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Training configuration for ResNets Strike Back, A3 configuration #924

Answered by rwightman
lengstrom asked this question in General
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Two configs attached, one for seed 21, one for seed 0 with train interpolation fixed at bicubic (otherwise randomly selects per sample). They both exceeded the paper 78.1 as you can see. This was run on 4xV100, batch 512 per card, so you'll need to scale LR appropriately if that differs.

_78_25-fusedlamb-cosine-lr0.00800-wd0.020000-n0-rand-m6-mstd0.5-inc1-m0.1-sd0.0-d0.0-ls0.0-100-100-resnet50-args.yaml.txt
_78_23-fusedlamb-cosine-lr0.00800-wd0.020000-n0-rand-m6-mstd0.5-inc1-m0.1-sd0.0-d0.0-ls0.0-101-100-resnet50-args.yaml.txt

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