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DLA

  • Paper:Deep Layer Aggregation

  • Origin Repo:ucbdrive/dla

  • Code:dla.py

  • Evaluate Transforms:

    # backend: pil
    # input_size: 224x224
    transforms = T.Compose([
        T.Resize(256, interpolation='bilinear'),
        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
    DLA-34 dla_34 15.8 3.1 76.39 93.15 Download
    DLA-46-c dla_46_c 1.3 0.5 64.88 86.29 Download
    DLA-46x-c dla_46x_c 1.1 0.5 65.98 86.98 Download
    DLA-60 dla_60 22.0 4.2 77.02 93.31 Download
    DLA-60x dla_60x 17.4 3.5 78.24 94.02 Download
    DLA-60x-c dla_60x_c 1.3 0.6 67.91 88.43 Download
    DLA-102 dla_102 33.3 7.2 79.44 94.76 Download
    DLA-102x dla_102x 26.4 5.9 78.51 94.23 Download
    DLA-102x2 dla_102x2 41.4 9.3 79.45 94.64 Download
    DLA-169 dla_169 53.5 11.6 78.71 94.34 Download
  • Citation:

    @misc{yu2019deep,
        title={Deep Layer Aggregation}, 
        author={Fisher Yu and Dequan Wang and Evan Shelhamer and Trevor Darrell},
        year={2019},
        eprint={1707.06484},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
    }