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

1	model	top1	top1_err	top5	top5_err	param_count	img_size	cropt_pct	interpolation
2	tf_efficientnet_l2_ns	98.530	1.470	99.820	0.180	480.31	800	0.960	bicubic
3	tf_efficientnet_l2_ns_475	98.530	1.470	99.840	0.160	480.31	475	0.936	bicubic
4	tf_efficientnet_b7_ns	97.920	2.080	99.720	0.280	66.35	600	0.949	bicubic
5	dm_nfnet_f6	97.740	2.260	99.580	0.420	438.36	576	0.956	bicubic
6	tf_efficientnet_b6_ns	97.630	2.370	99.590	0.410	43.04	528	0.942	bicubic
7	dm_nfnet_f4	97.580	2.420	99.530	0.470	316.07	512	0.951	bicubic
8	dm_nfnet_f5	97.580	2.420	99.550	0.450	377.21	544	0.954	bicubic
9	ig_resnext101_32x48d	97.580	2.420	99.690	0.310	828.41	224	0.875	bilinear
10	tf_efficientnet_b5_ns	97.500	2.500	99.650	0.350	30.39	456	0.934	bicubic
11	resnetv2_152x4_bitm	97.470	2.530	99.600	0.400	936.53	480	1.000	bilinear
12	dm_nfnet_f3	97.400	2.600	99.590	0.410	254.92	416	0.940	bicubic
13	ig_resnext101_32x32d	97.320	2.680	99.660	0.340	468.53	224	0.875	bilinear
14	tf_efficientnet_b8	97.230	2.770	99.500	0.500	87.41	672	0.954	bicubic
15	tf_efficientnet_b7_ap	97.210	2.790	99.550	0.450	66.35	600	0.949	bicubic
16	swsl_resnext101_32x8d	97.200	2.800	99.540	0.460	88.79	224	0.875	bilinear
17	vit_base_resnet50_384	97.190	2.810	99.560	0.440	98.95	384	1.000	bicubic
18	resnetv2_152x2_bitm	97.160	2.840	99.580	0.420	236.34	480	1.000	bilinear
19	ecaresnet269d	97.140	2.860	99.460	0.540	102.09	352	1.000	bicubic
20	vit_large_patch16_384	97.130	2.870	99.640	0.360	304.72	384	1.000	bicubic
21	tf_efficientnet_b6_ap	97.110	2.890	99.610	0.390	43.04	528	0.942	bicubic
22	tf_efficientnet_b8_ap	97.100	2.900	99.660	0.340	87.41	672	0.954	bicubic
23	resnetv2_101x3_bitm	97.040	2.960	99.520	0.480	387.93	480	1.000	bilinear
24	tf_efficientnet_b7	97.010	2.990	99.520	0.480	66.35	600	0.949	bicubic
25	tf_efficientnet_b4_ns	96.960	3.040	99.600	0.400	19.34	380	0.922	bicubic
26	vit_deit_base_distilled_patch16_384	96.960	3.040	99.490	0.510	87.63	384	1.000	bicubic
27	dm_nfnet_f1	96.930	3.070	99.380	0.620	132.63	320	0.910	bicubic
28	dm_nfnet_f2	96.890	3.110	99.450	0.550	193.78	352	0.920	bicubic
29	ig_resnext101_32x16d	96.820	3.180	99.610	0.390	194.03	224	0.875	bilinear
30	seresnet152d	96.810	3.190	99.450	0.550	66.84	320	1.000	bicubic
31	resnetv2_50x3_bitm	96.770	3.230	99.430	0.570	217.32	480	1.000	bilinear
32	vit_base_patch16_384	96.740	3.260	99.480	0.520	86.86	384	1.000	bicubic
33	resnet200d	96.720	3.280	99.340	0.660	64.69	320	1.000	bicubic
34	tf_efficientnet_b6	96.690	3.310	99.370	0.630	43.04	528	0.942	bicubic
35	tf_efficientnet_b5_ap	96.670	3.330	99.470	0.530	30.39	456	0.934	bicubic
36	swsl_resnext101_32x16d	96.610	3.390	99.520	0.480	194.03	224	0.875	bilinear
37	resnest200e	96.550	3.450	99.320	0.680	70.20	320	0.909	bicubic
