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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
FashionCLIP is a CLIP-like model fine-tuned for the fashion domain.
| Date | Stars |
|---|---|
| 2026-07-24 | 529 |
| 2026-07-25 | 530 |
| 2026-07-28 | 530 |
| 2026-07-30 | 530 |
| 2026-07-31 | 531 |
| 2026-08-06 | 531 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.19%/day
# FashionCLIP
[](https://www.youtube.com/watch?v=uqRSc-KSA1Y)
[](https://huggingface.co/patrickjohncyh/fashion-clip)
[](https://colab.research.google.com/drive/1Z1hAxBnWjF76bEi9KQ6CMBBEmI_FVDrW?usp=sharing)
[](https://towardsdatascience.com/teaching-clip-some-fashion-3005ac3fdcc3)
[](https://huggingface.co/spaces/vinid/fashion-clip-app)
## Quick Start
| Name | Link |
|-----------------|----------|
| FashionCLIP Feature Extraction and Classification | [](https://colab.research.google.com/drive/1Z1hAxBnWjF76bEi9KQ6CMBBEmI_FVDrW?usp=sharing)|
| Tutorial - FashionCLIP Evaluation with RecList | [](https://colab.research.google.com/drive/1ek-TIT1ZJta59-O73GaXsOINvt46dnkz?usp=sharing)|
UPDATE (10/03/23): We have updated the model! We found that [laion/CLIP-ViT-B-32-laion2B-s34B-b79K](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K) checkpoint (thanks [Bin](https://www.linkedin.com/in/bin-duan-56205310/)!) worked better than original OpenAI CLIP on Fashion. We thus fine-tune a newer (and better!) version of FashionCLIP (henceforth FashionCLIP 2.0), while keeping the architecture the same. We postulate that the perofrmance gains afforded by `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` are due to the increased training data (5x OpenAI CLIP data). Our [thesis](https://www.nature.com/articles/s41598-022-23052-9), however, remains the same -- fine-tuning `laion/CLIP` on our fashion dataset improved zero-shot perofrmance across our benchmarks. See the below table comparing weighted macro F1 score across models.
`
| Model | FMNIST | KAGL | DEEP |
| ------------- | ------------- | ------------- | ------------- |
| OpenAI CLIP | 0.66 | 0.63 | 0.45 |
| FashionCLIP | 0.74 | 0.67 | 0.48 |
| Laion CLIP | 0.78 | 0.71 | 0.58 |
| FashionCLIP 2.0 | __0.83__ | __0.73__ | __0.62__ |
---
We are now on Hugging Face! The model is available [here](https://huggingface.co/patrickjohncyh/fashion-clip).
We are now on [Nature Scientific Reports](https://www.nature.com/articles/s41598-022-23052-9)!
## Citation
```
@Article{Chia2022,
title="Contrastive language and vision learning of general fashion concepts",
author="Chia, Patrick John
and Attanasio, Giuseppe
and Bianchi, Federico
and Terragni, Silvia
and Magalh{\~a}es, Ana Rita
and Goncalves, Diogo
and Greco, Ciro
and Tagliabue, Jacopo",
journal="Scientific Reports",
year="2022",
month="Nov",
day="08",
volume="12",
number="1",
pages="18958",
abstract="The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry. We demonstrate the effectiveness of the representations learned by FashionCLIP with extensive tests across a variety of tasks, datasets and generalization probes. We argue that adaptations of large pre-trained models such as CLIP offer new perspectives in terms of scalabiliExcerpt of 10,636 characters
Read on GitHub
Federico Bianchi · Together AI · United States
27
21
3
Louis Gauthier · CentraleSupélec · France
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d5b15696b8d0ce1d, topic:transformer
matched fp:d5b15696b8d0ce1d, topic:clip
matched fp:d5b15696b8d0ce1d, topic:nlp