Better model checkpoints for various classification models

[datumbox]

Fixes partially #3995

torchrun --nproc_per_node=1 train.py --test-only --weights ImageNet1K_RefV2 --model mobilenet_v3_large -b 1
Acc@1 75.274 Acc@5 92.566

torchrun --nproc_per_node=1 train.py --test-only --weights ImageNet1K_RefV2 --model resnet101 -b 1
Acc@1 81.886 Acc@5 95.780

torchrun --nproc_per_node=1 train.py --test-only --weights ImageNet1K_RefV2 --model resnet152 -b 1
Acc@1 82.284 Acc@5 96.002

torchrun --nproc_per_node=1 train.py --test-only --weights ImageNet1K_RefV2 --model resnext50_32x4d -b 1
Acc@1 81.198 Acc@5 95.340

torchrun --nproc_per_node=1 train.py --test-only --weights ImageNet1K_RefV2 --model resnext101_32x8d -b 1
Acc@1 82.834 Acc@5 96.228

cc @datumbox @bjuncek

[facebook-github-bot]

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[oke-aditya]

Are you using Resnet strikes back recipe? https://arxiv.org/pdf/2110.00476.pdf
The accuracy look like it matches.

[datumbox]

This is from a different recipe, you can read #3995 the high-level info. Next week we will publish a blogpost with the details. :)

[prabhat00155]

Thanks @datumbox!