# Model card for xcit_small_24_p16_384.fb_dist_in1k

A XCiT (Cross-Covariance Image Transformer) image classification model. Pretrained on ImageNet-1k with distillation by paper authors.

## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 47.7
  - GMACs: 26.7
  - Activations (M): 68.6
  - Image size: 384 x 384
- **Papers:**
  - XCiT: Cross-Covariance Image Transformers: https://arxiv.org/abs/2106.09681
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/facebookresearch/xcit

## 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('xcit_small_24_p16_384.fb_dist_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
```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(
    'xcit_small_24_p16_384.fb_dist_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, 577, 384) shaped tensor

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

## Citation
```bibtex
@article{el2021xcit,
  title={XCiT: Cross-Covariance Image Transformers},
  author={El-Nouby, Alaaeldin and Touvron, Hugo and Caron, Mathilde and Bojanowski, Piotr and Douze, Matthijs and Joulin, Armand and Laptev, Ivan and Neverova, Natalia and Synnaeve, Gabriel and Verbeek, Jakob and others},
  journal={arXiv preprint arXiv:2106.09681},
  year={2021}
}
```

{"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm"], "datasets": ["imagenet-1k"]}
timm pytorch safetensors image-classification dataset:imagenet-1k arxiv:2106.09681 license:apache-2.0 region:us
TAGS
#timm #pytorch #safetensors #image-classification #dataset-imagenet-1k #arxiv-2106.09681 #license-apache-2.0 #region-us 

# Model card for xcit_small_24_p16_384.fb_dist_in1k

A XCiT (Cross-Covariance Image Transformer) image classification model. Pretrained on ImageNet-1k with distillation by paper authors.

## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 47.7
  - GMACs: 26.7
  - Activations (M): 68.6
  - Image size: 384 x 384
- Papers:
  - XCiT: Cross-Covariance Image Transformers: URL
- Dataset: ImageNet-1k
- Original: URL

## Model Usage
### Image Classification


### Image Embeddings



# Model card for xcit_small_24_p16_384.fb_dist_in1k

A XCiT (Cross-Covariance Image Transformer) image classification model. Pretrained on ImageNet-1k with distillation by paper authors. ## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 47.7
  - GMACs: 26.7
  - Activations (M): 68.6
  - Image size: 384 x 384
- Papers:
  - XCiT: Cross-Covariance Image Transformers: URL
- Dataset: ImageNet-1k
- Original: URL ## Model Usage ### Image Classification ### Image Embeddings
TAGS
#timm #pytorch #safetensors #image-classification #dataset-imagenet-1k #arxiv-2106.09681 #license-apache-2.0 #region-us 
 # Model card for xcit_small_24_p16_384.fb_dist_in1k

A XCiT (Cross-Covariance Image Transformer) image classification model. Pretrained on ImageNet-1k with distillation by paper authors. ## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 47.7
  - GMACs: 26.7
  - Activations (M): 68.6
  - Image size: 384 x 384
- Papers:
  - XCiT: Cross-Covariance Image Transformers: URL
- Dataset: ImageNet-1k
- Original: URL ## Model Usage ### Image Classification ### Image Embeddings
TAGS
#timm #pytorch #safetensors #image-classification #dataset-imagenet-1k #arxiv-2106.09681 #license-apache-2.0 #region-us 
# Model card for xcit_small_24_p16_384.fb_dist_in1k

A XCiT (Cross-Covariance Image Transformer) image classification model. Pretrained on ImageNet-1k with distillation by paper authors.## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 47.7
  - GMACs: 26.7
  - Activations (M): 68.6
  - Image size: 384 x 384
- Papers:
  - XCiT: Cross-Covariance Image Transformers: URL
- Dataset: ImageNet-1k
- Original: URL## Model Usage### Image Classification### Image Embeddings