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	97.760	2.240	99.880	0.120	480.31	800	0.960	bicubic
3	tf_efficientnet_l2_ns_475	97.760	2.240	99.820	0.180	480.31	475	0.936	bicubic
4	tf_efficientnet_b7_ns	97.200	2.800	99.700	0.300	66.35	600	0.949	bicubic
5	tf_efficientnet_b6_ns	97.010	2.990	99.710	0.290	43.04	528	0.942	bicubic
6	ig_resnext101_32x48d	96.980	3.020	99.660	0.340	828.41	224	0.875	bilinear
7	dm_nfnet_f6	96.970	3.030	99.740	0.260	438.36	576	0.956	bicubic
8	tf_efficientnet_b5_ns	96.880	3.120	99.640	0.360	30.39	456	0.934	bicubic
9	resnetv2_152x4_bitm	96.860	3.140	99.660	0.340	936.53	480	1.000	bilinear
10	ig_resnext101_32x32d	96.850	3.150	99.520	0.480	468.53	224	0.875	bilinear
11	dm_nfnet_f4	96.800	3.200	99.640	0.360	316.07	512	0.951	bicubic
12	dm_nfnet_f5	96.720	3.280	99.680	0.320	377.21	544	0.954	bicubic
13	tf_efficientnet_b8	96.700	3.300	99.520	0.480	87.41	672	0.954	bicubic
14	tf_efficientnet_b4_ns	96.680	3.320	99.680	0.320	19.34	380	0.922	bicubic
15	dm_nfnet_f3	96.660	3.340	99.640	0.360	254.92	416	0.940	bicubic
16	tf_efficientnet_b7	96.580	3.420	99.520	0.480	66.35	600	0.949	bicubic
17	tf_efficientnet_b8_ap	96.560	3.440	99.540	0.460	87.41	672	0.954	bicubic
18	resnetv2_152x2_bitm	96.510	3.490	99.620	0.380	236.34	480	1.000	bilinear
19	vit_deit_base_distilled_patch16_384	96.490	3.510	99.590	0.410	87.63	384	1.000	bicubic
20	vit_base_resnet50_384	96.460	3.540	99.670	0.330	98.95	384	1.000	bicubic
21	dm_nfnet_f2	96.460	3.540	99.560	0.440	193.78	352	0.920	bicubic
22	ecaresnet269d	96.420	3.580	99.640	0.360	102.09	352	1.000	bicubic
23	ig_resnext101_32x16d	96.380	3.620	99.560	0.440	194.03	224	0.875	bilinear
24	vit_large_patch16_384	96.380	3.620	99.640	0.360	304.72	384	1.000	bicubic
25	tf_efficientnet_b6_ap	96.370	3.630	99.550	0.450	43.04	528	0.942	bicubic
26	resnetv2_101x3_bitm	96.350	3.650	99.600	0.400	387.93	480	1.000	bilinear
27	tf_efficientnet_b7_ap	96.350	3.650	99.590	0.410	66.35	600	0.949	bicubic
28	dm_nfnet_f1	96.340	3.660	99.460	0.540	132.63	320	0.910	bicubic
29	tf_efficientnet_b6	96.320	3.680	99.530	0.470	43.04	528	0.942	bicubic
30	swsl_resnext101_32x16d	96.290	3.710	99.540	0.460	194.03	224	0.875	bilinear
31	seresnet152d	96.280	3.720	99.510	0.490	66.84	320	1.000	bicubic
32	swsl_resnext101_32x8d	96.270	3.730	99.590	0.410	88.79	224	0.875	bilinear
33	vit_base_patch16_384	96.190	3.810	99.550	0.450	86.86	384	1.000	bicubic
34	resnest269e	96.140	3.860	99.540	0.460	110.93	416	0.928	bicubic
35	resnet200d	96.130	3.870	99.440	0.560	64.69	320	1.000	bicubic
36	resnetv2_50x3_bitm	96.130	3.870	99.620	0.380	217.32	480	1.000	bilinear
37	resnest200e	96.080	3.920	99.450	0.550	70.20	320	0.909	bicubic
38	tf_efficientnet_b5_ap	96.080	3.920	99.540	0.460	30.39	456	0.934	bicubic
