Update README.md

README.md

@@ -1,3 +1,104 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- Vision
+- Multi-model
+- Vision-Language
+- Remote-sensing
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# Git-RSCLIP-base
+
+[Git-RSCLIP](https://arxiv.org/pdf/2501.00895) is pre-trained on the Git-10M dataset (a global-scale remote sensing image-text pair dataset, consisting of 10 million image-text pairs) at size 256x256, first released in [this repository](https://github.com/chen-yang-liu/Text2Earth). It employs a similar structure to [[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224)]. 
+
+This is a **base version**, the **large version** is here: [[**Git-RSCLIP-large**](https://huggingface.co/lcybuaa/Git-RSCLIP)]
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot image classification and image-text retrieval.
+
+
+### How to use
+
+#### Use Git-RSCLIP to get image features
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+
+model = AutoModel.from_pretrained("lcybuaa/Git-RSCLIP")
+processor = AutoProcessor.from_pretrained("lcybuaa/Git-RSCLIP")
+
+url = "https://github.com/Chen-Yang-Liu/PromptCC/blob/main/Example/B/train_000051.png?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+  image_features = model.get_image_features(**inputs)
+```
+
+
+#### zero-shot image classification:
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+
+model = AutoModel.from_pretrained("lcybuaa/Git-RSCLIP")
+processor = AutoProcessor.from_pretrained("lcybuaa/Git-RSCLIP")
+
+url = "https://github.com/Chen-Yang-Liu/PromptCC/blob/main/Example/B/train_000051.png?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+
+texts = ["a remote sensing image of river", "a remote sensing image of houses and roads"]
+inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+logits_per_image = outputs.logits_per_image
+probs = torch.sigmoid(logits_per_image) # these are the probabilities
+top5_indices = torch.argsort(probs, descending=True)[:, :5].cpu().numpy()
+top1_indices = top5_indices[:, 0]
+print(f"the image 0 is '{top1_indices[0]}'")
+```
+
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+
+## Training procedure
+
+### Training data
+
+Git-RSCLIP is pre-trained on the Git-10M dataset (a global-scale remote sensing image-text pair dataset, consisting of 10 million image-text pairs) [(Liu et al., 2024)](https://github.com/chen-yang-liu/Text2Earth).
+
+### Preprocessing
+
+Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+Texts are tokenized and padded to the same length (64 tokens).
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{liu2025text2earthunlockingtextdrivenremote,
+      title={Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model},
+      author={Chenyang Liu and Keyan Chen and Rui Zhao and Zhengxia Zou and Zhenwei Shi},
+      year={2025},
+      eprint={2501.00895},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2501.00895},
+}
+```