Model card for davit_base.msft_in1k

A DaViT image classification model. Trained on ImageNet-1k by paper authors.

Thanks to Fredo Guan for bringing the classification backbone to timm.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 88.0
  • GMACs: 15.5
  • Activations (M): 40.7
  • Image size: 224 x 224
• Papers:
  • DaViT: Dual Attention Vision Transformers: https://arxiv.org/abs/2204.03645
• Original: https://github.com/dingmyu/davit
• Dataset: ImageNet-1k

Model Usage

Image Classification

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('davit_base.msft_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)

Feature Map Extraction

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(
    'davit_base.msft_in1k',
    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

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(
    'davit_base.msft_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 (ie.e a (batch_size, num_features, H, W) tensor

output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor

Model Comparison

By Top-1

model	top1	top1_err	top5	top5_err	param_count	img_size	crop_pct	interpolation
davit_base.msft_in1k	84.634	15.366	97.014	2.986	87.95	224	0.95	bicubic
davit_small.msft_in1k	84.25	15.75	96.94	3.06	49.75	224	0.95	bicubic
davit_tiny.msft_in1k	82.676	17.324	96.276	3.724	28.36	224	0.95	bicubic

Citation

@inproceedings{ding2022davit,
    title={DaViT: Dual Attention Vision Transformer}, 
    author={Ding, Mingyu and Xiao, Bin and Codella, Noel and Luo, Ping and Wang, Jingdong and Yuan, Lu},
    booktitle={ECCV},
    year={2022},
}