pytorch-image-models/results/results-imagenet-r-clean.csv

1	model	top1	top1_err	top5	top5_err	param_count	img_size	cropt_pct	interpolation
2	tf_efficientnet_l2_ns	97.780	2.220	99.890	0.110	480.31	800	0.960	bicubic
3	tf_efficientnet_l2_ns_475	97.750	2.250	99.820	0.180	480.31	475	0.936	bicubic
4	vit_large_patch16_384	97.420	2.580	99.780	0.220	304.72	384	1.000	bicubic
5	tf_efficientnet_b7_ns	97.200	2.800	99.700	0.300	66.35	600	0.949	bicubic
6	swin_large_patch4_window12_384	97.170	2.830	99.680	0.320	196.74	384	1.000	bicubic
7	swin_base_patch4_window12_384	97.120	2.880	99.780	0.220	87.90	384	1.000	bicubic
8	tf_efficientnetv2_l_in21ft1k	97.110	2.890	99.710	0.290	118.52	480	1.000	bicubic
9	tf_efficientnet_b6_ns	97.020	2.980	99.710	0.290	43.04	528	0.942	bicubic
10	vit_base_patch16_384	97.020	2.980	99.710	0.290	86.86	384	1.000	bicubic
11	ig_resnext101_32x48d	96.970	3.030	99.670	0.330	828.41	224	0.875	bilinear
12	tf_efficientnetv2_m_in21ft1k	96.970	3.030	99.610	0.390	54.14	480	1.000	bicubic
13	swin_large_patch4_window7_224	96.950	3.050	99.660	0.340	196.53	224	0.900	bicubic
14	vit_large_r50_s32_384	96.950	3.050	99.710	0.290	329.09	384	1.000	bicubic
15	dm_nfnet_f6	96.920	3.080	99.720	0.280	438.36	576	0.956	bicubic
16	cait_m48_448	96.880	3.120	99.620	0.380	356.46	448	1.000	bicubic
17	resnetv2_152x4_bitm	96.870	3.130	99.660	0.340	936.53	480	1.000	bilinear
18	tf_efficientnet_b5_ns	96.870	3.130	99.640	0.360	30.39	456	0.934	bicubic
19	cait_m36_384	96.830	3.170	99.660	0.340	271.22	384	1.000	bicubic
20	dm_nfnet_f5	96.810	3.190	99.670	0.330	377.21	544	0.954	bicubic
21	dm_nfnet_f4	96.780	3.220	99.620	0.380	316.07	512	0.951	bicubic
22	ig_resnext101_32x32d	96.780	3.220	99.530	0.470	468.53	224	0.875	bilinear
23	dm_nfnet_f3	96.730	3.270	99.630	0.370	254.92	416	0.940	bicubic
24	tf_efficientnet_b4_ns	96.710	3.290	99.640	0.360	19.34	380	0.922	bicubic
25	vit_large_patch16_224	96.710	3.290	99.650	0.350	304.33	224	0.900	bicubic
26	tf_efficientnet_b8	96.700	3.300	99.530	0.470	87.41	672	0.954	bicubic
27	swin_base_patch4_window7_224	96.680	3.320	99.660	0.340	87.77	224	0.900	bicubic
28	tf_efficientnetv2_l	96.650	3.350	99.560	0.440	118.52	480	1.000	bicubic
29	cait_s36_384	96.630	3.370	99.600	0.400	68.37	384	1.000	bicubic
30	tf_efficientnet_b7	96.580	3.420	99.510	0.490	66.35	600	0.949	bicubic
31	cait_s24_384	96.570	3.430	99.550	0.450	47.06	384	1.000	bicubic
32	tf_efficientnet_b8_ap	96.550	3.450	99.540	0.460	87.41	672	0.954	bicubic
33	tf_efficientnetv2_m	96.550	3.450	99.570	0.430	54.14	480	1.000	bicubic
34	resnetv2_152x2_bitm	96.520	3.480	99.590	0.410	236.34	448	1.000	bilinear
35	deit_base_distilled_patch16_384	96.510	3.490	99.590	0.410	87.63	384	1.000	bicubic
36	tf_efficientnetv2_s_in21ft1k	96.470	3.530	99.570	0.430	21.46	384	1.000	bicubic
37	dm_nfnet_f2	96.460	3.540	99.540	0.460	193.78	352	0.920	bicubic
38	ecaresnet269d	96.460	3.540	99.610	0.390	102.09	352	1.000	bicubic
39	eca_nfnet_l2	96.450	3.550	99.620	0.380	56.72	384	1.000	bicubic
40	vit_base_r50_s16_384	96.450	3.550	99.660	0.340	98.95	384	1.000	bicubic
41	ig_resnext101_32x16d	96.440	3.560	99.540	0.460	194.03	224	0.875	bilinear
42	resnetrs420	96.400	3.600	99.540	0.460	191.89	416	1.000	bicubic
43	dm_nfnet_f1	96.390	3.610	99.470	0.530	132.63	320	0.910	bicubic
44	tf_efficientnet_b6_ap	96.370	3.630	99.550	0.450	43.04	528	0.942	bicubic
45	resmlp_big_24_224_in22ft1k	96.350	3.650	99.520	0.480	129.14	224	0.875	bicubic
46	tf_efficientnet_b7_ap	96.350	3.650	99.590	0.410	66.35	600	0.949	bicubic
47	seresnet152d	96.310	3.690	99.510	0.490	66.84	320	1.000	bicubic
48	vit_base_patch16_224	96.300	3.700	99.560	0.440	86.57	224	0.900	bicubic
49	tf_efficientnet_b6	96.290	3.710	99.520	0.480	43.04	528	0.942	bicubic
