timm / visformer_small.in1k

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Total runs: 22.8K
24-hour runs: 0
7-day runs: 887
30-day runs: 9.7K
Model's Last Updated: January 21 2025
image-classification

Introduction of visformer_small.in1k

Model Details of visformer_small.in1k

Model card for visformer_small.in1k

A Visformer image classification model. Trained on ImageNet-1k by https://github.com/hzhang57 and https://github.com/developer0hye .

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('visformer_small.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
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(
    'visformer_small.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, 768, 7, 7) 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 .

Citation
@inproceedings{chen2021visformer,
  title={Visformer: The vision-friendly transformer},
  author={Chen, Zhengsu and Xie, Lingxi and Niu, Jianwei and Liu, Xuefeng and Wei, Longhui and Tian, Qi},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={589--598},
  year={2021}
}

Runs of timm visformer_small.in1k on huggingface.co

22.8K
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0
24-hour runs
1.5K
3-day runs
887
7-day runs
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30-day runs

More Information About visformer_small.in1k huggingface.co Model

More visformer_small.in1k license Visit here:

https://choosealicense.com/licenses/apache-2.0

visformer_small.in1k huggingface.co

visformer_small.in1k huggingface.co is an AI model on huggingface.co that provides visformer_small.in1k's model effect (), which can be used instantly with this timm visformer_small.in1k model. huggingface.co supports a free trial of the visformer_small.in1k model, and also provides paid use of the visformer_small.in1k. Support call visformer_small.in1k model through api, including Node.js, Python, http.

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https://huggingface.co/timm/visformer_small.in1k

timm visformer_small.in1k online free

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timm visformer_small.in1k online free url in huggingface.co:

https://huggingface.co/timm/visformer_small.in1k

visformer_small.in1k install

visformer_small.in1k is an open source model from GitHub that offers a free installation service, and any user can find visformer_small.in1k on GitHub to install. At the same time, huggingface.co provides the effect of visformer_small.in1k install, users can directly use visformer_small.in1k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

visformer_small.in1k install url in huggingface.co:

https://huggingface.co/timm/visformer_small.in1k

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