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PiT

  • Paper：Rethinking Spatial Dimensions of Vision Transformers

  • Origin Repo：naver-ai/pit

  • Code：pit.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])
])

  • Model Details：

    Model Model Name Params (M) FLOPs (G) Top-1 (%) Top-5 (%) Pretrained Model
    PiT-Ti pit_ti 4.9 0.7 72.91 91.40 Download
    PiT-XS pit_xs 10.6 1.4 78.18 94.16 Download
    PiT-S pit_s 23.5 2.9 81.08 95.33 Download
    PiT-B pit_b 73.8 12.5 82.44 95.71 Download
    PiT-Ti distilled pit_ti_distilled 4.9 0.7 74.54 92.10 Download
    PiT-XS distilled pit_xs_distilled 10.6 1.4 79.31 94.36 Download
    PiT-S distilled pit_s_distilled 23.5 2.9 81.99 95.79 Download
    PiT-B distilled pit_b_distilled 73.8 12.5 84.14 96.86 Download

  • Citation：

    @article{heo2021pit,
    title={Rethinking Spatial Dimensions of Vision Transformers},
    author={Byeongho Heo and Sangdoo Yun and Dongyoon Han and Sanghyuk Chun and Junsuk Choe and Seong Joon Oh},
    journal={arXiv: 2103.16302},
    year={2021},
}