50	efficientnetv2_rw_m	96.270	3.730	99.560	0.440	53.24	416	1.000	bicubic
51	resnetv2_50x3_bitm	96.270	3.730	99.630	0.370	217.32	448	1.000	bilinear
52	swsl_resnext101_32x16d	96.270	3.730	99.500	0.500	194.03	224	0.875	bilinear
53	resnetv2_101x3_bitm	96.250	3.750	99.590	0.410	387.93	448	1.000	bilinear
54	swsl_resnext101_32x8d	96.240	3.760	99.590	0.410	88.79	224	0.875	bilinear
55	resnetrs350	96.240	3.760	99.470	0.530	163.96	384	1.000	bicubic
56	resnetv2_152x2_bit_teacher_384	96.190	3.810	99.500	0.500	236.34	384	1.000	bicubic
57	vit_large_r50_s32_224	96.180	3.820	99.530	0.470	328.99	224	0.900	bicubic
58	resnest269e	96.120	3.880	99.520	0.480	110.93	416	0.928	bicubic
59	resnet200d	96.110	3.890	99.460	0.540	64.69	320	1.000	bicubic
60	tf_efficientnet_b3_ns	96.100	3.900	99.480	0.520	12.23	300	0.904	bicubic
61	tf_efficientnet_b5_ap	96.080	3.920	99.540	0.460	30.39	456	0.934	bicubic
62	pit_b_distilled_224	96.070	3.930	99.380	0.620	74.79	224	0.900	bicubic
63	resnest200e	96.070	3.930	99.480	0.520	70.20	320	0.909	bicubic
64	resnetrs270	96.060	3.940	99.490	0.510	129.86	352	1.000	bicubic
65	vit_small_r26_s32_384	96.060	3.940	99.560	0.440	36.47	384	1.000	bicubic
66	swsl_resnext101_32x4d	96.050	3.950	99.530	0.470	44.18	224	0.875	bilinear
67	vit_base_patch16_224_miil	96.030	3.970	99.350	0.650	86.54	224	0.875	bilinear
68	cait_xs24_384	96.010	3.990	99.430	0.570	26.67	384	1.000	bicubic
69	resnetrs200	95.990	4.010	99.440	0.560	93.21	320	1.000	bicubic
70	tf_efficientnet_b5	95.980	4.020	99.450	0.550	30.39	456	0.934	bicubic
71	vit_small_patch16_384	95.980	4.020	99.590	0.410	22.20	384	1.000	bicubic
72	resnetrs152	95.960	4.040	99.380	0.620	86.62	320	1.000	bicubic
73	eca_nfnet_l1	95.940	4.060	99.490	0.510	41.41	320	1.000	bicubic
74	ig_resnext101_32x8d	95.930	4.070	99.380	0.620	88.79	224	0.875	bilinear
75	vit_base_patch32_384	95.900	4.100	99.440	0.560	88.30	384	1.000	bicubic
76	regnety_160	95.880	4.120	99.560	0.440	83.59	288	1.000	bicubic
77	resmlp_big_24_distilled_224	95.870	4.130	99.440	0.560	129.14	224	0.875	bicubic
78	resnet152d	95.870	4.130	99.430	0.570	60.21	320	1.000	bicubic
79	resnet101d	95.750	4.250	99.440	0.560	44.57	320	1.000	bicubic
80	resnetv2_152x2_bit_teacher	95.750	4.250	99.430	0.570	236.34	224	0.875	bicubic
81	deit_base_distilled_patch16_224	95.750	4.250	99.280	0.720	87.34	224	0.900	bicubic
82	twins_pcpvt_large	95.720	4.280	99.490	0.510	60.99	224	0.900	bicubic
83	twins_svt_large	95.720	4.280	99.370	0.630	99.27	224	0.900	bicubic
84	swin_small_patch4_window7_224	95.720	4.280	99.290	0.710	49.61	224	0.900	bicubic
85	efficientnetv2_rw_s	95.710	4.290	99.380	0.620	23.94	384	1.000	bicubic
86	tf_efficientnetv2_s	95.710	4.290	99.400	0.600	21.46	384	1.000	bicubic
87	dm_nfnet_f0	95.690	4.310	99.330	0.670	71.49	256	0.900	bicubic
88	cait_s24_224	95.650	4.350	99.390	0.610	46.92	224	1.000	bicubic
89	deit_base_patch16_384	95.650	4.350	99.240	0.760	86.86	384	1.000	bicubic
90	swsl_resnext50_32x4d	95.620	4.380	99.440	0.560	25.03	224	0.875	bilinear
91	tf_efficientnet_b4	95.590	4.410	99.330	0.670	19.34	380	0.922	bicubic
92	resnest101e	95.570	4.430	99.270	0.730	48.28	256	0.875	bilinear
93	twins_svt_base	95.570	4.430	99.230	0.770	56.07	224	0.900	bicubic
94	tf_efficientnet_b2_ns	95.520	4.480	99.340	0.660	9.11	260	0.890	bicubic
95	efficientnet_b4	95.520	4.480	99.390	0.610	19.34	384	1.000	bicubic
96	tresnet_xl_448	95.510	4.490	99.340	0.660	78.44	448	0.875	bilinear
97	tf_efficientnet_b4_ap	95.490	4.510	99.390	0.610	19.34	380	0.922	bicubic
98	regnety_032	95.470	4.530	99.320	0.680	19.44	288	1.000	bicubic
99	twins_pcpvt_base	95.460	4.540	99.390	0.610	43.83	224	0.900	bicubic
100	eca_nfnet_l0	95.450	4.550	99.390	0.610	24.14	288	1.000	bicubic
101	ssl_resnext101_32x16d	95.410	4.590	99.410	0.590	194.03	224	0.875	bilinear