39	tf_efficientnet_b3_ns	96.040	3.960	99.460	0.540	12.23	300	0.904	bicubic
40	swsl_resnext101_32x4d	96.030	3.970	99.540	0.460	44.18	224	0.875	bilinear
41	tf_efficientnet_b5	95.970	4.030	99.460	0.540	30.39	456	0.934	bicubic
42	ig_resnext101_32x8d	95.880	4.120	99.350	0.650	88.79	224	0.875	bilinear
43	resnet152d	95.870	4.130	99.420	0.580	60.21	320	1.000	bicubic
44	resnet101d	95.740	4.260	99.420	0.580	44.57	320	1.000	bicubic
45	vit_deit_base_distilled_patch16_224	95.730	4.270	99.290	0.710	87.34	224	0.900	bicubic
46	dm_nfnet_f0	95.650	4.350	99.300	0.700	71.49	256	0.900	bicubic
47	swsl_resnext50_32x4d	95.630	4.370	99.450	0.550	25.03	224	0.875	bilinear
48	vit_deit_base_patch16_384	95.620	4.380	99.240	0.760	86.86	384	1.000	bicubic
49	tf_efficientnet_b4	95.580	4.420	99.290	0.710	19.34	380	0.922	bicubic
50	resnest101e	95.560	4.440	99.280	0.720	48.28	256	0.875	bilinear
51	tresnet_xl_448	95.520	4.480	99.340	0.660	78.44	448	0.875	bilinear
52	tf_efficientnet_b4_ap	95.520	4.480	99.420	0.580	19.34	380	0.922	bicubic
53	resnetv2_101x1_bitm	95.510	4.490	99.520	0.480	44.54	480	1.000	bilinear
54	ssl_resnext101_32x16d	95.470	4.530	99.410	0.590	194.03	224	0.875	bilinear
55	tf_efficientnet_b2_ns	95.460	4.540	99.390	0.610	9.11	260	0.890	bicubic
56	tresnet_l_448	95.420	4.580	99.300	0.700	55.99	448	0.875	bilinear
57	pnasnet5large	95.370	4.630	99.120	0.880	86.06	331	0.911	bicubic
58	nasnetalarge	95.320	4.680	99.140	0.860	88.75	331	0.911	bicubic
59	swsl_resnet50	95.290	4.710	99.350	0.650	25.56	224	0.875	bilinear
60	vit_base_patch16_224	95.290	4.710	99.230	0.770	86.57	224	0.900	bicubic
61	ssl_resnext101_32x8d	95.290	4.710	99.310	0.690	88.79	224	0.875	bilinear
62	vit_large_patch16_224	95.280	4.720	99.310	0.690	304.33	224	0.900	bicubic
63	vit_base_patch32_384	95.270	4.730	99.190	0.810	88.30	384	1.000	bicubic
64	vit_large_patch32_384	95.240	4.760	99.310	0.690	306.63	384	1.000	bicubic
65	ecaresnet101d	95.210	4.790	99.240	0.760	44.57	224	0.875	bicubic
66	efficientnet_b3a	95.170	4.830	99.220	0.780	12.23	320	1.000	bicubic
67	ssl_resnext101_32x4d	95.150	4.850	99.320	0.680	44.18	224	0.875	bilinear
68	regnety_032	95.130	4.870	99.220	0.780	19.44	224	0.875	bicubic
69	ecaresnet50t	95.080	4.920	99.270	0.730	25.57	320	0.950	bicubic
70	tresnet_xl	94.990	5.010	99.220	0.780	78.44	224	0.875	bilinear
71	vit_deit_base_patch16_224	94.970	5.030	99.000	1.000	86.57	224	0.900	bicubic
72	gernet_l	94.940	5.060	99.180	0.820	31.08	256	0.875	bilinear
73	tf_efficientnet_b3	94.900	5.100	99.140	0.860	12.23	300	0.904	bicubic
74	efficientnet_b3	94.890	5.110	99.190	0.810	12.23	300	0.904	bicubic
75	tf_efficientnet_b3_ap	94.890	5.110	99.130	0.870	12.23	300	0.904	bicubic
76	tf_efficientnet_lite4	94.880	5.120	99.110	0.890	13.01	380	0.920	bilinear