38	resnest269e	96.550	3.450	99.350	0.650	110.93	416	0.928	bicubic
39	swsl_resnext101_32x4d	96.400	3.600	99.450	0.550	44.18	224	0.875	bilinear
40	tf_efficientnet_b3_ns	96.380	3.620	99.340	0.660	12.23	300	0.904	bicubic
41	tf_efficientnet_b5	96.350	3.650	99.320	0.680	30.39	456	0.934	bicubic
42	resnet152d	96.350	3.650	99.370	0.630	60.21	320	1.000	bicubic
43	resnet101d	96.300	3.700	99.240	0.760	44.57	320	1.000	bicubic
44	ig_resnext101_32x8d	96.270	3.730	99.410	0.590	88.79	224	0.875	bilinear
45	vit_deit_base_patch16_384	96.170	3.830	99.140	0.860	86.86	384	1.000	bicubic
46	tf_efficientnet_b4_ap	96.150	3.850	99.250	0.750	19.34	380	0.922	bicubic
47	dm_nfnet_f0	96.140	3.860	99.240	0.760	71.49	256	0.900	bicubic
48	vit_deit_base_distilled_patch16_224	96.110	3.890	99.180	0.820	87.34	224	0.900	bicubic
49	resnetv2_101x1_bitm	96.090	3.910	99.310	0.690	44.54	480	1.000	bilinear
50	vit_large_patch16_224	95.940	4.060	99.220	0.780	304.33	224	0.900	bicubic
51	tresnet_xl_448	95.930	4.070	99.160	0.840	78.44	448	0.875	bilinear
52	resnest101e	95.910	4.090	99.180	0.820	48.28	256	0.875	bilinear
53	swsl_resnext50_32x4d	95.900	4.100	99.250	0.750	25.03	224	0.875	bilinear
54	tf_efficientnet_b4	95.870	4.130	99.140	0.860	19.34	380	0.922	bicubic
55	tresnet_l_448	95.850	4.150	99.140	0.860	55.99	448	0.875	bilinear
56	vit_base_patch32_384	95.820	4.180	99.150	0.850	88.30	384	1.000	bicubic
57	vit_large_patch32_384	95.810	4.190	99.150	0.850	306.63	384	1.000	bicubic
58	tf_efficientnet_b2_ns	95.750	4.250	99.150	0.850	9.11	260	0.890	bicubic
59	efficientnet_b3a	95.720	4.280	99.050	0.950	12.23	320	1.000	bicubic
60	pnasnet5large	95.690	4.310	98.900	1.100	86.06	331	0.911	bicubic
61	nasnetalarge	95.690	4.310	98.950	1.050	88.75	331	0.911	bicubic
62	ssl_resnext101_32x16d	95.640	4.360	99.130	0.870	194.03	224	0.875	bilinear
63	swsl_resnet50	95.550	4.450	99.260	0.740	25.56	224	0.875	bilinear
64	ecaresnet50t	95.520	4.480	99.110	0.890	25.57	320	0.950	bicubic
65	ssl_resnext101_32x4d	95.510	4.490	99.060	0.940	44.18	224	0.875	bilinear
66	ecaresnet101d	95.490	4.510	99.090	0.910	44.57	224	0.875	bicubic
67	tresnet_xl	95.470	4.530	99.140	0.860	78.44	224	0.875	bilinear
68	ssl_resnext101_32x8d	95.440	4.560	99.140	0.860	88.79	224	0.875	bilinear
69	vit_deit_base_patch16_224	95.440	4.560	98.820	1.180	86.57	224	0.900	bicubic
70	efficientnet_b3	95.420	4.580	99.100	0.900	12.23	300	0.904	bicubic
71	vit_base_patch16_224	95.360	4.640	99.000	1.000	86.57	224	0.900	bicubic
72	tf_efficientnet_b3_ap	95.310	4.690	98.940	1.060	12.23	300	0.904	bicubic
73	gernet_l	95.190	4.810	98.870	1.130	31.08	256	0.875	bilinear
74	regnety_032	95.180	4.820	99.140	0.860	19.44	224	0.875	bicubic
75	tf_efficientnet_b1_ns	95.180	4.820	99.080	0.920	7.79	240	0.882	bicubic
