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timm/
hiera_small_abswin_256.sbb2_pd_e200_in12k

Name: hiera_small_abswin_256.sbb2_pd_e200_in12k
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Model card for hiera_small_abswin_256.sbb2_pd_e200_in12k

A Hiera image classification model w/ resizeable abs-win position embeddings and layer-scale. Trained on ImageNet-12k by Ross Wightman using "Searching for better ViT baselines" recipe. Patch dropout used during training using Hiera mask units, appeared to make pos embed more generalizable to other resolutions.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 42.7
- GMACs: 7.7
- Activations (M): 21.2
- Image size: 256 x 256
Dataset: ImageNet-12k
Papers:
- Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles: https://arxiv.org/abs/2306.00989
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- Window Attention is Bugged: How not to Interpolate Position Embeddings: https://arxiv.org/abs/2311.05613
Original: https://github.com/facebookresearch/hiera

Model Usage

Image Classification

python

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('hiera_small_abswin_256.sbb2_pd_e200_in12k', 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)

Feature Map Extraction

python

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(
    'hiera_small_abswin_256.sbb2_pd_e200_in12k',
    pretrained=True,
    features_only=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

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 96, 64, 64])
    #  torch.Size([1, 192, 32, 32])
    #  torch.Size([1, 384, 16, 16])
    #  torch.Size([1, 768, 8, 8])

    print(o.shape)

Image Embeddings

python

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(
    'hiera_small_abswin_256.sbb2_pd_e200_in12k',
    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, 64, 768) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

By Top-1

model	top1	top5	param_count
hiera_huge_224.mae_in1k_ft_in1k	86.834	98.01	672.78
hiera_large_224.mae_in1k_ft_in1k	86.042	97.648	213.74
hiera_base_plus_224.mae_in1k_ft_in1k	85.134	97.158	69.9
hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k	84.912	97.260	35.01
hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k	84.560	97.106	35.01
hiera_base_224.mae_in1k_ft_in1k	84.49	97.032	51.52
hiera_small_224.mae_in1k_ft_in1k	83.884	96.684	35.01
hiera_tiny_224.mae_in1k_ft_in1k	82.786	96.204	27.91

Citation

bibtex

@article{ryali2023hiera,
  title={Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles},
  author={Ryali, Chaitanya and Hu, Yuan-Ting and Bolya, Daniel and Wei, Chen and Fan, Haoqi and Huang, Po-Yao and Aggarwal, Vaibhav and Chowdhury, Arkabandhu and Poursaeed, Omid and Hoffman, Judy and Malik, Jitendra and Li, Yanghao and Feichtenhofer, Christoph},
  journal={ICML},
  year={2023}
}

bibtex

@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}}
}

bibtex

@article{bolya2023window,
  title={Window Attention is Bugged: How not to Interpolate Position Embeddings},
  author={Bolya, Daniel and Ryali, Chaitanya and Hoffman, Judy and Feichtenhofer, Christoph},
  journal={arXiv preprint arXiv:2311.05613},
  year={2023}
}

timm/hiera_small_abswin_256.sbb2_pd_e200_in12k

Model card for hiera_small_abswin_256.sbb2_pd_e200_in12k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

By Top-1

Citation

timm/
hiera_small_abswin_256.sbb2_pd_e200_in12k