77	tf_efficientnet_b1_ns	94.850	5.150	99.240	0.760	7.79	240	0.882	bicubic
78	tresnet_l	94.830	5.170	99.060	0.940	55.99	224	0.875	bilinear
79	seresnext50_32x4d	94.770	5.230	99.120	0.880	27.56	224	0.875	bicubic
80	legacy_senet154	94.760	5.240	99.100	0.900	115.09	224	0.875	bilinear
81	resnest50d_4s2x40d	94.720	5.280	99.130	0.870	30.42	224	0.875	bicubic
82	ssl_resnext50_32x4d	94.720	5.280	99.190	0.810	25.03	224	0.875	bilinear
83	wide_resnet50_2	94.690	5.310	99.090	0.910	68.88	224	0.875	bicubic
84	tresnet_m_448	94.670	5.330	99.140	0.860	31.39	448	0.875	bilinear
85	gluon_resnet152_v1s	94.650	5.350	99.070	0.930	60.32	224	0.875	bicubic
86	gluon_senet154	94.650	5.350	98.990	1.010	115.09	224	0.875	bicubic
87	repvgg_b3	94.630	5.370	98.990	1.010	123.09	224	0.875	bilinear
88	rexnet_200	94.600	5.400	99.060	0.940	16.37	224	0.875	bicubic
89	repvgg_b3g4	94.570	5.430	99.000	1.000	83.83	224	0.875	bilinear
90	vit_deit_small_distilled_patch16_224	94.570	5.430	99.100	0.900	22.44	224	0.900	bicubic
91	efficientnet_b3_pruned	94.550	5.450	99.060	0.940	9.86	300	0.904	bicubic
92	gluon_seresnext101_64x4d	94.500	5.500	98.970	1.030	88.23	224	0.875	bicubic
93	regnety_320	94.500	5.500	99.150	0.850	145.05	224	0.875	bicubic
94	resnetv2_50x1_bitm	94.500	5.500	99.260	0.740	25.55	480	1.000	bilinear
95	resnest50d	94.490	5.510	99.000	1.000	27.48	224	0.875	bilinear
96	gernet_m	94.480	5.520	98.910	1.090	21.14	224	0.875	bilinear
97	gluon_resnet152_v1d	94.470	5.530	98.990	1.010	60.21	224	0.875	bicubic
98	ecaresnet101d_pruned	94.460	5.540	99.130	0.870	24.88	224	0.875	bicubic
99	tf_efficientnet_el	94.450	5.550	99.100	0.900	10.59	300	0.904	bicubic
100	nf_resnet50	94.420	5.580	99.100	0.900	25.56	288	0.940	bicubic
101	efficientnet_b2a	94.400	5.600	99.050	0.950	9.11	288	1.000	bicubic
102	gluon_seresnext101_32x4d	94.400	5.600	99.110	0.890	48.96	224	0.875	bicubic
103	inception_v4	94.390	5.610	98.830	1.170	42.68	299	0.875	bicubic
104	tf_efficientnet_b2_ap	94.360	5.640	98.970	1.030	9.11	260	0.890	bicubic
105	gluon_resnext101_64x4d	94.350	5.650	98.830	1.170	83.46	224	0.875	bicubic
106	efficientnet_b2	94.340	5.660	99.120	0.880	9.11	260	0.875	bicubic
107	resnest50d_1s4x24d	94.340	5.660	99.060	0.940	25.68	224	0.875	bicubic
108	gluon_resnet101_v1s	94.290	5.710	99.040	0.960	44.67	224	0.875	bicubic
109	rexnet_150	94.280	5.720	99.070	0.930	9.73	224	0.875	bicubic
110	regnety_160	94.280	5.720	99.020	0.980	83.59	224	0.875	bicubic
111	inception_resnet_v2	94.260	5.740	98.830	1.170	55.84	299	0.897	bicubic
112	mixnet_xl	94.250	5.750	98.840	1.160	11.90	224	0.875	bicubic
113	ssl_resnet50	94.250	5.750	99.080	0.920	25.56	224	0.875	bilinear
114	dpn92	94.240	5.760	98.950	1.050	37.67	224	0.875	bicubic
115	regnetx_120	94.240	5.760	99.190	0.810	46.11	224	0.875	bicubic
