[FEATURE] Update HRNet Pretrained Weights

[seefun]

Gluon Pretrained hrnet_w18_small and hrnet_w18_small_v2 has obvious advantages than these weights provided by torchvision / timm

hrnet_w18_small.ms_in1k 72.3 (Top1) | 90.7 (Top5) # timm
hrnet_w18_small.gluon_in1k 73.5 (Top1) | 91.0 (Top5) # gluon
hrnet_w18_small_v2.ms_in1k 75.1 (Top1) | 92.4 (Top5) # timm
hrnet_w18_small_v2.gluon_in1k 78.1 (Top1) | 93.8 (Top5) # gluon

These weights have been adapted into pytorch, which are able to download here:
https://drive.google.com/drive/folders/1-Tks3nh_WHIrYXGXbtSJQxJXKH5kol4H

Could you help add them to timm‘s huggingface hub

[rwightman]

@seefun I'd noticed gluon ones but is there much point with the ssld / ms_aug weights I added? there was another set of aug weights but their file was corrupted...

model	top1	top1_err	top5	top5_err	param_count	img_size	crop_pct	interpolation
243	hrnet_w48_ssld.paddle_in1k	84.478	15.522	97.234	2.766	77.47	288	1.000
499	hrnet_w18_ssld.paddle_in1k	82.046	17.954	96.250	3.750	21.30	288	1.000
720	hrnet_w64.ms_in1k	79.474	20.526	94.646	5.354	128.06	224	0.875
734	hrnet_w48.ms_in1k	79.302	20.698	94.516	5.484	77.47	224	0.875
759	hrnet_w40.ms_in1k	78.930	21.070	94.464	5.536	57.56	224	0.875
762	hrnet_w44.ms_in1k	78.892	21.108	94.364	5.636	67.06	224	0.875
793	hrnet_w32.ms_in1k	78.444	21.556	94.188	5.812	41.23	224	0.875
812	hrnet_w30.ms_in1k	78.192	21.808	94.222	5.778	37.71	224	0.875
817	hrnet_w18.ms_aug_in1k	78.126	21.874	94.052	5.948	21.30	224	0.950
884	hrnet_w18.ms_in1k	76.752	23.248	93.444	6.556	21.30	224	0.875
923	hrnet_w18_small_v2.ms_in1k	75.114	24.886	92.414	7.586	15.60	224	0.875
963	hrnet_w18_small.ms_in1k	72.338	27.662	90.678	9.322	13.19	224	0.875

[seefun]

@rwightman
Paddle ssld weights is much better than ms/ms_aug weights, bug there are only two ssld pretrained weights released: hrw18 and hrw48；
Gluon weights is also much better than ms/ms_aug weights, but only in small models (hrnet_w18_small and hrnet_w18_small_v2);

In my actual downstream tasks, the pretrained models of SSLD(w18 / w48) and Gluon(SmallV1 / SmallV2) were used most frequently, which greatly improved the performance than ms_aug weights.

ImageNet

model	top1	top5
hrnet_w18_small.ms_in1k	72.3	90.7
hrnet_w18_small.gluon_in1k	73.5(+1.2)	91.0
hrnet_w18_small_v2.ms_in1k	75.1	92.4
hrnet_w18_small_v2.gluon_in1k	78.1(+3.0)	93.8
hrnet_w18.ms_in1k	76.75	93.44
hrnet_w18.ms_aug_in1k	78.1	94.05
hrnet_w18_ssld.paddle_in1k	82.05	96.25
hrnet_w48.ms_in1k	79.30	94.52
hrnet_w48_ssld.paddle_in1k	84.48	97.23

CityScape

model	iter	mIOU
hrnet_w18_small.ms_in1k	40k	67.46
hrnet_w18_small.gluon_in1k	40k	68.17
hrnet_w18_small.ms_in1k	80k	71.06
hrnet_w18_small.gluon_in1k	80k	71.17
hrnet_w18_small_v2.ms_in1k	40k	73.51
hrnet_w18_small_v2.gluon_in1k	40k	75.81
hrnet_w18_small_v2.ms_in1k	80k	75.62
hrnet_w18_small_v2.gluon_in1k	80k	77.19

So I recommend adding only these two validated small models pretrained: hrnet_w18_small.gluon_in1k and hrnet_w18_small_v2.gluon_in1k from here

[rwightman]

@seefun okay, I'll look at doing this soon