license: apache-2.0
tags:
- vision
datasets:
- imagenet-1k
Vision Transformer (large sized model) pre-trained with MSN (patch size of 7)
Vision Transformer (ViT) model pre-trained using the MSN method. It was introduced in the paper Masked Siamese Networks for Label-Efficient Learning by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas and first released in this repository.
Disclaimer: The team releasing MSN did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like). Images are presented to the model as a sequence of fixed-size patches.
MSN presents a joint-embedding architecture to match the prototypes of masked patches with that of the unmasked patches. With this setup, their method yields excellent performance in the low-shot and extreme low-shot regimes.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder.
Intended uses & limitations
You can use the raw model for downstream tasks like image classification. See the model hub to look for different versions of MSN pre-trained models that interest you. The model is particularly beneficial when you have a few labeled samples in your training set.
How to use
Here is how to use this backbone encoder:
from transformers import AutoFeatureExtractor, ViTMSNModel
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-msn-large-7")
model = ViTMSNModel.from_pretrained("facebook/vit-msn-large-7")
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
For fine-tuning on image classification use the ViTMSNForImageClassification class:
from transformers import AutoFeatureExtractor, ViTMSNForImageClassification
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-msn-large-7")
model = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-large-7")
...
Citation
@article{assran2022masked,
title={Masked Siamese Networks for Label-Efficient Learning},
author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas},
journal={arXiv preprint arXiv:2204.07141},
year={2022}
}