# Model card for resmlp_12_224.fb_dino

A ResMLP image classification model. Pretrained with DINO (self-supervised) on ImageNet-1k by paper authors. No classifier.

## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 15.4
  - GMACs: 3.0
  - Activations (M): 5.5
  - Image size: 224 x 224
- **Papers:**
  - ResMLP:  Feedforward networks for image classification with data-efficient training: https://arxiv.org/abs/2105.03404
- **Original:** https://github.com/facebookresearch/deit
- **Dataset:** ImageNet-1k

## 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('resmlp_12_224.fb_dino', 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
```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(
    'resmlp_12_224.fb_dino',
    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, 196, 384) 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](https://github.com/huggingface/pytorch-image-models/tree/main/results).

## Citation
```bibtex
@article{touvron2021resmlp,
  title={ResMLP: Feedforward networks for image classification with data-efficient training},
  author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and Edouard Grave and Gautier Izacard and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Herv'e J'egou},
  journal={arXiv preprint arXiv:2105.03404},
  year={2021},
}
```
```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}}
}
```

{"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm"], "datasets": ["imagenet-1k"]}
timm pytorch safetensors image-classification dataset:imagenet-1k arxiv:2105.03404 license:apache-2.0 region:us
TAGS
#timm #pytorch #safetensors #image-classification #dataset-imagenet-1k #arxiv-2105.03404 #license-apache-2.0 #region-us 

# Model card for resmlp_12_224.fb_dino

A ResMLP image classification model. Pretrained with DINO (self-supervised) on ImageNet-1k by paper authors. No classifier.

## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 15.4
  - GMACs: 3.0
  - Activations (M): 5.5
  - Image size: 224 x 224
- Papers:
  - ResMLP:  Feedforward networks for image classification with data-efficient training: URL
- Original: URL
- Dataset: ImageNet-1k

## Model Usage
### Image Classification


### Image Embeddings


## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.


# Model card for resmlp_12_224.fb_dino

A ResMLP image classification model. Pretrained with DINO (self-supervised) on ImageNet-1k by paper authors. No classifier. ## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 15.4
  - GMACs: 3.0
  - Activations (M): 5.5
  - Image size: 224 x 224
- Papers:
  - ResMLP:  Feedforward networks for image classification with data-efficient training: URL
- Original: URL
- Dataset: ImageNet-1k ## Model Usage ### Image Classification ### Image Embeddings ## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.
TAGS
#timm #pytorch #safetensors #image-classification #dataset-imagenet-1k #arxiv-2105.03404 #license-apache-2.0 #region-us 
 # Model card for resmlp_12_224.fb_dino

A ResMLP image classification model. Pretrained with DINO (self-supervised) on ImageNet-1k by paper authors. No classifier. ## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 15.4
  - GMACs: 3.0
  - Activations (M): 5.5
  - Image size: 224 x 224
- Papers:
  - ResMLP:  Feedforward networks for image classification with data-efficient training: URL
- Original: URL
- Dataset: ImageNet-1k ## Model Usage ### Image Classification ### Image Embeddings ## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.