116	regnetx_320	94.230	5.770	99.060	0.940	107.81	224	0.875	bicubic
117	gluon_resnext101_32x4d	94.190	5.810	98.920	1.080	44.18	224	0.875	bicubic
118	tf_efficientnet_b2	94.190	5.810	99.030	0.970	9.11	260	0.890	bicubic
119	tf_efficientnet_lite3	94.140	5.860	98.940	1.060	8.20	300	0.904	bilinear
120	ecaresnet50d	94.140	5.860	99.030	0.970	25.58	224	0.875	bicubic
121	ecaresnetlight	94.120	5.880	98.940	1.060	30.16	224	0.875	bicubic
122	gluon_resnet101_v1d	94.120	5.880	98.920	1.080	44.57	224	0.875	bicubic
123	legacy_seresnext101_32x4d	94.110	5.890	98.950	1.050	48.96	224	0.875	bilinear
124	regnety_064	94.110	5.890	99.020	0.980	30.58	224	0.875	bicubic
125	resnet50d	94.110	5.890	98.960	1.040	25.58	224	0.875	bicubic
126	seresnet50	94.100	5.900	98.980	1.020	28.09	224	0.875	bicubic
127	cspdarknet53	94.090	5.910	98.970	1.030	27.64	256	0.887	bilinear
128	ens_adv_inception_resnet_v2	94.070	5.930	98.790	1.210	55.84	299	0.897	bicubic
129	gluon_seresnext50_32x4d	94.040	5.960	98.900	1.100	27.56	224	0.875	bicubic
130	dla102x2	94.040	5.960	99.070	0.930	41.28	224	0.875	bilinear
131	tresnet_m	94.020	5.980	98.820	1.180	31.39	224	0.875	bilinear
132	gluon_xception65	94.010	5.990	99.010	0.990	39.92	299	0.903	bicubic
133	regnetx_160	94.010	5.990	99.120	0.880	54.28	224	0.875	bicubic
134	gluon_resnet152_v1b	94.000	6.000	98.770	1.230	60.19	224	0.875	bicubic
135	dpn107	93.990	6.010	98.870	1.130	86.92	224	0.875	bicubic
136	resnext50d_32x4d	93.990	6.010	98.790	1.210	25.05	224	0.875	bicubic
137	regnety_120	93.960	6.040	99.060	0.940	51.82	224	0.875	bicubic
138	hrnet_w64	93.950	6.050	98.960	1.040	128.06	224	0.875	bilinear
139	ese_vovnet39b	93.940	6.060	98.930	1.070	24.57	224	0.875	bicubic
140	hrnet_w48	93.930	6.070	99.020	0.980	77.47	224	0.875	bilinear
141	ecaresnet26t	93.920	6.080	98.940	1.060	16.01	320	0.950	bicubic
142	efficientnet_b2_pruned	93.920	6.080	98.910	1.090	8.31	260	0.890	bicubic
143	regnetx_080	93.910	6.090	98.910	1.090	39.57	224	0.875	bicubic
144	dpn98	93.900	6.100	98.930	1.070	61.57	224	0.875	bicubic
145	gluon_resnet152_v1c	93.900	6.100	98.850	1.150	60.21	224	0.875	bicubic
146	skresnext50_32x4d	93.900	6.100	98.800	1.200	27.48	224	0.875	bicubic
147	xception71	93.890	6.110	98.950	1.050	42.34	299	0.903	bicubic
148	vit_deit_small_patch16_224	93.890	6.110	98.970	1.030	22.05	224	0.900	bicubic
149	repvgg_b2g4	93.890	6.110	98.920	1.080	61.76	224	0.875	bilinear
150	nf_regnet_b1	93.890	6.110	98.790	1.210	10.22	288	0.900	bicubic
151	resnext50_32x4d	93.850	6.150	98.790	1.210	25.03	224	0.875	bicubic
152	cspresnet50	93.840	6.160	98.900	1.100	21.62	256	0.887	bilinear
153	ecaresnet50d_pruned	93.840	6.160	98.980	1.020	19.94	224	0.875	bicubic
154	wide_resnet101_2	93.840	6.160	98.800	1.200	126.89	224	0.875	bilinear