102	tresnet_l_448	95.410	4.590	99.300	0.700	55.99	448	0.875	bilinear
103	nfnet_l0	95.390	4.610	99.420	0.580	35.07	288	1.000	bicubic
104	resnetv2_50x1_bit_distilled	95.390	4.610	99.430	0.570	25.55	224	0.875	bicubic
105	tresnet_m	95.380	4.620	99.150	0.850	31.39	224	0.875	bilinear
106	pnasnet5large	95.360	4.640	99.130	0.870	86.06	331	0.911	bicubic
107	ssl_resnext101_32x8d	95.340	4.660	99.320	0.680	88.79	224	0.875	bilinear
108	resnetv2_101x1_bitm	95.320	4.680	99.370	0.630	44.54	448	1.000	bilinear
109	resnetrs101	95.250	4.750	99.210	0.790	63.62	288	0.940	bicubic
110	vit_large_patch32_384	95.240	4.760	99.320	0.680	306.63	384	1.000	bicubic
111	cait_xxs36_384	95.220	4.780	99.320	0.680	17.37	384	1.000	bicubic
112	levit_384	95.210	4.790	99.160	0.840	39.13	224	0.900	bicubic
113	resnet51q	95.200	4.800	99.280	0.720	35.70	288	1.000	bilinear
114	swsl_resnet50	95.200	4.800	99.390	0.610	25.56	224	0.875	bilinear
115	ecaresnet101d	95.160	4.840	99.230	0.770	44.57	224	0.875	bicubic
116	ssl_resnext101_32x4d	95.160	4.840	99.300	0.700	44.18	224	0.875	bilinear
117	nasnetalarge	95.150	4.850	99.130	0.870	88.75	331	0.911	bicubic
118	efficientnet_b3	95.140	4.860	99.210	0.790	12.23	320	1.000	bicubic
119	vit_small_r26_s32_224	95.130	4.870	99.220	0.780	36.43	224	0.900	bicubic
120	tf_efficientnetv2_b3	95.120	4.880	99.200	0.800	14.36	300	0.904	bicubic
121	convit_base	95.100	4.900	99.140	0.860	86.54	224	0.875	bicubic
122	coat_lite_small	95.080	4.920	99.020	0.980	19.84	224	0.900	bicubic
123	ecaresnet50t	95.070	4.930	99.290	0.710	25.57	320	0.950	bicubic
124	tresnet_xl	95.060	4.940	99.260	0.740	78.44	224	0.875	bilinear
125	deit_base_patch16_224	95.010	4.990	98.980	1.020	86.57	224	0.900	bicubic
126	tf_efficientnet_b3_ap	94.970	5.030	99.110	0.890	12.23	300	0.904	bicubic
127	visformer_small	94.960	5.040	99.210	0.790	40.22	224	0.900	bicubic
128	gernet_l	94.930	5.070	99.200	0.800	31.08	256	0.875	bilinear
129	cait_xxs24_384	94.920	5.080	99.140	0.860	12.03	384	1.000	bicubic
130	convit_small	94.920	5.080	99.110	0.890	27.78	224	0.875	bicubic
131	tf_efficientnet_b3	94.910	5.090	99.110	0.890	12.23	300	0.904	bicubic
132	tresnet_l	94.900	5.100	99.030	0.970	55.99	224	0.875	bilinear
133	vit_small_patch16_224	94.880	5.120	99.270	0.730	22.05	224	0.900	bicubic
134	mixer_b16_224_miil	94.880	5.120	99.080	0.920	59.88	224	0.875	bilinear
135	tf_efficientnet_lite4	94.870	5.130	99.090	0.910	13.01	380	0.920	bilinear
136	tf_efficientnet_b1_ns	94.860	5.140	99.250	0.750	7.79	240	0.882	bicubic
137	seresnext50_32x4d	94.820	5.180	99.130	0.870	27.56	224	0.875	bicubic
138	pit_b_224	94.790	5.210	98.820	1.180	73.76	224	0.900	bicubic
139	coat_mini	94.770	5.230	98.950	1.050	10.34	224	0.900	bicubic
140	twins_svt_small	94.770	5.230	99.080	0.920	24.06	224	0.900	bicubic
141	legacy_senet154	94.730	5.270	99.100	0.900	115.09	224	0.875	bilinear
142	pit_s_distilled_224	94.730	5.270	99.190	0.810	24.04	224	0.900	bicubic
143	resnetv2_50x1_bitm	94.730	5.270	99.180	0.820	25.55	448	1.000	bilinear
144	gluon_resnet152_v1s	94.720	5.280	99.060	0.940	60.32	224	0.875	bicubic
145	gluon_senet154	94.710	5.290	98.970	1.030	115.09	224	0.875	bicubic
146	resnest50d_4s2x40d	94.710	5.290	99.130	0.870	30.42	224	0.875	bicubic
147	ssl_resnext50_32x4d	94.700	5.300	99.240	0.760	25.03	224	0.875	bilinear
148	efficientnet_el	94.670	5.330	99.130	0.870	10.59	300	0.904	bicubic
149	wide_resnet50_2	94.670	5.330	99.050	0.950	68.88	224	0.875	bicubic
150	rexnet_200	94.660	5.340	99.090	0.910	16.37	224	0.875	bicubic
151	tresnet_m_448	94.660	5.340	99.150	0.850	31.39	448	0.875	bilinear
152	gluon_seresnext101_64x4d	94.650	5.350	98.980	1.020	88.23	224	0.875	bicubic
153	resnest50d	94.620	5.380	99.030	0.970	27.48	224	0.875	bilinear
154	swin_tiny_patch4_window7_224	94.620	5.380	99.120	0.880	28.29	224	0.900	bicubic