76	tresnet_l	95.180	4.820	99.010	0.990	55.99	224	0.875	bilinear
77	gluon_resnet152_v1s	95.110	4.890	98.930	1.070	60.32	224	0.875	bicubic
78	ecaresnet101d_pruned	95.080	4.920	98.960	1.040	24.88	224	0.875	bicubic
79	seresnext50_32x4d	95.080	4.920	98.910	1.090	27.56	224	0.875	bicubic
80	wide_resnet50_2	95.040	4.960	98.990	1.010	68.88	224	0.875	bicubic
81	resnest50d_4s2x40d	95.040	4.960	99.090	0.910	30.42	224	0.875	bicubic
82	resnetv2_50x1_bitm	95.030	4.970	99.170	0.830	25.55	480	1.000	bilinear
83	tf_efficientnet_b3	95.020	4.980	98.900	1.100	12.23	300	0.904	bicubic
84	rexnet_200	95.000	5.000	98.970	1.030	16.37	224	0.875	bicubic
85	tf_efficientnet_lite4	94.980	5.020	98.990	1.010	13.01	380	0.920	bilinear
86	tresnet_m_448	94.980	5.020	98.990	1.010	31.39	448	0.875	bilinear
87	legacy_senet154	94.970	5.030	98.870	1.130	115.09	224	0.875	bilinear
88	gluon_senet154	94.950	5.050	98.740	1.260	115.09	224	0.875	bicubic
89	gluon_seresnext101_32x4d	94.940	5.060	98.730	1.270	48.96	224	0.875	bicubic
90	ssl_resnext50_32x4d	94.930	5.070	98.850	1.150	25.03	224	0.875	bilinear
91	gluon_seresnext101_64x4d	94.880	5.120	98.840	1.160	88.23	224	0.875	bicubic
92	gluon_resnet101_v1s	94.750	5.250	98.890	1.110	44.67	224	0.875	bicubic
93	vit_deit_small_distilled_patch16_224	94.720	5.280	99.030	0.970	22.44	224	0.900	bicubic
94	ecaresnetlight	94.690	5.310	98.800	1.200	30.16	224	0.875	bicubic
95	gernet_m	94.670	5.330	98.840	1.160	21.14	224	0.875	bilinear
96	resnest50d	94.670	5.330	98.790	1.210	27.48	224	0.875	bilinear
97	gluon_resnet152_v1d	94.660	5.340	98.780	1.220	60.21	224	0.875	bicubic
98	resnest50d_1s4x24d	94.660	5.340	99.000	1.000	25.68	224	0.875	bicubic
99	efficientnet_b3_pruned	94.630	5.370	98.770	1.230	9.86	300	0.904	bicubic
100	gluon_resnext101_32x4d	94.630	5.370	98.630	1.370	44.18	224	0.875	bicubic
101	nf_resnet50	94.620	5.380	98.780	1.220	25.56	288	0.940	bicubic
102	efficientnet_b2a	94.620	5.380	98.730	1.270	9.11	288	1.000	bicubic
103	regnety_320	94.610	5.390	98.840	1.160	145.05	224	0.875	bicubic
104	ecaresnet50d	94.610	5.390	98.870	1.130	25.58	224	0.875	bicubic
105	tresnet_m	94.600	5.400	98.560	1.440	31.39	224	0.875	bilinear
106	cspdarknet53	94.590	5.410	98.780	1.220	27.64	256	0.887	bilinear
107	efficientnet_b2	94.560	5.440	98.720	1.280	9.11	260	0.875	bicubic
108	gluon_resnext101_64x4d	94.550	5.450	98.640	1.360	83.46	224	0.875	bicubic
109	repvgg_b3	94.550	5.450	98.750	1.250	123.09	224	0.875	bilinear
110	seresnet50	94.500	5.500	98.710	1.290	28.09	224	0.875	bicubic
111	repvgg_b3g4	94.490	5.510	98.950	1.050	83.83	224	0.875	bilinear
112	inception_resnet_v2	94.490	5.510	98.770	1.230	55.84	299	0.897	bicubic
113	tf_efficientnet_b2_ap	94.460	5.540	98.640	1.360	9.11	260	0.890	bicubic
114	cspresnext50	94.440	5.560	98.720	1.280	20.57	224	0.875	bilinear