155	cspresnext50	93.820	6.180	98.840	1.160	20.57	224	0.875	bilinear
156	resnext101_32x8d	93.820	6.180	98.950	1.050	88.79	224	0.875	bilinear
157	xception65	93.810	6.190	98.850	1.150	39.92	299	0.903	bicubic
158	regnety_080	93.800	6.200	99.000	1.000	39.18	224	0.875	bicubic
159	dpn131	93.770	6.230	98.800	1.200	79.25	224	0.875	bicubic
160	gluon_resnet101_v1c	93.750	6.250	98.800	1.200	44.57	224	0.875	bicubic
161	gluon_resnet101_v1b	93.750	6.250	98.720	1.280	44.55	224	0.875	bicubic
162	efficientnet_em	93.730	6.270	98.940	1.060	6.90	240	0.882	bicubic
163	resnetblur50	93.730	6.270	98.810	1.190	25.56	224	0.875	bicubic
164	regnetx_040	93.710	6.290	98.880	1.120	22.12	224	0.875	bicubic
165	tf_efficientnet_b1	93.710	6.290	98.800	1.200	7.79	240	0.882	bicubic
166	gluon_resnext50_32x4d	93.700	6.300	98.700	1.300	25.03	224	0.875	bicubic
167	tf_efficientnet_b1_ap	93.680	6.320	98.810	1.190	7.79	240	0.882	bicubic
168	xception	93.680	6.320	98.820	1.180	22.86	299	0.897	bicubic
169	dla169	93.670	6.330	98.890	1.110	53.39	224	0.875	bilinear
170	hrnet_w44	93.660	6.340	98.930	1.070	67.06	224	0.875	bilinear
171	regnetx_064	93.640	6.360	99.040	0.960	26.21	224	0.875	bicubic
172	rexnet_130	93.620	6.380	98.720	1.280	7.56	224	0.875	bicubic
173	dpn68b	93.610	6.390	98.770	1.230	12.61	224	0.875	bicubic
174	regnety_040	93.600	6.400	98.990	1.010	20.65	224	0.875	bicubic
175	repvgg_b2	93.590	6.410	99.090	0.910	89.02	224	0.875	bilinear
176	res2net50_26w_8s	93.590	6.410	98.710	1.290	48.40	224	0.875	bilinear
177	res2net101_26w_4s	93.590	6.410	98.700	1.300	45.21	224	0.875	bilinear
178	tf_efficientnet_b0_ns	93.580	6.420	99.010	0.990	5.29	224	0.875	bicubic
179	gluon_inception_v3	93.580	6.420	98.830	1.170	23.83	299	0.875	bicubic
180	gluon_resnet50_v1s	93.560	6.440	98.820	1.180	25.68	224	0.875	bicubic
181	legacy_seresnext50_32x4d	93.560	6.440	98.750	1.250	27.56	224	0.875	bilinear
182	tf_efficientnet_cc_b1_8e	93.560	6.440	98.680	1.320	39.72	240	0.882	bicubic
183	res2net50_26w_6s	93.550	6.450	98.800	1.200	37.05	224	0.875	bilinear
184	dla102x	93.530	6.470	98.860	1.140	26.31	224	0.875	bilinear
185	gluon_resnet50_v1d	93.530	6.470	98.830	1.170	25.58	224	0.875	bicubic
186	hrnet_w40	93.520	6.480	98.810	1.190	57.56	224	0.875	bilinear
187	xception41	93.510	6.490	98.750	1.250	26.97	299	0.903	bicubic
188	resnet50	93.500	6.500	98.580	1.420	25.56	224	0.875	bicubic
189	selecsls60b	93.450	6.550	98.850	1.150	32.77	224	0.875	bicubic
190	dla60_res2net	93.440	6.560	98.870	1.130	20.85	224	0.875	bilinear
191	repvgg_b1	93.440	6.560	98.740	1.260	57.42	224	0.875	bilinear
192	dla60_res2next	93.370	6.630	98.820	1.180	17.03	224	0.875	bilinear
193	hrnet_w30	93.370	6.630	98.880	1.120	37.71	224	0.875	bilinear
194	regnetx_032	93.350	6.650	98.660	1.340	15.30	224	0.875	bicubic
