Introduction of beit_base_patch16_224.in22k_ft_in22k_in1k
Model Details of beit_base_patch16_224.in22k_ft_in22k_in1k
Model card for beit_base_patch16_224.in22k_ft_in22k_in1k
A BEiT image classification model. Trained on ImageNet-22k with self-supervised masked image modelling (MIM) using a DALL-E dVAE as visual tokenizer. Fine-tuned on ImageNet-22k and then ImageNet-1k.
Model Details
Model Type:
Image classification / feature backbone
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('beit_base_patch16_224.in22k_ft_in22k_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
Image Embeddings
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'beit_base_patch16_224.in22k_ft_in22k_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
Model Comparison
Explore the dataset and runtime metrics of this model in timm
model results
.
Citation
@article{bao2021beit,
title={Beit: Bert pre-training of image transformers},
author={Bao, Hangbo and Dong, Li and Piao, Songhao and Wei, Furu},
journal={arXiv preprint arXiv:2106.08254},
year={2021}
}
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
Runs of timm beit_base_patch16_224.in22k_ft_in22k_in1k on huggingface.co
21.1K
Total runs
0
24-hour runs
1.2K
3-day runs
-400
7-day runs
5.6K
30-day runs
More Information About beit_base_patch16_224.in22k_ft_in22k_in1k huggingface.co Model
More beit_base_patch16_224.in22k_ft_in22k_in1k license Visit here:
beit_base_patch16_224.in22k_ft_in22k_in1k huggingface.co is an AI model on huggingface.co that provides beit_base_patch16_224.in22k_ft_in22k_in1k's model effect (), which can be used instantly with this timm beit_base_patch16_224.in22k_ft_in22k_in1k model. huggingface.co supports a free trial of the beit_base_patch16_224.in22k_ft_in22k_in1k model, and also provides paid use of the beit_base_patch16_224.in22k_ft_in22k_in1k. Support call beit_base_patch16_224.in22k_ft_in22k_in1k model through api, including Node.js, Python, http.
beit_base_patch16_224.in22k_ft_in22k_in1k huggingface.co is an online trial and call api platform, which integrates beit_base_patch16_224.in22k_ft_in22k_in1k's modeling effects, including api services, and provides a free online trial of beit_base_patch16_224.in22k_ft_in22k_in1k, you can try beit_base_patch16_224.in22k_ft_in22k_in1k online for free by clicking the link below.
timm beit_base_patch16_224.in22k_ft_in22k_in1k online free url in huggingface.co:
beit_base_patch16_224.in22k_ft_in22k_in1k is an open source model from GitHub that offers a free installation service, and any user can find beit_base_patch16_224.in22k_ft_in22k_in1k on GitHub to install. At the same time, huggingface.co provides the effect of beit_base_patch16_224.in22k_ft_in22k_in1k install, users can directly use beit_base_patch16_224.in22k_ft_in22k_in1k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
beit_base_patch16_224.in22k_ft_in22k_in1k install url in huggingface.co: