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	97.770	2.230	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	tf_efficientnet_b7_ns	97.210	2.790	99.700	0.300	66.35	600	0.949	bicubic
5	tf_efficientnet_b6_ns	97.030	2.970	99.710	0.290	43.04	528	0.942	bicubic
6	ig_resnext101_32x48d	96.960	3.040	99.670	0.330	828.41	224	0.875	bilinear
7	resnetv2_152x4_bitm	96.880	3.120	99.660	0.340	936.53	480	1.000	bilinear
8	tf_efficientnet_b5_ns	96.880	3.120	99.640	0.360	30.39	456	0.934	bicubic
9	ig_resnext101_32x32d	96.780	3.220	99.530	0.470	468.53	224	0.875	bilinear
10	tf_efficientnet_b4_ns	96.710	3.290	99.640	0.360	19.34	380	0.922	bicubic
11	tf_efficientnet_b8	96.700	3.300	99.550	0.450	87.41	672	0.954	bicubic
12	tf_efficientnet_b7	96.580	3.420	99.510	0.490	66.35	600	0.949	bicubic
13	tf_efficientnet_b8_ap	96.560	3.440	99.550	0.450	87.41	672	0.954	bicubic
14	resnetv2_152x2_bitm	96.490	3.510	99.620	0.380	236.34	480	1.000	bilinear
15	vit_base_resnet50_384	96.470	3.530	99.660	0.340	98.95	384	1.000	bicubic
16	ig_resnext101_32x16d	96.430	3.570	99.540	0.460	194.03	224	0.875	bilinear
17	resnetv2_101x3_bitm	96.380	3.620	99.600	0.400	387.93	480	1.000	bilinear
18	vit_large_patch16_384	96.360	3.640	99.630	0.370	304.72	384	1.000	bicubic
19	tf_efficientnet_b6_ap	96.360	3.640	99.550	0.450	43.04	528	0.942	bicubic
20	tf_efficientnet_b7_ap	96.350	3.650	99.600	0.400	66.35	600	0.949	bicubic
21	seresnet152d_320	96.330	3.670	99.510	0.490	66.84	320	1.000	bicubic
22	tf_efficientnet_b6	96.280	3.720	99.520	0.480	43.04	528	0.942	bicubic
23	swsl_resnext101_32x16d	96.270	3.730	99.500	0.500	194.03	224	0.875	bilinear
24	swsl_resnext101_32x8d	96.220	3.780	99.590	0.410	88.79	224	0.875	bilinear
25	resnetv2_50x3_bitm	96.170	3.830	99.620	0.380	217.32	480	1.000	bilinear
26	vit_base_patch16_384	96.170	3.830	99.520	0.480	86.86	384	1.000	bicubic
27	resnest269e	96.120	3.880	99.520	0.480	110.93	416	0.928	bicubic
28	resnet200d_320	96.110	3.890	99.460	0.540	64.69	320	1.000	bicubic
29	tf_efficientnet_b3_ns	96.110	3.890	99.470	0.530	12.23	300	0.904	bicubic
30	resnest200e	96.080	3.920	99.470	0.530	70.20	320	0.909	bicubic
31	tf_efficientnet_b5_ap	96.080	3.920	99.540	0.460	30.39	456	0.934	bicubic
32	swsl_resnext101_32x4d	96.060	3.940	99.540	0.460	44.18	224	0.875	bilinear
33	tf_efficientnet_b5	95.980	4.020	99.450	0.550	30.39	456	0.934	bicubic
34	ig_resnext101_32x8d	95.940	4.060	99.390	0.610	88.79	224	0.875	bilinear
35	seresnet152d	95.940	4.060	99.400	0.600	66.84	256	0.940	bicubic
36	resnet152d_320	95.850	4.150	99.430	0.570	60.21	320	1.000	bicubic
37	resnet101d_320	95.750	4.250	99.440	0.560	44.57	320	1.000	bicubic
38	resnet200d	95.700	4.300	99.340	0.660	64.69	256	0.940	bicubic
39	swsl_resnext50_32x4d	95.610	4.390	99.440	0.560	25.03	224	0.875	bilinear