115	legacy_seresnext101_32x4d	94.430	5.570	98.580	1.420	48.96	224	0.875	bilinear
116	regnety_120	94.430	5.570	98.870	1.130	51.82	224	0.875	bicubic
117	ssl_resnet50	94.430	5.570	98.870	1.130	25.56	224	0.875	bilinear
118	tf_efficientnet_el	94.420	5.580	98.750	1.250	10.59	300	0.904	bicubic
119	regnety_160	94.410	5.590	98.880	1.120	83.59	224	0.875	bicubic
120	tf_efficientnet_b2	94.400	5.600	98.610	1.390	9.11	260	0.890	bicubic
121	rexnet_150	94.400	5.600	98.820	1.180	9.73	224	0.875	bicubic
122	inception_v4	94.360	5.640	98.560	1.440	42.68	299	0.875	bicubic
123	vit_deit_small_patch16_224	94.350	5.650	98.670	1.330	22.05	224	0.900	bicubic
124	regnetx_320	94.340	5.660	98.810	1.190	107.81	224	0.875	bicubic
125	resnet50d	94.340	5.660	98.730	1.270	25.58	224	0.875	bicubic
126	dpn107	94.330	5.670	98.530	1.470	86.92	224	0.875	bicubic
127	ecaresnet26t	94.320	5.680	98.710	1.290	16.01	320	0.950	bicubic
128	gluon_seresnext50_32x4d	94.310	5.690	98.590	1.410	27.56	224	0.875	bicubic
129	ecaresnet50d_pruned	94.280	5.720	98.790	1.210	19.94	224	0.875	bicubic
130	xception71	94.280	5.720	98.620	1.380	42.34	299	0.903	bicubic
131	gluon_resnet101_v1d	94.270	5.730	98.640	1.360	44.57	224	0.875	bicubic
132	gluon_xception65	94.250	5.750	98.590	1.410	39.92	299	0.903	bicubic
133	tf_efficientnet_lite3	94.240	5.760	98.630	1.370	8.20	300	0.904	bilinear
134	regnety_064	94.230	5.770	98.750	1.250	30.58	224	0.875	bicubic
135	regnetx_160	94.220	5.780	98.800	1.200	54.28	224	0.875	bicubic
136	dpn92	94.210	5.790	98.730	1.270	37.67	224	0.875	bicubic
137	skresnext50_32x4d	94.200	5.800	98.460	1.540	27.48	224	0.875	bicubic
138	mixnet_xl	94.190	5.810	98.350	1.650	11.90	224	0.875	bicubic
139	gluon_resnet152_v1c	94.180	5.820	98.640	1.360	60.21	224	0.875	bicubic
140	regnety_080	94.160	5.840	98.700	1.300	39.18	224	0.875	bicubic
141	resnext50d_32x4d	94.160	5.840	98.540	1.460	25.05	224	0.875	bicubic
142	ens_adv_inception_resnet_v2	94.160	5.840	98.630	1.370	55.84	299	0.897	bicubic
143	gluon_resnet152_v1b	94.160	5.840	98.520	1.480	60.19	224	0.875	bicubic
144	ese_vovnet39b	94.160	5.840	98.690	1.310	24.57	224	0.875	bicubic
145	nf_regnet_b1	94.140	5.860	98.600	1.400	10.22	288	0.900	bicubic
146	regnetx_120	94.120	5.880	98.740	1.260	46.11	224	0.875	bicubic
147	dpn98	94.100	5.900	98.580	1.420	61.57	224	0.875	bicubic
148	efficientnet_b2_pruned	94.090	5.910	98.520	1.480	8.31	260	0.890	bicubic
149	hrnet_w64	94.030	5.970	98.490	1.510	128.06	224	0.875	bilinear
150	resnext50_32x4d	94.010	5.990	98.390	1.610	25.03	224	0.875	bicubic
151	resnetblur50	93.980	6.020	98.520	1.480	25.56	224	0.875	bicubic
152	tf_efficientnet_cc_b1_8e	93.960	6.040	98.240	1.760	39.72	240	0.882	bicubic
153	dla102x2	93.940	6.060	98.560	1.440	41.28	224	0.875	bilinear