195	legacy_seresnet152	93.330	6.670	98.760	1.240	66.82	224	0.875	bilinear
196	dla102	93.270	6.730	98.800	1.200	33.27	224	0.875	bilinear
197	legacy_seresnet101	93.250	6.750	98.760	1.240	49.33	224	0.875	bilinear
198	tv_resnet152	93.220	6.780	98.790	1.210	60.19	224	0.875	bilinear
199	res2net50_26w_4s	93.210	6.790	98.630	1.370	25.70	224	0.875	bilinear
200	tf_inception_v3	93.200	6.800	98.510	1.490	23.83	299	0.875	bicubic
201	res2next50	93.180	6.820	98.670	1.330	24.67	224	0.875	bilinear
202	tf_efficientnet_em	93.160	6.840	98.670	1.330	6.90	240	0.882	bicubic
203	mixnet_l	93.150	6.850	98.680	1.320	7.33	224	0.875	bicubic
204	resnest26d	93.150	6.850	98.900	1.100	17.07	224	0.875	bilinear
205	efficientnet_b1	93.100	6.900	98.590	1.410	7.79	240	0.875	bicubic
206	dla60x	93.080	6.920	98.710	1.290	17.35	224	0.875	bilinear
207	regnety_016	93.070	6.930	98.740	1.260	11.20	224	0.875	bicubic
208	efficientnet_es	93.010	6.990	98.740	1.260	5.44	224	0.875	bicubic
209	tf_mixnet_l	93.010	6.990	98.540	1.460	7.33	224	0.875	bicubic
210	efficientnet_b1_pruned	93.000	7.000	98.520	1.480	6.33	240	0.882	bicubic
211	hrnet_w32	93.000	7.000	98.860	1.140	41.23	224	0.875	bilinear
212	densenet161	92.990	7.010	98.780	1.220	28.68	224	0.875	bicubic
213	gluon_resnet50_v1c	92.990	7.010	98.640	1.360	25.58	224	0.875	bicubic
214	res2net50_14w_8s	92.980	7.020	98.620	1.380	25.06	224	0.875	bilinear
215	selecsls60	92.980	7.020	98.750	1.250	30.67	224	0.875	bicubic
216	inception_v3	92.970	7.030	98.330	1.670	23.83	299	0.875	bicubic
217	adv_inception_v3	92.960	7.040	98.500	1.500	23.83	299	0.875	bicubic
218	repvgg_b1g4	92.940	7.060	98.800	1.200	39.97	224	0.875	bilinear
219	tv_resnet101	92.940	7.060	98.640	1.360	44.55	224	0.875	bilinear
220	tv_resnext50_32x4d	92.940	7.060	98.720	1.280	25.03	224	0.875	bilinear
221	hrnet_w18	92.860	7.140	98.690	1.310	21.30	224	0.875	bilinear
222	tf_efficientnet_cc_b0_8e	92.850	7.150	98.480	1.520	24.01	224	0.875	bicubic
223	legacy_seresnet50	92.820	7.180	98.650	1.350	28.09	224	0.875	bilinear
224	mobilenetv2_120d	92.770	7.230	98.480	1.520	5.83	224	0.875	bicubic
225	rexnet_100	92.760	7.240	98.630	1.370	4.80	224	0.875	bicubic
226	tf_efficientnet_cc_b0_4e	92.740	7.260	98.520	1.480	13.31	224	0.875	bicubic
227	seresnext26t_32x4d	92.740	7.260	98.550	1.450	16.81	224	0.875	bicubic
228	gluon_resnet50_v1b	92.730	7.270	98.560	1.440	25.56	224	0.875	bicubic
229	dla60	92.710	7.290	98.650	1.350	22.04	224	0.875	bilinear
230	repvgg_a2	92.700	7.300	98.600	1.400	28.21	224	0.875	bilinear
231	res2net50_48w_2s	92.670	7.330	98.460	1.540	25.29	224	0.875	bilinear
232	densenet201	92.660	7.340	98.680	1.320	20.01	224	0.875	bicubic
233	regnetx_016	92.620	7.380	98.530	1.470	9.19	224	0.875	bicubic
234	vit_small_patch16_224	92.620	7.380	98.400	1.600	48.75	224	0.900	bicubic