40	resnest101e	95.590	4.410	99.270	0.730	48.28	256	0.875	bilinear
41	tf_efficientnet_b4	95.590	4.410	99.320	0.680	19.34	380	0.922	bicubic
42	tf_efficientnet_b2_ns	95.530	4.470	99.340	0.660	9.11	260	0.890	bicubic
43	tresnet_xl_448	95.520	4.480	99.340	0.660	78.44	448	0.875	bilinear
44	resnetv2_101x1_bitm	95.500	4.500	99.510	0.490	44.54	480	1.000	bilinear
45	tf_efficientnet_b4_ap	95.500	4.500	99.390	0.610	19.34	380	0.922	bicubic
46	resnet152d	95.470	4.530	99.300	0.700	60.21	256	0.940	bicubic
47	ssl_resnext101_32x16d	95.400	4.600	99.410	0.590	194.03	224	0.875	bilinear
48	tresnet_l_448	95.400	4.600	99.280	0.720	55.99	448	0.875	bilinear
49	ssl_resnext101_32x8d	95.330	4.670	99.320	0.680	88.79	224	0.875	bilinear
50	vit_large_patch16_224	95.290	4.710	99.310	0.690	304.33	224	0.900	bicubic
51	resnet101d	95.280	4.720	99.260	0.740	44.57	256	0.940	bicubic
52	vit_base_patch32_384	95.250	4.750	99.180	0.820	88.30	384	1.000	bicubic
53	vit_base_patch16_224	95.220	4.780	99.240	0.760	86.57	224	0.900	bicubic
54	swsl_resnet50	95.220	4.780	99.400	0.600	25.56	224	0.875	bilinear
55	nasnetalarge	95.170	4.830	99.130	0.870	88.75	331	0.911	bicubic
56	ecaresnet101d	95.160	4.840	99.230	0.770	44.57	224	0.875	bicubic
57	ssl_resnext101_32x4d	95.160	4.840	99.300	0.700	44.18	224	0.875	bilinear
58	efficientnet_b3a	95.150	4.850	99.210	0.790	12.23	320	1.000	bicubic
59	regnety_032	95.070	4.930	99.210	0.790	19.44	224	0.875	bicubic
60	tresnet_xl	95.070	4.930	99.250	0.750	78.44	224	0.875	bilinear
61	efficientnet_b3	94.970	5.030	99.240	0.760	12.23	300	0.904	bicubic
62	tf_efficientnet_b3_ap	94.960	5.040	99.110	0.890	12.23	300	0.904	bicubic
63	tf_efficientnet_b3	94.910	5.090	99.100	0.900	12.23	300	0.904	bicubic
64	tresnet_l	94.900	5.100	99.030	0.970	55.99	224	0.875	bilinear
65	tf_efficientnet_b1_ns	94.880	5.120	99.250	0.750	7.79	240	0.882	bicubic
66	tf_efficientnet_lite4	94.870	5.130	99.090	0.910	13.01	380	0.920	bilinear
67	seresnext50_32x4d	94.810	5.190	99.130	0.870	27.56	224	0.875	bicubic
68	legacy_senet154	94.730	5.270	99.100	0.900	115.09	224	0.875	bilinear
69	gluon_senet154	94.710	5.290	98.970	1.030	115.09	224	0.875	bicubic
70	resnest50d_4s2x40d	94.710	5.290	99.130	0.870	30.42	224	0.875	bicubic
71	ssl_resnext50_32x4d	94.700	5.300	99.240	0.760	25.03	224	0.875	bilinear
72	gluon_resnet152_v1s	94.700	5.300	99.050	0.950	60.32	224	0.875	bicubic
73	rexnet_200	94.670	5.330	99.090	0.910	16.37	224	0.875	bicubic
74	tresnet_m_448	94.670	5.330	99.170	0.830	31.39	448	0.875	bilinear
75	wide_resnet50_2	94.660	5.340	99.050	0.950	68.88	224	0.875	bicubic
76	gluon_seresnext101_64x4d	94.630	5.370	98.970	1.030	88.23	224	0.875	bicubic
77	resnest50d	94.620	5.380	99.030	0.970	27.48	224	0.875	bilinear
78	efficientnet_b3_pruned	94.580	5.420	99.070	0.930	9.86	300	0.904	bicubic