154	regnetx_064	93.940	6.060	98.670	1.330	26.21	224	0.875	bicubic
155	regnety_040	93.930	6.070	98.690	1.310	20.65	224	0.875	bicubic
156	resnet50	93.930	6.070	98.340	1.660	25.56	224	0.875	bicubic
157	repvgg_b2g4	93.920	6.080	98.600	1.400	61.76	224	0.875	bilinear
158	regnetx_080	93.900	6.100	98.540	1.460	39.57	224	0.875	bicubic
159	efficientnet_em	93.900	6.100	98.820	1.180	6.90	240	0.882	bicubic
160	dpn131	93.880	6.120	98.740	1.260	79.25	224	0.875	bicubic
161	wide_resnet101_2	93.880	6.120	98.570	1.430	126.89	224	0.875	bilinear
162	resnext101_32x8d	93.860	6.140	98.650	1.350	88.79	224	0.875	bilinear
163	rexnet_130	93.840	6.160	98.400	1.600	7.56	224	0.875	bicubic
164	hrnet_w48	93.830	6.170	98.630	1.370	77.47	224	0.875	bilinear
165	gluon_resnet101_v1c	93.790	6.210	98.400	1.600	44.57	224	0.875	bicubic
166	gluon_resnet101_v1b	93.760	6.240	98.470	1.530	44.55	224	0.875	bicubic
167	xception65	93.760	6.240	98.350	1.650	39.92	299	0.903	bicubic
168	cspresnet50	93.750	6.250	98.670	1.330	21.62	256	0.887	bilinear
169	legacy_seresnext50_32x4d	93.740	6.260	98.510	1.490	27.56	224	0.875	bilinear
170	gluon_resnext50_32x4d	93.720	6.280	98.470	1.530	25.03	224	0.875	bicubic
171	gluon_resnet50_v1d	93.710	6.290	98.390	1.610	25.58	224	0.875	bicubic
172	gluon_resnet50_v1s	93.700	6.300	98.470	1.530	25.68	224	0.875	bicubic
173	tf_efficientnet_b1_ap	93.700	6.300	98.370	1.630	7.79	240	0.882	bicubic
174	hrnet_w44	93.660	6.340	98.690	1.310	67.06	224	0.875	bilinear
175	res2net50_26w_8s	93.650	6.350	98.270	1.730	48.40	224	0.875	bilinear
176	res2net101_26w_4s	93.650	6.350	98.400	1.600	45.21	224	0.875	bilinear
177	dpn68b	93.600	6.400	98.510	1.490	12.61	224	0.875	bicubic
178	dla102x	93.560	6.440	98.560	1.440	26.31	224	0.875	bilinear
179	regnetx_040	93.550	6.450	98.460	1.540	22.12	224	0.875	bicubic
180	res2net50_26w_6s	93.530	6.470	98.430	1.570	37.05	224	0.875	bilinear
181	tf_efficientnet_b0_ns	93.520	6.480	98.620	1.380	5.29	224	0.875	bicubic
182	tf_efficientnet_b1	93.490	6.510	98.340	1.660	7.79	240	0.882	bicubic
183	hrnet_w32	93.470	6.530	98.390	1.610	41.23	224	0.875	bilinear
184	gluon_inception_v3	93.460	6.540	98.600	1.400	23.83	299	0.875	bicubic
185	xception41	93.460	6.540	98.410	1.590	26.97	299	0.903	bicubic
186	hrnet_w40	93.450	6.550	98.570	1.430	57.56	224	0.875	bilinear
187	repvgg_b2	93.440	6.560	98.700	1.300	89.02	224	0.875	bilinear
188	resnest26d	93.440	6.560	98.600	1.400	17.07	224	0.875	bilinear
189	mixnet_l	93.440	6.560	98.270	1.730	7.33	224	0.875	bicubic
190	repvgg_b1	93.410	6.590	98.530	1.470	57.42	224	0.875	bilinear
191	dla169	93.390	6.610	98.550	1.450	53.39	224	0.875	bilinear
192	xception	93.380	6.620	98.520	1.480	22.86	299	0.897	bicubic
193	tf_mixnet_l	93.360	6.640	98.040	1.960	7.33	224	0.875	bicubic
194	legacy_seresnet152	93.330	6.670	98.380	1.620	66.82	224	0.875	bilinear
195	tv_resnet152	93.330	6.670	98.370	1.630	60.19	224	0.875	bilinear
196	selecsls60b	93.300	6.700	98.310	1.690	32.77	224	0.875	bicubic
197	legacy_seresnet101	93.280	6.720	98.470	1.530	49.33	224	0.875	bilinear
198	tf_inception_v3	93.270	6.730	98.050	1.950	23.83	299	0.875	bicubic
199	efficientnet_b1	93.250	6.750	98.170	1.830	7.79	240	0.875	bicubic
200	regnety_016	93.250	6.750	98.400	1.600	11.20	224	0.875	bicubic
201	dla60_res2net	93.190	6.810	98.390	1.610	20.85	224	0.875	bilinear
202	dla60_res2next	93.170	6.830	98.430	1.570	17.03	224	0.875	bilinear
203	dla60x	93.130	6.870	98.520	1.480	17.35	224	0.875	bilinear
204	hrnet_w30	93.130	6.870	98.400	1.600	37.71	224	0.875	bilinear
205	dla102	93.090	6.910	98.490	1.510	33.27	224	0.875	bilinear
206	rexnet_100	93.080	6.920	98.150	1.850	4.80	224	0.875	bicubic
207	efficientnet_es	93.070	6.930	98.480	1.520	5.44	224	0.875	bicubic
208	regnetx_032	93.060	6.940	98.340	1.660	15.30	224	0.875	bicubic
209	gluon_resnet50_v1c	93.030	6.970	98.330	1.670	25.58	224	0.875	bicubic
210	legacy_seresnet50	93.010	6.990	98.180	1.820	28.09	224	0.875	bilinear
211	selecsls60	92.960	7.040	98.220	1.780	30.67	224	0.875	bicubic
212	tf_efficientnet_em	92.940	7.060	98.190	1.810	6.90	240	0.882	bicubic
213	adv_inception_v3	92.890	7.110	98.170	1.830	23.83	299	0.875	bicubic
214	res2next50	92.890	7.110	98.190	1.810	24.67	224	0.875	bilinear
215	repvgg_b1g4	92.820	7.180	98.380	1.620	39.97	224	0.875	bilinear
216	tf_efficientnet_cc_b0_8e	92.820	7.180	98.260	1.740	24.01	224	0.875	bicubic
217	tv_resnet101	92.810	7.190	98.260	1.740	44.55	224	0.875	bilinear
218	res2net50_14w_8s	92.800	7.200	98.170	1.830	25.06	224	0.875	bilinear
219	resnet34d	92.770	7.230	98.260	1.740	21.82	224	0.875	bicubic
220	tv_resnext50_32x4d	92.770	7.230	98.200	1.800	25.03	224	0.875	bilinear
221	efficientnet_b1_pruned	92.760	7.240	98.040	1.960	6.33	240	0.882	bicubic
222	vit_small_patch16_224	92.730	7.270	97.920	2.080	48.75	224	0.900	bicubic
223	inception_v3	92.710	7.290	97.960	2.040	23.83	299	0.875	bicubic
224	seresnext26t_32x4d	92.700	7.300	98.380	1.620	16.81	224	0.875	bicubic
225	tf_efficientnet_lite2	92.690	7.310	98.220	1.780	6.09	260	0.890	bicubic
226	densenet161	92.680	7.320	98.350	1.650	28.68	224	0.875	bicubic
227	seresnext26d_32x4d	92.680	7.320	98.130	1.870	16.81	224	0.875	bicubic
228	densenet201	92.670	7.330	98.260	1.740	20.01	224	0.875	bicubic
229	gluon_resnet50_v1b	92.620	7.380	98.170	1.830	25.56	224	0.875	bicubic
230	tf_efficientnet_lite1	92.600	7.400	98.050	1.950	5.42	240	0.882	bicubic
231	res2net50_48w_2s	92.580	7.420	98.070	1.930	25.29	224	0.875	bilinear
232	tf_efficientnet_cc_b0_4e	92.560	7.440	98.140	1.860	13.31	224	0.875	bicubic
233	efficientnet_b0	92.550	7.450	98.080	1.920	5.29	224	0.875	bicubic
