Update README.md

README.md

@@ -2,9 +2,55 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+license: mit
+datasets:
+- carolineec/CyclePrefDB-I2T
+language:
+- en
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# Model Card for CycleReward-I2T
+
+[Project page](https://cyclereward.github.io) | [Paper](https://huggingface.co/papers/2506.02095) | [Code](https://github.com/hjbahng/cyclereward)
+
+Reward model for image-text alignment trained on image-to-text comparison pairs from [CyclePrefDB-I2T](https://huggingface.co/datasets/carolineec/CyclePrefDB-I2T).
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
+
+
+## Loading the model
+
+Download the `model.py`, `med_config.json` files and `blip` folder from this repository. You can load the pretrained model using the code below:
+
+
+```
+import torch
+from PIL import Image
+from model import CycleReward
+
+device='cuda'
+model = CycleReward.from_pretrained("carolineec/CycleReward-I2T")
+model.to(device)
+model.eval()
+
+preprocess = model.preprocess
+image_path = "cat.jpg"
+caption = "a photo of a cat"
+image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
+print('prepared data')
+
+score = model.score(image, caption) 
+print('my score:', score.item())
+
+```
+
+## Citation
+
+```
+@article{bahng2025cyclereward,
+title={Cycle Consistency as Reward: Learning Image-Text Alignment without Human Preferences},
+author= {Bahng, Hyojin and Chan, Caroline and Durand, Fredo and Isola, Phillip},
+journal={arXiv preprint arXiv:2506.02095},
+year={2025}
+}
+```