79	resnetv2_50x1_bitm	94.530	5.470	99.240	0.760	25.55	480	1.000	bilinear
80	regnety_320	94.500	5.500	99.170	0.830	145.05	224	0.875	bicubic
81	tf_efficientnet_el	94.470	5.530	99.080	0.920	10.59	300	0.904	bicubic
82	gluon_resnet152_v1d	94.450	5.550	99.000	1.000	60.21	224	0.875	bicubic
83	ecaresnet101d_pruned	94.440	5.560	99.100	0.900	24.88	224	0.875	bicubic
84	gluon_seresnext101_32x4d	94.430	5.570	99.090	0.910	48.96	224	0.875	bicubic
85	resnest50d_1s4x24d	94.390	5.610	99.060	0.940	25.68	224	0.875	bicubic
86	efficientnet_b2a	94.370	5.630	99.050	0.950	9.11	288	1.000	bicubic
87	efficientnet_b2	94.340	5.660	99.100	0.900	9.11	260	0.875	bicubic
88	gluon_resnext101_64x4d	94.330	5.670	98.880	1.120	83.46	224	0.875	bicubic
89	ssl_resnet50	94.320	5.680	99.160	0.840	25.56	224	0.875	bilinear
90	regnetx_120	94.300	5.700	99.200	0.800	46.11	224	0.875	bicubic
91	rexnet_150	94.280	5.720	99.090	0.910	9.73	224	0.875	bicubic
92	tf_efficientnet_b2_ap	94.270	5.730	98.950	1.050	9.11	260	0.890	bicubic
93	mixnet_xl	94.220	5.780	98.820	1.180	11.90	224	0.875	bicubic
94	dpn92	94.200	5.800	98.930	1.070	37.67	224	0.875	bicubic
95	ecaresnet50d	94.200	5.800	99.020	0.980	25.58	224	0.875	bicubic
96	regnetx_320	94.200	5.800	99.050	0.950	107.81	224	0.875	bicubic
97	tf_efficientnet_b2	94.200	5.800	99.030	0.970	9.11	260	0.890	bicubic
98	gluon_seresnext50_32x4d	94.180	5.820	98.910	1.090	27.56	224	0.875	bicubic
99	gluon_resnet101_v1s	94.180	5.820	99.020	0.980	44.67	224	0.875	bicubic
100	legacy_seresnext101_32x4d	94.170	5.830	98.970	1.030	48.96	224	0.875	bilinear
101	gluon_resnet101_v1d	94.160	5.840	98.950	1.050	44.57	224	0.875	bicubic
102	ecaresnetlight	94.140	5.860	98.950	1.050	30.16	224	0.875	bicubic
103	regnety_064	94.140	5.860	99.030	0.970	30.58	224	0.875	bicubic
104	gluon_resnext101_32x4d	94.120	5.880	98.930	1.070	44.18	224	0.875	bicubic
105	tf_efficientnet_lite3	94.120	5.880	98.960	1.040	8.20	300	0.904	bilinear
106	regnety_160	94.110	5.890	99.010	0.990	83.59	224	0.875	bicubic
107	cspdarknet53	94.100	5.900	98.980	1.020	27.64	256	0.887	bilinear
108	seresnet50	94.080	5.920	98.970	1.030	28.09	224	0.875	bicubic
109	tresnet_m	94.080	5.920	98.830	1.170	31.39	224	0.875	bilinear
110	resnet50d	94.070	5.930	98.920	1.080	25.58	224	0.875	bicubic
111	regnety_120	94.060	5.940	99.020	0.980	51.82	224	0.875	bicubic
112	gluon_xception65	94.030	5.970	99.030	0.970	39.92	299	0.903	bicubic
113	hrnet_w48	94.030	5.970	99.030	0.970	77.47	224	0.875	bilinear
114	gluon_resnet152_v1b	94.020	5.980	98.740	1.260	60.19	224	0.875	bicubic
115	dla102x2	94.020	5.980	99.030	0.970	41.28	224	0.875	bilinear
116	dpn107	93.960	6.040	98.850	1.150	86.92	224	0.875	bicubic
117	dpn98	93.950	6.050	98.920	1.080	61.57	224	0.875	bicubic
118	skresnext50_32x4d	93.950	6.050	98.820	1.180	27.48	224	0.875	bicubic