155	twins_pcpvt_small	94.600	5.400	99.150	0.850	24.11	224	0.900	bicubic
156	deit_small_distilled_patch16_224	94.590	5.410	99.100	0.900	22.44	224	0.900	bicubic
157	pit_s_224	94.590	5.410	98.930	1.070	23.46	224	0.900	bicubic
158	vit_small_patch32_384	94.590	5.410	99.140	0.860	22.92	384	1.000	bicubic
159	tnt_s_patch16_224	94.580	5.420	99.180	0.820	23.76	224	0.900	bicubic
160	efficientnet_b3_pruned	94.580	5.420	99.070	0.930	9.86	300	0.904	bicubic
161	resmlp_36_distilled_224	94.570	5.430	99.160	0.840	44.69	224	0.875	bicubic
162	gernet_m	94.550	5.450	98.930	1.070	21.14	224	0.875	bilinear
163	repvgg_b3	94.550	5.450	98.910	1.090	123.09	224	0.875	bilinear
164	regnety_320	94.520	5.480	99.170	0.830	145.05	224	0.875	bicubic
165	repvgg_b3g4	94.490	5.510	99.020	0.980	83.83	224	0.875	bilinear
166	ecaresnet101d_pruned	94.450	5.550	99.100	0.900	24.88	224	0.875	bicubic
167	gluon_seresnext101_32x4d	94.450	5.550	99.090	0.910	48.96	224	0.875	bicubic
168	gluon_resnet152_v1d	94.440	5.560	99.010	0.990	60.21	224	0.875	bicubic
169	levit_256	94.400	5.600	99.060	0.940	18.89	224	0.900	bicubic
170	nf_resnet50	94.400	5.600	99.070	0.930	25.56	288	0.940	bicubic
171	vit_base_patch32_224	94.390	5.610	99.060	0.940	88.22	224	0.900	bicubic
172	resnest50d_1s4x24d	94.390	5.610	99.070	0.930	25.68	224	0.875	bicubic
173	inception_v4	94.380	5.620	98.820	1.180	42.68	299	0.875	bicubic
174	efficientnet_b2	94.370	5.630	99.050	0.950	9.11	288	1.000	bicubic
175	tf_efficientnet_el	94.360	5.640	99.100	0.900	10.59	300	0.904	bicubic
176	gluon_resnext101_64x4d	94.350	5.650	98.880	1.120	83.46	224	0.875	bicubic
177	inception_resnet_v2	94.340	5.660	98.800	1.200	55.84	299	0.897	bicubic
178	resmlp_24_distilled_224	94.330	5.670	99.090	0.910	30.02	224	0.875	bicubic
179	ssl_resnet50	94.310	5.690	99.150	0.850	25.56	224	0.875	bilinear
180	regnetx_120	94.270	5.730	99.190	0.810	46.11	224	0.875	bicubic
181	rexnet_150	94.270	5.730	99.080	0.920	9.73	224	0.875	bicubic
182	tf_efficientnet_b2_ap	94.270	5.730	98.950	1.050	9.11	260	0.890	bicubic
183	resmlp_big_24_224	94.260	5.740	98.820	1.180	129.14	224	0.875	bicubic
184	mixnet_xl	94.230	5.770	98.820	1.180	11.90	224	0.875	bicubic
185	tf_efficientnet_b2	94.210	5.790	99.030	0.970	9.11	260	0.890	bicubic
186	regnetx_320	94.210	5.790	99.050	0.950	107.81	224	0.875	bicubic
187	dpn92	94.190	5.810	98.930	1.070	37.67	224	0.875	bicubic
188	ecaresnet50d	94.190	5.810	99.020	0.980	25.58	224	0.875	bicubic
189	gluon_resnet101_v1d	94.170	5.830	98.940	1.060	44.57	224	0.875	bicubic
190	gluon_resnet101_v1s	94.170	5.830	99.010	0.990	44.67	224	0.875	bicubic
191	gluon_seresnext50_32x4d	94.170	5.830	98.910	1.090	27.56	224	0.875	bicubic
192	ecaresnetlight	94.140	5.860	98.950	1.050	30.16	224	0.875	bicubic
193	regnety_064	94.140	5.860	99.030	0.970	30.58	224	0.875	bicubic
194	ens_adv_inception_resnet_v2	94.130	5.870	98.790	1.210	55.84	299	0.897	bicubic
195	legacy_seresnext101_32x4d	94.130	5.870	98.970	1.030	48.96	224	0.875	bilinear
196	tf_efficientnet_lite3	94.130	5.870	98.960	1.040	8.20	300	0.904	bilinear
197	gluon_resnext101_32x4d	94.120	5.880	98.930	1.070	44.18	224	0.875	bicubic
198	efficientnet_el_pruned	94.090	5.910	99.010	0.990	10.59	300	0.904	bicubic
199	cspdarknet53	94.090	5.910	98.980	1.020	27.64	256	0.887	bilinear
200	seresnet50	94.080	5.920	98.970	1.030	28.09	224	0.875	bicubic
201	resnet50d	94.070	5.930	98.920	1.080	25.58	224	0.875	bicubic
202	tf_efficientnetv2_b2	94.070	5.930	98.930	1.070	10.10	260	0.890	bicubic
203	gluon_resnet152_v1b	94.030	5.970	98.740	1.260	60.19	224	0.875	bicubic
204	hrnet_w48	94.030	5.970	99.040	0.960	77.47	224	0.875	bilinear
205	resnetrs50	94.020	5.980	98.850	1.150	35.69	224	0.910	bicubic
206	gluon_xception65	94.010	5.990	99.020	0.980	39.92	299	0.903	bicubic