234	legacy_seresnext26_32x4d	92.520	7.480	98.150	1.850	16.79	224	0.875	bicubic
235	res2net50_26w_4s	92.480	7.520	98.140	1.860	25.70	224	0.875	bilinear
236	mobilenetv2_120d	92.470	7.530	98.000	2.000	5.83	224	0.875	bicubic
237	mixnet_m	92.450	7.550	97.870	2.130	5.01	224	0.875	bicubic
238	skresnet34	92.410	7.590	98.220	1.780	22.28	224	0.875	bicubic
239	tf_mixnet_m	92.380	7.620	97.910	2.090	5.01	224	0.875	bicubic
240	hrnet_w18	92.320	7.680	98.310	1.690	21.30	224	0.875	bilinear
241	ese_vovnet19b_dw	92.300	7.700	98.080	1.920	6.54	224	0.875	bicubic
242	tf_efficientnet_b0	92.260	7.740	97.900	2.100	5.29	224	0.875	bicubic
243	selecsls42b	92.240	7.760	98.200	1.800	32.46	224	0.875	bicubic
244	tf_efficientnet_b0_ap	92.180	7.820	98.050	1.950	5.29	224	0.875	bicubic
245	dla60	92.140	7.860	98.060	1.940	22.04	224	0.875	bilinear
246	regnetx_016	92.140	7.860	98.280	1.720	9.19	224	0.875	bicubic
247	repvgg_a2	92.120	7.880	98.200	1.800	28.21	224	0.875	bilinear
248	densenet169	92.080	7.920	98.170	1.830	14.15	224	0.875	bicubic
249	resnet26d	92.080	7.920	98.000	2.000	16.01	224	0.875	bicubic
250	dpn68	92.060	7.940	98.060	1.940	12.61	224	0.875	bicubic
251	tf_efficientnet_es	92.000	8.000	97.890	2.110	5.44	224	0.875	bicubic
252	gernet_s	91.960	8.040	98.210	1.790	8.17	224	0.875	bilinear
253	tv_resnet50	91.910	8.090	98.060	1.940	25.56	224	0.875	bilinear
254	mobilenetv2_140	91.860	8.140	97.870	2.130	6.11	224	0.875	bicubic
255	regnety_008	91.860	8.140	98.190	1.810	6.26	224	0.875	bicubic
256	mixnet_s	91.830	8.170	97.690	2.310	4.13	224	0.875	bicubic
257	densenetblur121d	91.750	8.250	98.100	1.900	8.00	224	0.875	bicubic
258	densenet121	91.700	8.300	98.010	1.990	7.98	224	0.875	bicubic
259	resnest14d	91.530	8.470	97.950	2.050	10.61	224	0.875	bilinear
260	repvgg_b0	91.420	8.580	97.920	2.080	15.82	224	0.875	bilinear
261	tf_mixnet_s	91.420	8.580	97.610	2.390	4.13	224	0.875	bicubic
262	hrnet_w18_small_v2	91.340	8.660	97.950	2.050	15.60	224	0.875	bilinear
263	regnety_006	91.280	8.720	97.810	2.190	6.06	224	0.875	bicubic
264	semnasnet_100	91.260	8.740	97.560	2.440	3.89	224	0.875	bicubic
265	mobilenetv3_rw	91.200	8.800	97.650	2.350	5.48	224	0.875	bicubic
266	resnet34	91.180	8.820	97.550	2.450	21.80	224	0.875	bilinear
267	resnet26	91.180	8.820	97.800	2.200	16.00	224	0.875	bicubic
268	regnetx_008	91.180	8.820	97.730	2.270	7.26	224	0.875	bicubic
269	mobilenetv3_large_100	91.120	8.880	97.700	2.300	5.48	224	0.875	bicubic
270	tf_mobilenetv3_large_100	91.110	8.890	97.670	2.330	5.48	224	0.875	bilinear
271	efficientnet_lite0	91.070	8.930	97.660	2.340	4.65	224	0.875	bicubic
272	tf_efficientnet_lite0	91.020	8.980	97.420	2.580	4.65	224	0.875	bicubic
273	tv_densenet121	90.980	9.020	97.740	2.260	7.98	224	0.875	bicubic