119	regnety_080	93.900	6.100	98.990	1.010	39.18	224	0.875	bicubic
120	xception71	93.900	6.100	98.950	1.050	42.34	299	0.903	bicubic
121	regnetx_160	93.890	6.110	99.080	0.920	54.28	224	0.875	bicubic
122	gluon_resnet152_v1c	93.870	6.130	98.800	1.200	60.21	224	0.875	bicubic
123	ese_vovnet39b	93.850	6.150	98.900	1.100	24.57	224	0.875	bicubic
124	cspresnet50	93.850	6.150	98.880	1.120	21.62	256	0.887	bilinear
125	hrnet_w64	93.850	6.150	98.920	1.080	128.06	224	0.875	bilinear
126	resnext50_32x4d	93.850	6.150	98.830	1.170	25.03	224	0.875	bicubic
127	ecaresnet50d_pruned	93.820	6.180	99.000	1.000	19.94	224	0.875	bicubic
128	resnext50d_32x4d	93.800	6.200	98.720	1.280	25.05	224	0.875	bicubic
129	efficientnet_b2_pruned	93.800	6.200	98.910	1.090	8.31	260	0.890	bicubic
130	regnetx_080	93.790	6.210	98.910	1.090	39.57	224	0.875	bicubic
131	dla169	93.780	6.220	98.840	1.160	53.39	224	0.875	bilinear
132	gluon_resnet101_v1b	93.770	6.230	98.710	1.290	44.55	224	0.875	bicubic
133	resnext101_32x8d	93.770	6.230	98.950	1.050	88.79	224	0.875	bilinear
134	dpn131	93.760	6.240	98.810	1.190	79.25	224	0.875	bicubic
135	tf_efficientnet_b0_ns	93.760	6.240	98.980	1.020	5.29	224	0.875	bicubic
136	cspresnext50	93.750	6.250	98.840	1.160	20.57	224	0.875	bilinear
137	efficientnet_em	93.750	6.250	98.920	1.080	6.90	240	0.882	bicubic
138	xception65	93.750	6.250	98.870	1.130	39.92	299	0.903	bicubic
139	wide_resnet101_2	93.730	6.270	98.810	1.190	126.89	224	0.875	bilinear
140	tf_efficientnet_b1	93.710	6.290	98.800	1.200	7.79	240	0.882	bicubic
141	hrnet_w40	93.710	6.290	98.800	1.200	57.56	224	0.875	bilinear
142	resnetblur50	93.710	6.290	98.800	1.200	25.56	224	0.875	bicubic
143	rexnet_130	93.680	6.320	98.720	1.280	7.56	224	0.875	bicubic
144	regnetx_040	93.680	6.320	98.950	1.050	22.12	224	0.875	bicubic
145	gluon_resnext50_32x4d	93.670	6.330	98.700	1.300	25.03	224	0.875	bicubic
146	gluon_resnet101_v1c	93.660	6.340	98.760	1.240	44.57	224	0.875	bicubic
147	regnetx_064	93.650	6.350	99.050	0.950	26.21	224	0.875	bicubic
148	xception	93.650	6.350	98.790	1.210	22.86	299	0.897	bicubic
149	tf_efficientnet_b1_ap	93.640	6.360	98.780	1.220	7.79	240	0.882	bicubic
150	hrnet_w44	93.630	6.370	98.950	1.050	67.06	224	0.875	bilinear
151	regnety_040	93.610	6.390	98.960	1.040	20.65	224	0.875	bicubic
152	dpn68b	93.600	6.400	98.710	1.290	12.61	224	0.875	bicubic
153	gluon_inception_v3	93.590	6.410	98.840	1.160	23.83	299	0.875	bicubic
154	gluon_resnet50_v1s	93.590	6.410	98.830	1.170	25.68	224	0.875	bicubic
155	tf_efficientnet_cc_b1_8e	93.580	6.420	98.690	1.310	39.72	240	0.882	bicubic
156	res2net50_26w_6s	93.570	6.430	98.750	1.250	37.05	224	0.875	bilinear
157	dla60_res2next	93.560	6.440	98.800	1.200	17.03	224	0.875	bilinear
158	gluon_resnet50_v1d	93.550	6.450	98.710	1.290	25.58	224	0.875	bicubic