207	regnety_120	94.010	5.990	99.030	0.970	51.82	224	0.875	bicubic
208	deit_small_patch16_224	94.000	6.000	98.960	1.040	22.05	224	0.900	bicubic
209	dla102x2	94.000	6.000	99.030	0.970	41.28	224	0.875	bilinear
210	dpn107	93.960	6.040	98.840	1.160	86.92	224	0.875	bicubic
211	skresnext50_32x4d	93.950	6.050	98.820	1.180	27.48	224	0.875	bicubic
212	ecaresnet26t	93.940	6.060	98.920	1.080	16.01	320	0.950	bicubic
213	cait_xxs36_224	93.940	6.060	98.890	1.110	17.30	224	1.000	bicubic
214	dpn98	93.940	6.060	98.920	1.080	61.57	224	0.875	bicubic
215	xception71	93.890	6.110	98.950	1.050	42.34	299	0.903	bicubic
216	regnety_080	93.890	6.110	99.000	1.000	39.18	224	0.875	bicubic
217	gluon_resnet152_v1c	93.880	6.120	98.800	1.200	60.21	224	0.875	bicubic
218	regnetx_160	93.880	6.120	99.090	0.910	54.28	224	0.875	bicubic
219	nf_regnet_b1	93.880	6.120	98.740	1.260	10.22	288	0.900	bicubic
220	cspresnet50	93.860	6.140	98.870	1.130	21.62	256	0.887	bilinear
221	ese_vovnet39b	93.850	6.150	98.900	1.100	24.57	224	0.875	bicubic
222	resnext50_32x4d	93.840	6.160	98.830	1.170	25.03	224	0.875	bicubic
223	hrnet_w64	93.830	6.170	98.930	1.070	128.06	224	0.875	bilinear
224	ecaresnet50d_pruned	93.820	6.180	99.000	1.000	19.94	224	0.875	bicubic
225	repvgg_b2g4	93.820	6.180	98.930	1.070	61.76	224	0.875	bilinear
226	resnext50d_32x4d	93.810	6.190	98.740	1.260	25.05	224	0.875	bicubic
227	efficientnet_b2_pruned	93.800	6.200	98.910	1.090	8.31	260	0.890	bicubic
228	dla169	93.800	6.200	98.840	1.160	53.39	224	0.875	bilinear
229	regnetx_080	93.790	6.210	98.910	1.090	39.57	224	0.875	bicubic
230	resnext101_32x8d	93.770	6.230	98.950	1.050	88.79	224	0.875	bilinear
231	cspresnext50	93.760	6.240	98.840	1.160	20.57	224	0.875	bilinear
232	dpn131	93.760	6.240	98.800	1.200	79.25	224	0.875	bicubic
233	gluon_resnet101_v1b	93.760	6.240	98.700	1.300	44.55	224	0.875	bicubic
234	xception65	93.760	6.240	98.860	1.140	39.92	299	0.903	bicubic
235	efficientnet_em	93.740	6.260	98.930	1.070	6.90	240	0.882	bicubic
236	tf_efficientnet_b0_ns	93.740	6.260	98.980	1.020	5.29	224	0.875	bicubic
237	wide_resnet101_2	93.730	6.270	98.810	1.190	126.89	224	0.875	bilinear
238	resnetblur50	93.710	6.290	98.810	1.190	25.56	224	0.875	bicubic
239	tf_efficientnetv2_b1	93.710	6.290	98.820	1.180	8.14	240	0.882	bicubic
240	tf_efficientnet_b1	93.710	6.290	98.800	1.200	7.79	240	0.882	bicubic
241	levit_192	93.710	6.290	98.790	1.210	10.95	224	0.900	bicubic
242	hrnet_w40	93.710	6.290	98.800	1.200	57.56	224	0.875	bilinear
243	gluon_resnet101_v1c	93.690	6.310	98.760	1.240	44.57	224	0.875	bicubic
244	regnetx_040	93.680	6.320	98.940	1.060	22.12	224	0.875	bicubic
245	rexnet_130	93.670	6.330	98.710	1.290	7.56	224	0.875	bicubic
246	gluon_resnext50_32x4d	93.650	6.350	98.690	1.310	25.03	224	0.875	bicubic
247	resmlp_36_224	93.650	6.350	98.950	1.050	44.69	224	0.875	bicubic
248	xception	93.640	6.360	98.770	1.230	22.86	299	0.897	bicubic
249	regnetx_064	93.630	6.370	99.050	0.950	26.21	224	0.875	bicubic
250	tf_efficientnet_b1_ap	93.630	6.370	98.800	1.200	7.79	240	0.882	bicubic
251	hrnet_w44	93.620	6.380	98.960	1.040	67.06	224	0.875	bilinear
252	regnety_040	93.620	6.380	98.950	1.050	20.65	224	0.875	bicubic
253	dpn68b	93.620	6.380	98.700	1.300	12.61	224	0.875	bicubic
254	gluon_resnet50_v1s	93.590	6.410	98.840	1.160	25.68	224	0.875	bicubic
255	repvgg_b2	93.590	6.410	99.070	0.930	89.02	224	0.875	bilinear
256	res2net50_26w_6s	93.590	6.410	98.750	1.250	37.05	224	0.875	bilinear
257	dla60_res2next	93.570	6.430	98.800	1.200	17.03	224	0.875	bilinear
258	tf_efficientnet_cc_b1_8e	93.570	6.430	98.690	1.310	39.72	240	0.882	bicubic
259	gluon_inception_v3	93.540	6.460	98.830	1.170	23.83	299	0.875	bicubic
260	dla102x	93.530	6.470	98.850	1.150	26.31	224	0.875	bilinear
