| Name | Link |
|---|---|
| FashionCLIP Feature Extraction and Classification | |
| Tutorial - FashionCLIP Evaluation with RecList | |
UPDATE (10/03/23): We have updated the model! We found that laion/CLIP-ViT-B-32-laion2B-s34B-b79K checkpoint (thanks Bin!) 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, 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.
We are now on Nature Scientific Reports!
@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 scalability and sustainability for certain types of players in the industry. Finally, we detail the costs and environmental impact of training, and release the model weights and code as open source contribution to the community.",
issn="2045-2322",
doi="10.1038/s41598-022-23052-9",
url="https://doi.org/10.1038/s41598-022-23052-9"
}
We are awaiting the official release of the Farfetch dataset, upon which fine-tuned model weights,
pre-processed image and text vectors will be made public. In the meanwhile, we currently use the
Hugging Face implementation of CLIP and can use the model weights
from OpenAI by following the standard hugginface
naming convention (i.e. fclip = FashionCLIP('<username>/<repo_name>', ... )). We also support private
repositories (i.e. fclip = FashionCLIP('<username>/<repo_name>', auth_token=<AUTH_TOKEN>, ... )).
See below for further details!
FashionCLIP is a CLIP-like model fine-tuned for the fashion industry. We fine tune
CLIP (Radford et al., 2021 on over 700K
<image, text> pairs from the Farfetch dataset1.
We evaluate FashionCLIP by applying it to open problems in industry such as retrieval, classification and fashion parsing. Our results demonstrate that fine-tuning helps capture domain-specific concepts and generalizes them in zero-shot scenarios. We also supplement quantitative tests with qualitative analyses, and offer preliminary insights into how concepts grounded in a visual space unlocks linguistic generalization. Please see our paper for more details.
In this repository, you will find an API for interacting with FashionCLIP and an interactive demo built using streamlit (coming soon!)
which showcases the capabilities of FashionCLIP.
Need a quick way to generate embeddings? do you want to test retrieval performance?
First of all, you should be able to quickly install this using pip.
$ pip install fashion-clip
If you have lists of texts and image paths, it is very easy to generate embeddings:
from fashion_clip.fashion_clip import FashionCLIP
fclip = FashionCLIP('fashion-clip')
# we create image embeddings and text embeddings
image_embeddings = fclip.encode_images(images, batch_size=32)
text_embeddings = fclip.encode_text(texts, batch_size=32)
# we normalize the embeddings to unit norm (so that we can use dot product instead of cosine similarity to do comparisons)
image_embeddings = image_embeddings/np.linalg.norm(image_embeddings, ord=2, axis=-1, keepdims=True)
text_embeddings = text_embeddings/np.linalg.norm(text_embeddings, ord=2, axis=-1, keepdims=True)Use our colab notebook to see more functionalities.
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("patrickjohncyh/fashion-clip")
processor = CLIPProcessor.from_pretrained("patrickjohncyh/fashion-clip")
image = Image.open("images/image1.jpg")
inputs = processor(text=["a photo of a red shoe", "a photo of a black shoe"],
images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)
print(probs)
image.resize((224, 224))From project root, install the fashion-clip package locally with
$ pip install -e .
There are two main abstractions to facilitate easy use of FashionCLIP.
First, the FCLIPDataset class which encapsulates information related to a given catalog
and exposes information critical for FashionCLIP. Additionally, it provides helper functions
for quick exploration and visualization of data. The main initialization parameters are
name: str -> Name of dataset
image_source_path: str -> absolute path to images (can be local or s3)
image_source_type: str -> type of source (i.e. local or s3)
catalog: List[dict] = None -> list of dicts containing at miniumum the keys ['id', 'image', 'caption']
For ease of use, the API also provides access to the dataset (once it is officialy released), used in the paper
for training FahionCLIP, by simply specifying the corresponding catalog name.
from fashion_clip import FCLIPDataset
dataset = FCLIPDataset(name='FF',
image_source_path='path/to/images',
image_source_type='local')
from fashion_clip import FCLIPDataset
my_catalog = [{'id': 1, 'image': 'x.jpg', 'caption': 'image x'}]
dataset = FCLIPDataset(name='my_dataset',
image_source_path='path/to/images',
image_source_type='local',
catalog=my_catalog)
The second abstraction is the FashionCLIP class, which takes in a Hugging Face CLIP model name and
an FCLIPDataset, and provides convenient functions to perform tasks such as multi-modal retrieval,
zero-shot classification and localization. The initialization parameters for FashionCLIP are as follows:
model_name: str -> Name of model OR path to local model
dataset: FCLIPDataset -> Dataset,
normalize: bool -> option to convert embeddings to unit norm
approx: bool -> option to use approximate nearest neighbors
Similar to the FCLIPDataset abstraction, we have included a pre-trained FashionCLIP model from the paper, hosted
here. If an unknown dataset and model combination is received,
the image and caption vectors will be generated upon object instantiation, otherwise pre-computed vectors/embeddings will
be pulled from S3.
from fashion_clip import FCLIPDataset, FashionCLIP
dataset = FCLIPDataset(name='FF',
image_source_path='path/to/images',
image_source_type='local')
fclip = FashionCLIP('fasihon-clip', ff_dataset)
For further details on how to use the package, refer to the accompanying notebook!
- Check RustEmbed for an application to use gRPC to create embeddings with FashionCLIP.
Footnotes
-
Pending official release. ↩