159	res2net101_26w_4s	93.520	6.480	98.630	1.370	45.21	224	0.875	bilinear
160	dla102x	93.510	6.490	98.850	1.150	26.31	224	0.875	bilinear
161	xception41	93.480	6.520	98.760	1.240	26.97	299	0.903	bicubic
162	selecsls60b	93.480	6.520	98.840	1.160	32.77	224	0.875	bicubic
163	resnet50	93.480	6.520	98.590	1.410	25.56	224	0.875	bicubic
164	tf_efficientnet_em	93.470	6.530	98.750	1.250	6.90	240	0.882	bicubic
165	legacy_seresnet152	93.460	6.540	98.850	1.150	66.82	224	0.875	bilinear
166	res2net50_26w_8s	93.450	6.550	98.710	1.290	48.40	224	0.875	bilinear
167	legacy_seresnext50_32x4d	93.400	6.600	98.800	1.200	27.56	224	0.875	bilinear
168	dla60_res2net	93.390	6.610	98.840	1.160	20.85	224	0.875	bilinear
169	hrnet_w30	93.390	6.610	98.830	1.170	37.71	224	0.875	bilinear
170	dla102	93.300	6.700	98.780	1.220	33.27	224	0.875	bilinear
171	legacy_seresnet101	93.280	6.720	98.750	1.250	49.33	224	0.875	bilinear
172	mixnet_l	93.280	6.720	98.710	1.290	7.33	224	0.875	bicubic
173	regnetx_032	93.270	6.730	98.740	1.260	15.30	224	0.875	bicubic
174	resnest26d	93.260	6.740	98.840	1.160	17.07	224	0.875	bilinear
175	tv_resnet152	93.250	6.750	98.750	1.250	60.19	224	0.875	bilinear
176	tf_inception_v3	93.210	6.790	98.480	1.520	23.83	299	0.875	bicubic
177	dla60x	93.210	6.790	98.720	1.280	17.35	224	0.875	bilinear
178	res2net50_26w_4s	93.180	6.820	98.670	1.330	25.70	224	0.875	bilinear
179	res2next50	93.130	6.870	98.660	1.340	24.67	224	0.875	bilinear
180	efficientnet_b1	93.060	6.940	98.530	1.470	7.79	240	0.875	bicubic
181	tf_mixnet_l	93.050	6.950	98.540	1.460	7.33	224	0.875	bicubic
182	regnety_016	93.030	6.970	98.690	1.310	11.20	224	0.875	bicubic
183	res2net50_14w_8s	93.010	6.990	98.700	1.300	25.06	224	0.875	bilinear
184	selecsls60	93.010	6.990	98.830	1.170	30.67	224	0.875	bicubic
185	adv_inception_v3	92.990	7.010	98.480	1.520	23.83	299	0.875	bicubic
186	seresnext26t_32x4d	92.980	7.020	98.480	1.520	16.82	224	0.875	bicubic
187	efficientnet_b1_pruned	92.970	7.030	98.520	1.480	6.33	240	0.882	bicubic
188	hrnet_w32	92.950	7.050	98.840	1.160	41.23	224	0.875	bilinear
189	efficientnet_es	92.930	7.070	98.680	1.320	5.44	224	0.875	bicubic
190	gluon_resnet50_v1c	92.910	7.090	98.710	1.290	25.58	224	0.875	bicubic
191	tv_resnext50_32x4d	92.910	7.090	98.730	1.270	25.03	224	0.875	bilinear
192	inception_v3	92.900	7.100	98.320	1.680	23.83	299	0.875	bicubic
193	tv_resnet101	92.880	7.120	98.660	1.340	44.55	224	0.875	bilinear
194	densenet161	92.880	7.120	98.810	1.190	28.68	224	0.875	bicubic
195	tf_efficientnet_cc_b0_8e	92.860	7.140	98.460	1.540	24.01	224	0.875	bicubic
196	rexnet_100	92.850	7.150	98.620	1.380	4.80	224	0.875	bicubic
197	tf_efficientnet_cc_b0_4e	92.840	7.160	98.440	1.560	13.31	224	0.875	bicubic
198	res2net50_48w_2s	92.800	7.200	98.460	1.540	25.29	224	0.875	bilinear
