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CaiT

  • Paper：Going deeper with Image Transformers

  • Origin Repo：facebookresearch/deit

  • Code：cait.py

  • Evaluate Transforms：

    # backend: pil
# input_size: 224x224
transforms = T.Compose([
    T.Resize(248, interpolation='bicubic'),
    T.CenterCrop(224),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# backend: pil
# input_size: 384x384
transforms = T.Compose([
    T.Resize(384, interpolation='bicubic'),
    T.CenterCrop(384),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# backend: pil
# input_size: 448x448
transforms = T.Compose([
    T.Resize(448, interpolation='bicubic'),
    T.CenterCrop(448),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

  • Model Details：

    Model Model Name Params (M) FLOPs (G) Top-1 (%) Top-5 (%) Pretrained Model
    CaiT-xxs-24 cait_xxs_24 12.0 2.5 78.50 94.30 Download
    CaiT-xxs-36 cait_xxs_36 17.3 3.8 79.87 94.90 Download
    CaiT-s-24 cait_s_24 49.6 9.4 83.40 96.62 Download
    CaiT-xxs-24-384 cait_xxs_24_384 12.0 9.5 80.97 95.64 Download
    CaiT-xxs-36-384 cait_xxs_36_384 17.3 14.2 82.20 96.15 Download
    CaiT-xs-24-384 cait_xs_24_384 26.6 19.3 84.06 96.89 Download
    CaiT-s-24-384 cait_s_24_384 49.6 32.2 85.05 97.34 Download
    CaiT-s-36-384 cait_s_36_384 68.2 48.0 85.45 97.48 Download
    CaiT-m-36-384 cait_m_36_384 270.9 173.3 86.06 97.73 Download
    CaiT-m-48-448 cait_m_48_448 356.0 329.6 86.49 97.75 Download

  • Citation：

    @article{touvron2021cait,
title={Going deeper with Image Transformers},
author={Hugo Touvron and Matthieu Cord and Alexandre Sablayrolles and Gabriel Synnaeve and Herv'e J'egou},
journal={arXiv preprint arXiv:2103.17239},
year={2021}
}