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license: mit |
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--- |
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# Model Card for CLIP_COCO |
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## Model Description |
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- **Homepage:** https://imirandam.github.io/BiVLC_project_page/ |
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- **Repository:** https://github.com/IMirandaM/BiVLC |
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- **Paper:** https://arxiv.org/abs/2406.09952 |
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- **Point of Contact:** [Imanol Miranda](mailto:[email protected]) |
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### Model Summary |
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CLIP_COCO is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been fine-tuned with OpenCLIP framework using as basis the CLIP ViT-B-32 model pre-trained by 'openai'. The idea behind this fine-tuning is to have a baseline to compare the [CLIP_TROHN-Text](https://huggingface.co/imirandam/CLIP_TROHN-Text) and [CLIP_TROHN-Img](https://huggingface.co/imirandam/CLIP_TROHN-Img) models. Hyperparameters: |
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* Learning rate: 1e-6. |
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* Scheduler: Cosine scheduler with 50 warmup steps. |
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* Optimizer: AdamW optimizer with beta1 = 0.9, beta2 = 0.98, eps = 1e-6 and weight decay = 0.1. |
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* Loss function: InfoNCE Loss. |
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* Batch size: We define a batch size of 400, resulting in 400 images x 400 captions. |
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* Epochs: We fine-tune all models over 10 epochs and we used validation accuracy as the model selection criterion, i.e. we selected the model with the highest accuracy on the corresponding validation set. |
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* Data: It is fine-tuned with COCO 2017 train split. |
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### Evaluation Data |
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The model is evaluated in [BiVLC](https://huggingface.co/datasets/imirandam/BiVLC). |
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### Licensing Information |
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This work is licensed under a MIT License. |
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## Citation Information |
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If you find this dataset useful, please consider citing our paper: |
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``` |
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@misc{miranda2024bivlc, |
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title={BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval}, |
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author={Imanol Miranda and Ander Salaberria and Eneko Agirre and Gorka Azkune}, |
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year={2024}, |
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eprint={2406.09952}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
