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--- |
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language: |
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- en |
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tags: |
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- multimodal |
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- language |
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- vision |
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- image-search |
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- pytorch |
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license: |
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- mit |
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metrics: |
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- MRR |
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--- |
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### Model Card: clip-imageclef |
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### Model Details |
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[OpenAI CLIP model](https://openai.com/blog/clip/) fine-tuned using image-caption pairs from the [Caption Prediction dataset](https://www.imageclef.org/2017/caption) provided for the ImageCLEF 2017 competition. The model was evaluated using before and after fine-tuning, MRR@10 were 0.57 and 0.88 respectively. |
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### Model Date |
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September 6, 2021 |
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### Model Type |
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The base model is the OpenAI CLIP model. It uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. |
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### Fine-tuning |
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The fine-tuning can be reproduced using code from the Github repository [elsevierlabs-os/clip-image-search](https://github.com/elsevierlabs-os/clip-image-search#fine-tuning). |
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### Usage |
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```python |
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from transformers import CLIPModel, CLIPProcessor |
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model = CLIPModel.from_pretrained("sujitpal/clip-imageclef") |
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processor = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") |
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inputs = processor(text=captions, images=images, |
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return_tensors="pt", padding=True) |
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output = model(**inputs) |
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``` |
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### Performance |
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| Model-name | k=1 | k=3 | k=5 | k=10 | k=20 | |
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| -------------------------------- | ----- | ----- | ----- | ----- | ----- | |
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| zero-shot CLIP (baseline) | 0.426 | 0.534 | 0.558 | 0.573 | 0.578 | |
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| clip-imageclef (this model) | 0.802 | 0.872 | 0.877 | 0.879 | 0.880 | |
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