274	gluon_resnet34_v1b	90.970	9.030	97.640	2.360	21.80	224	0.875	bicubic
275	legacy_seresnet34	90.970	9.030	97.610	2.390	21.96	224	0.875	bilinear
276	mobilenetv2_110d	90.860	9.140	97.610	2.390	4.52	224	0.875	bicubic
277	fbnetc_100	90.850	9.150	97.240	2.760	5.57	224	0.875	bilinear
278	dla34	90.790	9.210	97.590	2.410	15.74	224	0.875	bilinear
279	swsl_resnet18	90.770	9.230	97.770	2.230	11.69	224	0.875	bilinear
280	vit_deit_tiny_distilled_patch16_224	90.670	9.330	97.560	2.440	5.91	224	0.900	bicubic
281	regnety_004	90.480	9.520	97.510	2.490	4.34	224	0.875	bicubic
282	mnasnet_100	90.460	9.540	97.490	2.510	4.38	224	0.875	bicubic
283	regnetx_006	90.380	9.620	97.320	2.680	6.20	224	0.875	bicubic
284	spnasnet_100	90.370	9.630	97.160	2.840	4.42	224	0.875	bilinear
285	ssl_resnet18	90.250	9.750	97.620	2.380	11.69	224	0.875	bilinear
286	vgg19_bn	90.090	9.910	97.610	2.390	143.68	224	0.875	bilinear
287	vgg16_bn	90.040	9.960	97.360	2.640	138.37	224	0.875	bilinear
288	tv_resnet34	89.970	10.030	97.390	2.610	21.80	224	0.875	bilinear
289	skresnet18	89.760	10.240	97.320	2.680	11.96	224	0.875	bicubic
290	tf_mobilenetv3_large_075	89.660	10.340	97.280	2.720	3.99	224	0.875	bilinear
291	vit_deit_tiny_patch16_224	89.650	10.350	97.440	2.560	5.72	224	0.900	bicubic
292	mobilenetv2_100	89.460	10.540	97.200	2.800	3.50	224	0.875	bicubic
293	resnet18d	89.150	10.850	97.220	2.780	11.71	224	0.875	bicubic
294	vgg19	89.120	10.880	96.900	3.100	143.67	224	0.875	bilinear
295	regnetx_004	89.030	10.970	97.090	2.910	5.16	224	0.875	bicubic
296	tf_mobilenetv3_large_minimal_100	88.940	11.060	96.810	3.190	3.92	224	0.875	bilinear
297	legacy_seresnet18	88.910	11.090	96.990	3.010	11.78	224	0.875	bicubic
298	vgg13_bn	88.840	11.160	96.980	3.020	133.05	224	0.875	bilinear
299	hrnet_w18_small	88.810	11.190	96.990	3.010	13.19	224	0.875	bilinear
300	vgg16	88.640	11.360	96.780	3.220	138.36	224	0.875	bilinear
301	gluon_resnet18_v1b	88.280	11.720	96.640	3.360	11.69	224	0.875	bicubic
302	vgg11_bn	87.560	12.440	96.800	3.200	132.87	224	0.875	bilinear
303	regnety_002	87.400	12.600	96.670	3.330	3.16	224	0.875	bicubic
304	resnet18	87.400	12.600	96.280	3.720	11.69	224	0.875	bilinear
305	vgg13	87.060	12.940	96.240	3.760	133.05	224	0.875	bilinear
306	vgg11	86.560	13.440	96.220	3.780	132.86	224	0.875	bilinear
307	dla60x_c	86.260	13.740	96.100	3.900	1.32	224	0.875	bilinear
308	regnetx_002	86.150	13.850	96.020	3.980	2.68	224	0.875	bicubic
309	tf_mobilenetv3_small_100	85.350	14.650	95.640	4.360	2.54	224	0.875	bilinear
310	dla46x_c	84.430	15.570	95.340	4.660	1.07	224	0.875	bilinear
311	dla46_c	83.560	16.440	94.890	5.110	1.30	224	0.875	bilinear
312	tf_mobilenetv3_small_075	83.470	16.530	94.700	5.300	2.04	224	0.875	bilinear
313	tf_mobilenetv3_small_minimal_100	81.550	18.450	93.710	6.290	2.04	224	0.875	bilinear