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timm/
twins_pcpvt_small.in1k

Name: twins_pcpvt_small.in1k
Rating: 2.5 (452 reviews)

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Model card for twins_pcpvt_small.in1k

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

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 24.1
- GMACs: 3.8
- Activations (M): 18.1
- Image size: 224 x 224
Papers:
- Twins: Revisiting the Design of Spatial Attention in Vision Transformers: https://arxiv.org/abs/2104.13840
Dataset: ImageNet-1k
Original: https://github.com/Meituan-AutoML/Twins

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('twins_pcpvt_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

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(
    'twins_pcpvt_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, 49, 512) 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

bibtex

@inproceedings{chu2021Twins,
    title={Twins: Revisiting the Design of Spatial Attention in Vision Transformers},
    author={Xiangxiang Chu and Zhi Tian and Yuqing Wang and Bo Zhang and Haibing Ren and Xiaolin Wei and Huaxia Xia and Chunhua Shen},
    booktitle={NeurIPS 2021},
    url={https://openreview.net/forum?id=5kTlVBkzSRx},
    year={2021}
}

timm/twins_pcpvt_small.in1k