---
tags:
- clip
- e-commerce
- fashion
- multimodal retrieval
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- Marqo/atlas
- Marqo/deepfashion-inshop
- Marqo/deepfashion-multimodal
- Marqo/fashion200k
- Marqo/iMaterialist
- Marqo/KAGL
- Marqo/polyvore
language:
- en
metrics:
- precision
- recall
- MRR
---
# Marqo FashionSigLIP Model Card
Marqo-FashionSigLIP leverages Generalised Contrastive Learning ([GCL](https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking)) which allows the model to be trained on not just text descriptions but also categories, style, colors, materials, keywords and fine-details to provide highly relevant search results on fashion products. 
The model was fine-tuned from ViT-B-16-SigLIP (webli). 

**Github Page**: [Marqo-FashionCLIP](https://github.com/marqo-ai/marqo-FashionCLIP)


## Usage
The model can be seamlessly used with [OpenCLIP](https://github.com/mlfoundations/open_clip) by

```python
import open_clip
model, _, _ = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP')
_, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('ViT-B-16-SigLIP', 'webli')
tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP')
```

## Benchmark Results
Average evaluation results on 6 public multimodal fashion datasets ([Atlas](https://huggingface.co/datasets/Marqo/atlas), [DeepFashion (In-shop)](https://huggingface.co/datasets/Marqo/deepfashion-inshop), [DeepFashion (Multimodal)](https://huggingface.co/datasets/Marqo/deepfashion-multimodal), [Fashion200k](https://huggingface.co/datasets/Marqo/fashion200k), [KAGL](https://huggingface.co/datasets/Marqo/KAGL), and [Polyvore](https://huggingface.co/datasets/Marqo/polyvore)) are reported below: 

**Text-To-Image (Averaged across 6 datasets)**
| Model                      | AvgRecall   | Recall@1   | Recall@10   | MRR       |
|----------------------------|-------------|------------|-------------|-----------|
| FashionCLIP2.0                | 0.163       | 0.077      | 0.249       | 0.165     |
| Marqo-FashionSigLIP        | **0.231**   | **0.121**  | **0.340**   | **0.239** |
| OpenFashionCLIP            | 0.132       | 0.060      | 0.204       | 0.135     |
| ViT-B-16-laion2b_s34b_b88k | 0.174       | 0.088      | 0.261       | 0.180     |
| ViT-B-16-SigLIP-webli      | 0.212       | 0.111      | 0.314       | 0.214     |

**Category-To-Product (Averaged across 5 datasets)**
| Model                      | AvgP      | P@1       | P@10      | MRR       |
|----------------------------|-----------|-----------|-----------|-----------|
| FashionCLIP2.0                | 0.684     | 0.681     | 0.686     | 0.741     |
| Marqo-FashionSigLIP        | **0.737** | **0.758** | **0.716** | **0.812** |
| OpenFashionCLIP            | 0.646     | 0.653     | 0.639     | 0.720     |
| ViT-B-16-laion2b_s34b_b88k | 0.662     | 0.673     | 0.652     | 0.743     |
| ViT-B-16-SigLIP-webli      | 0.688     | 0.690     | 0.685     | 0.751     |

**Sub-Category-To-Product (Averaged across 4 datasets)**
| Model                      | AvgP      | P@1       | P@10      | MRR       |
|----------------------------|-----------|-----------|-----------|-----------|
| FashionCLIP2.0                | 0.657     | 0.676     | 0.638     | 0.733     |
| Marqo-FashionSigLIP        | **0.725** | **0.767** | **0.683** | **0.811** |
| OpenFashionCLIP            | 0.598     | 0.619     | 0.578     | 0.689     |
| ViT-B-16-laion2b_s34b_b88k | 0.638     | 0.651     | 0.624     | 0.712     |
| ViT-B-16-SigLIP-webli      | 0.643     | 0.643     | 0.643     | 0.726     |