261	gluon_resnet50_v1d	93.530	6.470	98.710	1.290	25.58	224	0.875	bicubic
262	res2net101_26w_4s	93.520	6.480	98.600	1.400	45.21	224	0.875	bilinear
263	coat_tiny	93.510	6.490	98.690	1.310	5.50	224	0.900	bicubic
264	selecsls60b	93.500	6.500	98.840	1.160	32.77	224	0.875	bicubic
265	cait_xxs24_224	93.490	6.510	98.770	1.230	11.96	224	1.000	bicubic
266	xception41	93.480	6.520	98.750	1.250	26.97	299	0.903	bicubic
267	resnet50	93.460	6.540	98.600	1.400	25.56	224	0.875	bicubic
268	res2net50_26w_8s	93.450	6.550	98.700	1.300	48.40	224	0.875	bilinear
269	coat_lite_mini	93.450	6.550	98.780	1.220	11.01	224	0.900	bicubic
270	legacy_seresnet152	93.440	6.560	98.850	1.150	66.82	224	0.875	bilinear
271	resmlp_24_224	93.440	6.560	98.810	1.190	30.02	224	0.875	bicubic
272	legacy_seresnext50_32x4d	93.430	6.570	98.800	1.200	27.56	224	0.875	bilinear
273	vit_tiny_patch16_384	93.420	6.580	98.830	1.170	5.79	384	1.000	bicubic
274	repvgg_b1	93.410	6.590	98.790	1.210	57.42	224	0.875	bilinear
275	dla60_res2net	93.380	6.620	98.860	1.140	20.85	224	0.875	bilinear
276	hrnet_w30	93.370	6.630	98.830	1.170	37.71	224	0.875	bilinear
277	dla102	93.260	6.740	98.780	1.220	33.27	224	0.875	bilinear
278	legacy_seresnet101	93.260	6.740	98.740	1.260	49.33	224	0.875	bilinear
279	mixnet_l	93.260	6.740	98.700	1.300	7.33	224	0.875	bicubic
280	regnetx_032	93.250	6.750	98.730	1.270	15.30	224	0.875	bicubic
281	tv_resnet152	93.240	6.760	98.750	1.250	60.19	224	0.875	bilinear
282	pit_xs_distilled_224	93.240	6.760	98.820	1.180	11.00	224	0.900	bicubic
283	resnest26d	93.240	6.760	98.850	1.150	17.07	224	0.875	bilinear
284	tf_inception_v3	93.200	6.800	98.480	1.520	23.83	299	0.875	bicubic
285	dla60x	93.190	6.810	98.710	1.290	17.35	224	0.875	bilinear
286	res2net50_26w_4s	93.180	6.820	98.670	1.330	25.70	224	0.875	bilinear
287	tf_efficientnet_em	93.170	6.830	98.670	1.330	6.90	240	0.882	bicubic
288	res2next50	93.150	6.850	98.660	1.340	24.67	224	0.875	bilinear
289	tf_efficientnetv2_b0	93.060	6.940	98.700	1.300	7.14	224	0.875	bicubic
290	levit_128	93.050	6.950	98.690	1.310	9.21	224	0.900	bicubic
291	tf_mixnet_l	93.040	6.960	98.540	1.460	7.33	224	0.875	bicubic
292	res2net50_14w_8s	93.030	6.970	98.700	1.300	25.06	224	0.875	bilinear
293	repvgg_b1g4	93.030	6.970	98.820	1.180	39.97	224	0.875	bilinear
294	efficientnet_b1	93.030	6.970	98.710	1.290	7.79	256	1.000	bicubic
295	adv_inception_v3	93.010	6.990	98.490	1.510	23.83	299	0.875	bicubic
296	selecsls60	93.010	6.990	98.830	1.170	30.67	224	0.875	bicubic
297	regnety_016	93.000	7.000	98.680	1.320	11.20	224	0.875	bicubic
298	efficientnet_b1_pruned	92.980	7.020	98.530	1.470	6.33	240	0.882	bicubic
299	hardcorenas_f	92.980	7.020	98.620	1.380	8.20	224	0.875	bilinear
300	hardcorenas_e	92.950	7.050	98.570	1.430	8.07	224	0.875	bilinear
301	hrnet_w32	92.950	7.050	98.840	1.160	41.23	224	0.875	bilinear
302	efficientnet_es	92.910	7.090	98.690	1.310	5.44	224	0.875	bicubic
303	gluon_resnet50_v1c	92.910	7.090	98.710	1.290	25.58	224	0.875	bicubic
304	pit_xs_224	92.910	7.090	98.780	1.220	10.62	224	0.900	bicubic
305	tv_resnext50_32x4d	92.900	7.100	98.720	1.280	25.03	224	0.875	bilinear
306	inception_v3	92.900	7.100	98.330	1.670	23.83	299	0.875	bicubic
307	densenet161	92.900	7.100	98.810	1.190	28.68	224	0.875	bicubic
308	tv_resnet101	92.880	7.120	98.660	1.340	44.55	224	0.875	bilinear
309	resmlp_12_distilled_224	92.870	7.130	98.630	1.370	15.35	224	0.875	bicubic
310	tf_efficientnet_cc_b0_8e	92.870	7.130	98.460	1.540	24.01	224	0.875	bicubic
311	coat_lite_tiny	92.850	7.150	98.640	1.360	5.72	224	0.900	bicubic
312	rexnet_100	92.850	7.150	98.620	1.380	4.80	224	0.875	bicubic
313	tf_efficientnet_cc_b0_4e	92.840	7.160	98.440	1.560	13.31	224	0.875	bicubic
