evaluation

To perform cross-modal retrieval with RemoteCLIP, we extract image and text representations on the test split, perform L-2 normalization, and retrieval most similar samples based on the dot-product similarity measure. We show the retrieval recall of top-1 (R@1), top-5 (R@5), top-10 (R@10), and the mean recall of these values.

We have prepared a retrieval.py script to replicate the retrieval evaluation. Follow the steps below to evaluate the retrieval performance of RemoteCLIP on the RSITMD, RSICD, and UCM datasets:

• To run the retrieval evaluation, please first install additional dependencies: pip install clip_benchmark.

• Then download and extract image-text datasets RSITMD, RSICD, and UCM.

• Execute the following command to obtain image-to-text and text-to-image retrieval results:

  python retrieval.py \
    --model-name "ViT-B-32" \
    --retrieval-images-dir "/path/to/rsitmd/images" \
    --retrieval-json-dir "/path/to/dataset_rsitmd.json" \
    --remoteclip-path "/path/to/RemoteCLIP_ViT-B-32.pt"

Acknowledgments

• Thanks Wenwen Cai (蔡雯雯) for her efforts on the RemoteCount dataset.
• Thanks Dr. Gordon McDonald for making Jupyter Notebook available in Colab!
• Thanks James Gallagher for integrating RemoteCLIP into autodistill!

Citation

If you find this work useful, please cite our paper as:

@article{remoteclip,
  author       = {Fan Liu and
                  Delong Chen and
                  Zhangqingyun Guan and
                  Xiaocong Zhou and
                  Jiale Zhu and
                  Qiaolin Ye and
                  Liyong Fu and
                  Jun Zhou},
  title        = {RemoteCLIP: {A} Vision Language Foundation Model for Remote Sensing},
  journal      = {{IEEE} Transactions on Geoscience and Remote Sensing},
  volume       = {62},
  pages        = {1--16},
  year         = {2024},
  url          = {https://doi.org/10.1109/TGRS.2024.3390838},
  doi          = {10.1109/TGRS.2024.3390838},
}