timm / davit_small.msft_in1k

huggingface.co
Total runs: 4.0K
24-hour runs: 0
7-day runs: -24
30-day runs: -28.7K
Model's Last Updated: January 21 2025
image-classification

Introduction of davit_small.msft_in1k

Model Details of davit_small.msft_in1k

Model card for davit_small.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 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_small.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_small.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_small.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},
}

Runs of timm davit_small.msft_in1k on huggingface.co

4.0K
Total runs
0
24-hour runs
-153
3-day runs
-24
7-day runs
-28.7K
30-day runs

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davit_small.msft_in1k huggingface.co Url

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davit_small.msft_in1k install

davit_small.msft_in1k is an open source model from GitHub that offers a free installation service, and any user can find davit_small.msft_in1k on GitHub to install. At the same time, huggingface.co provides the effect of davit_small.msft_in1k install, users can directly use davit_small.msft_in1k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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