314	seresnext26t_32x4d	92.820	7.180	98.560	1.440	16.81	224	0.875	bicubic
315	res2net50_48w_2s	92.790	7.210	98.470	1.530	25.29	224	0.875	bilinear
316	hrnet_w18	92.760	7.240	98.660	1.340	21.30	224	0.875	bilinear
317	densenet201	92.690	7.310	98.650	1.350	20.01	224	0.875	bicubic
318	repvgg_a2	92.680	7.320	98.520	1.480	28.21	224	0.875	bilinear
319	gmixer_24_224	92.680	7.320	98.280	1.720	24.72	224	0.875	bicubic
320	dla60	92.670	7.330	98.630	1.370	22.04	224	0.875	bilinear
321	legacy_seresnet50	92.670	7.330	98.650	1.350	28.09	224	0.875	bilinear
322	resnet34d	92.640	7.360	98.420	1.580	21.82	224	0.875	bicubic
323	mobilenetv2_120d	92.610	7.390	98.510	1.490	5.83	224	0.875	bicubic
324	tf_efficientnet_b0_ap	92.610	7.390	98.370	1.630	5.29	224	0.875	bicubic
325	hardcorenas_d	92.600	7.400	98.430	1.570	7.50	224	0.875	bilinear
326	tf_efficientnet_lite2	92.590	7.410	98.550	1.450	6.09	260	0.890	bicubic
327	legacy_seresnext26_32x4d	92.570	7.430	98.420	1.580	16.79	224	0.875	bicubic
328	skresnet34	92.570	7.430	98.520	1.480	22.28	224	0.875	bicubic
329	gluon_resnet50_v1b	92.560	7.440	98.550	1.450	25.56	224	0.875	bicubic
330	regnetx_016	92.540	7.460	98.550	1.450	9.19	224	0.875	bicubic
331	selecsls42b	92.480	7.520	98.440	1.560	32.46	224	0.875	bicubic
332	efficientnet_b0	92.480	7.520	98.680	1.320	5.29	224	0.875	bicubic
333	gernet_s	92.440	7.560	98.500	1.500	8.17	224	0.875	bilinear
334	seresnext26d_32x4d	92.440	7.560	98.540	1.460	16.81	224	0.875	bicubic
335	densenetblur121d	92.400	7.600	98.410	1.590	8.00	224	0.875	bicubic
336	tf_efficientnet_b0	92.400	7.600	98.470	1.530	5.29	224	0.875	bicubic
337	hardcorenas_c	92.330	7.670	98.340	1.660	5.52	224	0.875	bilinear
338	tf_efficientnet_lite1	92.310	7.690	98.490	1.510	5.42	240	0.882	bicubic
339	densenet169	92.300	7.700	98.590	1.410	14.15	224	0.875	bicubic
340	mixnet_m	92.270	7.730	98.350	1.650	5.01	224	0.875	bicubic
341	mobilenetv3_large_100_miil	92.250	7.750	98.250	1.750	5.48	224	0.875	bilinear
342	dpn68	92.240	7.760	98.610	1.390	12.61	224	0.875	bicubic
343	resnet26d	92.230	7.770	98.450	1.550	16.01	224	0.875	bicubic
344	tf_mixnet_m	92.200	7.800	98.420	1.580	5.01	224	0.875	bicubic
345	vit_small_patch32_224	92.150	7.850	98.510	1.490	22.88	224	0.900	bicubic
346	tv_resnet50	92.140	7.860	98.420	1.580	25.56	224	0.875	bilinear
347	resmlp_12_224	92.120	7.880	98.570	1.430	15.35	224	0.875	bicubic
348	tf_efficientnet_es	92.100	7.900	98.440	1.560	5.44	224	0.875	bicubic
349	mobilenetv2_140	92.030	7.970	98.250	1.750	6.11	224	0.875	bicubic
350	ese_vovnet19b_dw	92.010	7.990	98.510	1.490	6.54	224	0.875	bicubic
351	densenet121	91.940	8.060	98.280	1.720	7.98	224	0.875	bicubic
352	hardcorenas_b	91.940	8.060	98.400	1.600	5.18	224	0.875	bilinear
353	vit_tiny_patch16_224	91.930	8.070	98.340	1.660	5.72	224	0.900	bicubic
354	regnety_008	91.900	8.100	98.420	1.580	6.26	224	0.875	bicubic
355	mixnet_s	91.780	8.220	98.300	1.700	4.13	224	0.875	bicubic
356	vit_tiny_r_s16_p8_384	91.730	8.270	98.430	1.570	6.36	384	1.000	bicubic
357	efficientnet_es_pruned	91.700	8.300	98.420	1.580	5.44	224	0.875	bicubic
358	tf_mixnet_s	91.680	8.320	98.240	1.760	4.13	224	0.875	bicubic
359	repvgg_b0	91.680	8.320	98.450	1.550	15.82	224	0.875	bilinear
360	semnasnet_100	91.660	8.340	98.270	1.730	3.89	224	0.875	bicubic
361	hardcorenas_a	91.620	8.380	98.170	1.830	5.26	224	0.875	bilinear
362	regnety_006	91.570	8.430	98.430	1.570	6.06	224	0.875	bicubic
363	mobilenetv3_rw	91.550	8.450	98.270	1.730	5.48	224	0.875	bicubic
364	levit_128s	91.500	8.500	98.400	1.600	7.78	224	0.900	bicubic
365	legacy_seresnet34	91.480	8.520	98.200	1.800	21.96	224	0.875	bilinear
366	mobilenetv3_large_100	91.480	8.520	98.320	1.680	5.48	224	0.875	bicubic
367	resnet26	91.440	8.560	98.280	1.720	16.00	224	0.875	bicubic