199	seresnext26tn_32x4d	92.770	7.230	98.550	1.450	16.81	224	0.875	bicubic
200	hrnet_w18	92.760	7.240	98.650	1.350	21.30	224	0.875	bilinear
201	dla60	92.690	7.310	98.630	1.370	22.04	224	0.875	bilinear
202	densenet201	92.690	7.310	98.660	1.340	20.01	224	0.875	bicubic
203	legacy_seresnet50	92.670	7.330	98.660	1.340	28.09	224	0.875	bilinear
204	resnet34d	92.640	7.360	98.440	1.560	21.82	224	0.875	bicubic
205	mobilenetv2_120d	92.610	7.390	98.520	1.480	5.83	224	0.875	bicubic
206	vit_small_patch16_224	92.610	7.390	98.390	1.610	48.75	224	0.900	bicubic
207	tf_efficientnet_b0_ap	92.600	7.400	98.370	1.630	5.29	224	0.875	bicubic
208	legacy_seresnext26_32x4d	92.580	7.420	98.430	1.570	16.79	224	0.875	bicubic
209	tf_efficientnet_lite2	92.580	7.420	98.550	1.450	6.09	260	0.890	bicubic
210	skresnet34	92.560	7.440	98.510	1.490	22.28	224	0.875	bicubic
211	regnetx_016	92.560	7.440	98.550	1.450	9.19	224	0.875	bicubic
212	gluon_resnet50_v1b	92.550	7.450	98.550	1.450	25.56	224	0.875	bicubic
213	tf_efficientnet_es	92.540	7.460	98.510	1.490	5.44	224	0.875	bicubic
214	selecsls42b	92.480	7.520	98.440	1.560	32.46	224	0.875	bicubic
215	efficientnet_b0	92.470	7.530	98.680	1.320	5.29	224	0.875	bicubic
216	seresnext26d_32x4d	92.440	7.560	98.540	1.460	16.81	224	0.875	bicubic
217	densenetblur121d	92.400	7.600	98.420	1.580	8.00	224	0.875	bicubic
218	tf_efficientnet_b0	92.400	7.600	98.470	1.530	5.29	224	0.875	bicubic
219	tf_efficientnet_lite1	92.320	7.680	98.490	1.510	5.42	240	0.882	bicubic
220	densenet169	92.300	7.700	98.590	1.410	14.15	224	0.875	bicubic
221	mixnet_m	92.260	7.740	98.370	1.630	5.01	224	0.875	bicubic
222	dpn68	92.250	7.750	98.600	1.400	12.61	224	0.875	bicubic
223	resnet26d	92.230	7.770	98.470	1.530	16.01	224	0.875	bicubic
224	tf_mixnet_m	92.170	7.830	98.420	1.580	5.01	224	0.875	bicubic
225	tv_resnet50	92.130	7.870	98.420	1.580	25.56	224	0.875	bilinear
226	mobilenetv2_140	92.040	7.960	98.250	1.750	6.11	224	0.875	bicubic
227	ese_vovnet19b_dw	92.020	7.980	98.520	1.480	6.54	224	0.875	bicubic
228	densenet121	91.940	8.060	98.280	1.720	7.98	224	0.875	bicubic
229	regnety_008	91.890	8.110	98.420	1.580	6.26	224	0.875	bicubic
230	mixnet_s	91.780	8.220	98.300	1.700	4.13	224	0.875	bicubic
231	tf_mixnet_s	91.690	8.310	98.240	1.760	4.13	224	0.875	bicubic
232	semnasnet_100	91.660	8.340	98.260	1.740	3.89	224	0.875	bicubic
233	regnety_006	91.590	8.410	98.430	1.570	6.06	224	0.875	bicubic
234	mobilenetv3_rw	91.560	8.440	98.270	1.730	5.48	224	0.875	bicubic
235	mobilenetv3_large_100	91.480	8.520	98.330	1.670	5.48	224	0.875	bicubic
236	legacy_seresnet34	91.470	8.530	98.200	1.800	21.96	224	0.875	bilinear
237	resnet26	91.450	8.550	98.280	1.720	16.00	224	0.875	bicubic
238	tf_mobilenetv3_large_100	91.410	8.590	98.250	1.750	5.48	224	0.875	bilinear
239	tv_densenet121	91.390	8.610	98.250	1.750	7.98	224	0.875	bicubic
