README.md

---
tags:
- image-classification
- timm
library_name: timm
license: cc-by-nc-4.0
datasets:
- imagenet-1k
---
# Model card for hiera_tiny_224.mae

A Hiera image feature model. Pretrained on ImageNet-1k with Self-Supervised Masked Autoencoder (MAE) method by paper authors.



## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 27.1
  - GMACs: 4.7
  - Activations (M): 14.6
  - Image size: 224 x 224
- **Dataset:** ImageNet-1k
- **Papers:**
  - Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles: https://arxiv.org/abs/2306.00989
  - Masked Autoencoders Are Scalable Vision Learners: https://arxiv.org/abs/2111.06377
- **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_tiny_224.mae', 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_tiny_224.mae',
    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, 56, 56])
    #  torch.Size([1, 192, 28, 28])
    #  torch.Size([1, 384, 14, 14])
    #  torch.Size([1, 768, 7, 7])

    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_tiny_224.mae',
    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, 49, 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
@Article{MaskedAutoencoders2021,
  author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{'a}r and Ross Girshick},
  journal = {arXiv:2111.06377},
  title   = {Masked Autoencoders Are Scalable Vision Learners},
  year    = {2021},
}
```