368	tf_mobilenetv3_large_100	91.420	8.580	98.260	1.740	5.48	224	0.875	bilinear
369	tv_densenet121	91.400	8.600	98.250	1.750	7.98	224	0.875	bicubic
370	mobilenetv2_110d	91.350	8.650	98.190	1.810	4.52	224	0.875	bicubic
371	tf_efficientnet_lite0	91.300	8.700	98.090	1.910	4.65	224	0.875	bicubic
372	fbnetc_100	91.270	8.730	97.830	2.170	5.57	224	0.875	bilinear
373	efficientnet_lite0	91.260	8.740	98.250	1.750	4.65	224	0.875	bicubic
374	dla34	91.240	8.760	98.180	1.820	15.74	224	0.875	bilinear
375	mnasnet_100	91.200	8.800	98.050	1.950	4.38	224	0.875	bicubic
376	resnet34	91.200	8.800	98.240	1.760	21.80	224	0.875	bilinear
377	regnetx_008	91.180	8.820	98.380	1.620	7.26	224	0.875	bicubic
378	hrnet_w18_small_v2	91.170	8.830	98.340	1.660	15.60	224	0.875	bilinear
379	mixer_b16_224	91.140	8.860	97.400	2.600	59.88	224	0.875	bicubic
380	resnest14d	91.130	8.870	98.330	1.670	10.61	224	0.875	bilinear
381	deit_tiny_distilled_patch16_224	91.100	8.900	98.270	1.730	5.91	224	0.900	bicubic
382	gluon_resnet34_v1b	91.100	8.900	98.180	1.820	21.80	224	0.875	bicubic
383	swsl_resnet18	91.090	8.910	98.210	1.790	11.69	224	0.875	bilinear
384	vgg19_bn	91.000	9.000	98.110	1.890	143.68	224	0.875	bilinear
385	pit_ti_distilled_224	90.900	9.100	98.220	1.780	5.10	224	0.900	bicubic
386	regnety_004	90.780	9.220	98.080	1.920	4.34	224	0.875	bicubic
387	regnetx_006	90.760	9.240	98.100	1.900	6.20	224	0.875	bicubic
388	ssl_resnet18	90.700	9.300	98.020	1.980	11.69	224	0.875	bilinear
389	spnasnet_100	90.610	9.390	97.950	2.050	4.42	224	0.875	bilinear
390	vgg16_bn	90.540	9.460	97.990	2.010	138.37	224	0.875	bilinear
391	convit_tiny	90.530	9.470	98.210	1.790	5.71	224	0.875	bicubic
392	ghostnet_100	90.440	9.560	97.830	2.170	5.18	224	0.875	bilinear
393	pit_ti_224	90.420	9.580	98.010	1.990	4.85	224	0.900	bicubic
394	tf_mobilenetv3_large_075	90.320	9.680	97.870	2.130	3.99	224	0.875	bilinear
395	tv_resnet34	90.290	9.710	97.980	2.020	21.80	224	0.875	bilinear
396	skresnet18	90.160	9.840	97.780	2.220	11.96	224	0.875	bicubic
397	resnet18d	89.990	10.010	97.830	2.170	11.71	224	0.875	bicubic
398	hrnet_w18_small	89.880	10.120	97.900	2.100	13.19	224	0.875	bilinear
399	mobilenetv2_100	89.830	10.170	97.830	2.170	3.50	224	0.875	bicubic
400	vgg19	89.680	10.320	97.550	2.450	143.67	224	0.875	bilinear
401	deit_tiny_patch16_224	89.620	10.380	97.960	2.040	5.72	224	0.900	bicubic
402	regnetx_004	89.460	10.540	97.770	2.230	5.16	224	0.875	bicubic
403	vgg16	89.360	10.640	97.520	2.480	138.36	224	0.875	bilinear
404	vit_tiny_r_s16_p8_224	89.340	10.660	97.700	2.300	6.34	224	0.900	bicubic
405	legacy_seresnet18	89.270	10.730	97.680	2.320	11.78	224	0.875	bicubic
406	vgg13_bn	89.200	10.800	97.530	2.470	133.05	224	0.875	bilinear
407	tf_mobilenetv3_large_minimal_100	89.180	10.820	97.320	2.680	3.92	224	0.875	bilinear
408	gluon_resnet18_v1b	88.660	11.340	97.100	2.900	11.69	224	0.875	bicubic
409	vgg11_bn	88.390	11.610	97.270	2.730	132.87	224	0.875	bilinear
410	regnety_002	88.200	11.800	97.430	2.570	3.16	224	0.875	bicubic
411	resnet18	88.150	11.850	97.120	2.880	11.69	224	0.875	bilinear
412	vgg13	87.570	12.430	97.120	2.880	133.05	224	0.875	bilinear
413	regnetx_002	87.380	12.620	96.990	3.010	2.68	224	0.875	bicubic
414	vgg11	87.340	12.660	97.110	2.890	132.86	224	0.875	bilinear
415	dla60x_c	87.110	12.890	97.140	2.860	1.32	224	0.875	bilinear
416	mixer_l16_224	86.970	13.030	94.060	5.940	208.20	224	0.875	bicubic
417	tf_mobilenetv3_small_100	85.960	14.040	96.400	3.600	2.54	224	0.875	bilinear
418	dla46x_c	85.480	14.520	96.440	3.560	1.07	224	0.875	bilinear
419	dla46_c	84.660	15.340	96.200	3.800	1.30	224	0.875	bilinear
420	tf_mobilenetv3_small_075	84.530	15.470	95.890	4.110	2.04	224	0.875	bilinear
421	tf_mobilenetv3_small_minimal_100	82.670	17.330	95.000	5.000	2.04	224	0.875	bilinear