240	mobilenetv2_110d	91.330	8.670	98.190	1.810	4.52	224	0.875	bicubic
241	tf_efficientnet_lite0	91.280	8.720	98.090	1.910	4.65	224	0.875	bicubic
242	dla34	91.260	8.740	98.180	1.820	15.74	224	0.875	bilinear
243	efficientnet_lite0	91.260	8.740	98.250	1.750	4.65	224	0.875	bicubic
244	fbnetc_100	91.260	8.740	97.820	2.180	5.57	224	0.875	bilinear
245	regnetx_008	91.210	8.790	98.370	1.630	7.26	224	0.875	bicubic
246	mnasnet_100	91.200	8.800	98.040	1.960	4.38	224	0.875	bicubic
247	resnet34	91.190	8.810	98.230	1.770	21.80	224	0.875	bilinear
248	hrnet_w18_small_v2	91.170	8.830	98.360	1.640	15.60	224	0.875	bilinear
249	resnest14d	91.140	8.860	98.350	1.650	10.61	224	0.875	bilinear
250	gluon_resnet34_v1b	91.090	8.910	98.180	1.820	21.80	224	0.875	bicubic
251	swsl_resnet18	91.090	8.910	98.210	1.790	11.69	224	0.875	bilinear
252	regnety_004	90.760	9.240	98.080	1.920	4.34	224	0.875	bicubic
253	regnetx_006	90.750	9.250	98.100	1.900	6.20	224	0.875	bicubic
254	ssl_resnet18	90.700	9.300	98.030	1.970	11.69	224	0.875	bilinear
255	spnasnet_100	90.620	9.380	97.950	2.050	4.42	224	0.875	bilinear
256	tf_mobilenetv3_large_075	90.310	9.690	97.880	2.120	3.99	224	0.875	bilinear
257	tv_resnet34	90.310	9.690	97.970	2.030	21.80	224	0.875	bilinear
258	skresnet18	90.160	9.840	97.780	2.220	11.96	224	0.875	bicubic
259	resnet18d	90.000	10.000	97.840	2.160	11.71	224	0.875	bicubic
260	hrnet_w18_small	89.900	10.100	97.900	2.100	13.19	224	0.875	bilinear
261	mobilenetv2_100	89.840	10.160	97.840	2.160	3.50	224	0.875	bicubic
262	regnetx_004	89.450	10.550	97.780	2.220	5.16	224	0.875	bicubic
263	legacy_seresnet18	89.260	10.740	97.690	2.310	11.78	224	0.875	bicubic
264	tf_mobilenetv3_large_minimal_100	89.160	10.840	97.320	2.680	3.92	224	0.875	bilinear
265	gluon_resnet18_v1b	88.670	11.330	97.110	2.890	11.69	224	0.875	bicubic
266	regnety_002	88.220	11.780	97.430	2.570	3.16	224	0.875	bicubic
267	resnet18	88.170	11.830	97.120	2.880	11.69	224	0.875	bilinear
268	regnetx_002	87.370	12.630	96.990	3.010	2.68	224	0.875	bicubic
269	dla60x_c	87.080	12.920	97.140	2.860	1.32	224	0.875	bilinear
270	tf_mobilenetv3_small_100	85.980	14.020	96.400	3.600	2.54	224	0.875	bilinear
271	dla46x_c	85.470	14.530	96.450	3.550	1.07	224	0.875	bilinear
272	dla46_c	84.710	15.290	96.210	3.790	1.30	224	0.875	bilinear
273	tf_mobilenetv3_small_075	84.530	15.470	95.880	4.120	2.04	224	0.875	bilinear
274	tf_mobilenetv3_small_minimal_100	82.670	17.330	95.000	5.000	2.04	224	0.875	bilinear
275	pnasnet5large	17.870	82.130	42.120	57.880	86.06	331	0.911	bicubic
276	inception_resnet_v2	16.790	83.210	38.850	61.150	55.85	299	0.897	bicubic
277	inception_v4	16.540	83.460	37.380	62.620	42.68	299	0.875	bicubic
278	ens_adv_inception_resnet_v2	16.240	83.760	40.390	59.610	55.85	299	0.897	bicubic