235	resnet34d	92.610	7.390	98.430	1.570	21.82	224	0.875	bicubic
236	tf_efficientnet_lite2	92.590	7.410	98.570	1.430	6.09	260	0.890	bicubic
237	tf_efficientnet_b0_ap	92.570	7.430	98.410	1.590	5.29	224	0.875	bicubic
238	efficientnet_b0	92.550	7.450	98.640	1.360	5.29	224	0.875	bicubic
239	legacy_seresnext26_32x4d	92.530	7.470	98.450	1.550	16.79	224	0.875	bicubic
240	gernet_s	92.480	7.520	98.460	1.540	8.17	224	0.875	bilinear
241	selecsls42b	92.470	7.530	98.510	1.490	32.46	224	0.875	bicubic
242	seresnext26d_32x4d	92.440	7.560	98.550	1.450	16.81	224	0.875	bicubic
243	tf_efficientnet_b0	92.360	7.640	98.470	1.530	5.29	224	0.875	bicubic
244	skresnet34	92.340	7.660	98.520	1.480	22.28	224	0.875	bicubic
245	tf_efficientnet_lite1	92.330	7.670	98.510	1.490	5.42	240	0.882	bicubic
246	densenetblur121d	92.310	7.690	98.390	1.610	8.00	224	0.875	bicubic
247	dpn68	92.290	7.710	98.580	1.420	12.61	224	0.875	bicubic
248	tf_mixnet_m	92.270	7.730	98.490	1.510	5.01	224	0.875	bicubic
249	mixnet_m	92.210	7.790	98.360	1.640	5.01	224	0.875	bicubic
250	densenet169	92.200	7.800	98.570	1.430	14.15	224	0.875	bicubic
251	tv_resnet50	92.180	7.820	98.420	1.580	25.56	224	0.875	bilinear
252	mobilenetv2_140	92.110	7.890	98.250	1.750	6.11	224	0.875	bicubic
253	resnet26d	92.100	7.900	98.460	1.540	16.01	224	0.875	bicubic
254	ese_vovnet19b_dw	92.090	7.910	98.510	1.490	6.54	224	0.875	bicubic
255	tf_efficientnet_es	92.080	7.920	98.460	1.540	5.44	224	0.875	bicubic
256	densenet121	91.950	8.050	98.180	1.820	7.98	224	0.875	bicubic
257	regnety_008	91.810	8.190	98.410	1.590	6.26	224	0.875	bicubic
258	mixnet_s	91.800	8.200	98.330	1.670	4.13	224	0.875	bicubic
259	tf_mixnet_s	91.660	8.340	98.160	1.840	4.13	224	0.875	bicubic
260	semnasnet_100	91.620	8.380	98.310	1.690	3.89	224	0.875	bicubic
261	repvgg_b0	91.600	8.400	98.450	1.550	15.82	224	0.875	bilinear
262	regnety_006	91.570	8.430	98.390	1.610	6.06	224	0.875	bicubic
263	mobilenetv3_rw	91.550	8.450	98.340	1.660	5.48	224	0.875	bicubic
264	legacy_seresnet34	91.530	8.470	98.320	1.680	21.96	224	0.875	bilinear
265	mobilenetv3_large_100	91.490	8.510	98.310	1.690	5.48	224	0.875	bicubic
266	tv_densenet121	91.430	8.570	98.280	1.720	7.98	224	0.875	bicubic
267	tf_mobilenetv3_large_100	91.410	8.590	98.290	1.710	5.48	224	0.875	bilinear
268	resnet26	91.390	8.610	98.360	1.640	16.00	224	0.875	bicubic
269	regnetx_008	91.340	8.660	98.350	1.650	7.26	224	0.875	bicubic
270	resnet34	91.330	8.670	98.160	1.840	21.80	224	0.875	bilinear
271	hrnet_w18_small_v2	91.290	8.710	98.400	1.600	15.60	224	0.875	bilinear
272	tf_efficientnet_lite0	91.280	8.720	98.060	1.940	4.65	224	0.875	bicubic
273	dla34	91.230	8.770	98.140	1.860	15.74	224	0.875	bilinear
274	efficientnet_lite0	91.220	8.780	98.190	1.810	4.65	224	0.875	bicubic
