@register_model
def resnext101d_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
    """Constructs a ResNeXt101d 32x4d model."""
    model_args = dict(
        block=Bottleneck,
        layers=(3, 4, 23, 3),
        cardinality=32,
        base_width=4,
        stem_width=32,
        stem_type="deep",
        avg_down=True,
    )
    return _create_resnet("resnext101d_32x4d", pretrained, **dict(model_args, **kwargs))

training command

bash

torchrun train.py \
    --data-dir ~/workspace/dataset/ImageNet --model resnext101d_32x4d --lr 0.6 --warmup-epochs 5 --epochs 240 \
    --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 256 -j 6 --amp --dist-bn reduce