275	mnasnet_100	91.210	8.790	98.030	1.970	4.38	224	0.875	bicubic
276	resnest14d	91.170	8.830	98.300	1.700	10.61	224	0.875	bilinear
277	fbnetc_100	91.140	8.860	97.870	2.130	5.57	224	0.875	bilinear
278	swsl_resnet18	91.130	8.870	98.230	1.770	11.69	224	0.875	bilinear
279	vgg19_bn	91.120	8.880	98.160	1.840	143.68	224	0.875	bilinear
280	vit_deit_tiny_distilled_patch16_224	91.120	8.880	98.310	1.690	5.91	224	0.900	bicubic
281	mobilenetv2_110d	91.100	8.900	98.140	1.860	4.52	224	0.875	bicubic
282	gluon_resnet34_v1b	91.020	8.980	98.140	1.860	21.80	224	0.875	bicubic
283	regnetx_006	90.920	9.080	98.070	1.930	6.20	224	0.875	bicubic
284	ssl_resnet18	90.840	9.160	98.020	1.980	11.69	224	0.875	bilinear
285	regnety_004	90.750	9.250	98.070	1.930	4.34	224	0.875	bicubic
286	spnasnet_100	90.640	9.360	97.920	2.080	4.42	224	0.875	bilinear
287	vgg16_bn	90.620	9.380	98.050	1.950	138.37	224	0.875	bilinear
288	tv_resnet34	90.390	9.610	97.930	2.070	21.80	224	0.875	bilinear
289	tf_mobilenetv3_large_075	90.360	9.640	97.840	2.160	3.99	224	0.875	bilinear
290	skresnet18	90.180	9.820	97.860	2.140	11.96	224	0.875	bicubic
291	mobilenetv2_100	89.890	10.110	97.850	2.150	3.50	224	0.875	bicubic
292	resnet18d	89.860	10.140	97.900	2.100	11.71	224	0.875	bicubic
293	vgg19	89.690	10.310	97.580	2.420	143.67	224	0.875	bilinear
294	hrnet_w18_small	89.640	10.360	97.840	2.160	13.19	224	0.875	bilinear
295	vit_deit_tiny_patch16_224	89.630	10.370	97.930	2.070	5.72	224	0.900	bicubic
296	regnetx_004	89.610	10.390	97.780	2.220	5.16	224	0.875	bicubic
297	vgg16	89.490	10.510	97.530	2.470	138.36	224	0.875	bilinear
298	legacy_seresnet18	89.290	10.710	97.790	2.210	11.78	224	0.875	bicubic
299	tf_mobilenetv3_large_minimal_100	89.290	10.710	97.380	2.620	3.92	224	0.875	bilinear
300	vgg13_bn	89.200	10.800	97.640	2.360	133.05	224	0.875	bilinear
301	gluon_resnet18_v1b	88.770	11.230	97.080	2.920	11.69	224	0.875	bicubic
302	vgg11_bn	88.310	11.690	97.290	2.710	132.87	224	0.875	bilinear
303	regnety_002	88.180	11.820	97.430	2.570	3.16	224	0.875	bicubic
304	resnet18	88.050	11.950	97.110	2.890	11.69	224	0.875	bilinear
305	vgg13	87.560	12.440	97.160	2.840	133.05	224	0.875	bilinear
306	vgg11	87.370	12.630	97.010	2.990	132.86	224	0.875	bilinear
307	regnetx_002	87.210	12.790	96.900	3.100	2.68	224	0.875	bicubic
308	dla60x_c	87.190	12.810	97.090	2.910	1.32	224	0.875	bilinear
309	tf_mobilenetv3_small_100	85.870	14.130	96.310	3.690	2.54	224	0.875	bilinear
310	dla46x_c	85.690	14.310	96.460	3.540	1.07	224	0.875	bilinear
311	tf_mobilenetv3_small_075	84.770	15.230	95.880	4.120	2.04	224	0.875	bilinear
312	dla46_c	84.690	15.310	96.220	3.780	1.30	224	0.875	bilinear
313	tf_mobilenetv3_small_minimal_100	82.770	17.230	95.080	4.920	2.04	224	0.875	bilinear