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	98.540	1.460	99.820	0.180	480.31	800	0.960	bicubic
3	tf_efficientnet_l2_ns_475	98.490	1.510	99.830	0.170	480.31	475	0.936	bicubic
4	tf_efficientnet_b7_ns	97.880	2.120	99.720	0.280	66.35	600	0.949	bicubic
5	ig_resnext101_32x48d	97.640	2.360	99.700	0.300	828.41	224	0.875	bilinear
6	tf_efficientnet_b6_ns	97.630	2.370	99.580	0.420	43.04	528	0.942	bicubic
7	tf_efficientnet_b5_ns	97.490	2.510	99.630	0.370	30.39	456	0.934	bicubic
8	resnetv2_152x4_bitm	97.480	2.520	99.610	0.390	936.53	480	1.000	bilinear
9	ig_resnext101_32x32d	97.360	2.640	99.680	0.320	468.53	224	0.875	bilinear
10	tf_efficientnet_b8	97.210	2.790	99.500	0.500	87.41	672	0.954	bicubic
11	tf_efficientnet_b7_ap	97.200	2.800	99.540	0.460	66.35	600	0.949	bicubic
12	swsl_resnext101_32x8d	97.190	2.810	99.570	0.430	88.79	224	0.875	bilinear
13	vit_base_resnet50_384	97.180	2.820	99.560	0.440	98.95	384	1.000	bicubic
14	resnetv2_152x2_bitm	97.130	2.870	99.590	0.410	236.34	480	1.000	bilinear
15	vit_large_patch16_384	97.120	2.880	99.640	0.360	304.72	384	1.000	bicubic
16	tf_efficientnet_b8_ap	97.110	2.890	99.660	0.340	87.41	672	0.954	bicubic
17	tf_efficientnet_b6_ap	97.080	2.920	99.620	0.380	43.04	528	0.942	bicubic
18	resnetv2_101x3_bitm	97.080	2.920	99.510	0.490	387.93	480	1.000	bilinear
19	tf_efficientnet_b7	97.010	2.990	99.520	0.480	66.35	600	0.949	bicubic
20	tf_efficientnet_b4_ns	96.930	3.070	99.580	0.420	19.34	380	0.922	bicubic
21	ig_resnext101_32x16d	96.830	3.170	99.590	0.410	194.03	224	0.875	bilinear
22	resnetv2_50x3_bitm	96.780	3.220	99.420	0.580	217.32	480	1.000	bilinear
23	seresnet152d_320	96.770	3.230	99.450	0.550	66.84	320	1.000	bicubic
24	resnet200d_320	96.740	3.260	99.340	0.660	64.69	320	1.000	bicubic
25	vit_base_patch16_384	96.710	3.290	99.510	0.490	86.86	384	1.000	bicubic
26	tf_efficientnet_b5_ap	96.690	3.310	99.460	0.540	30.39	456	0.934	bicubic
27	tf_efficientnet_b6	96.670	3.330	99.370	0.630	43.04	528	0.942	bicubic
28	resnest200e	96.620	3.380	99.350	0.650	70.20	320	0.909	bicubic
29	swsl_resnext101_32x16d	96.600	3.400	99.520	0.480	194.03	224	0.875	bilinear
30	seresnet152d	96.570	3.430	99.400	0.600	66.84	256	0.940	bicubic
31	resnest269e	96.520	3.480	99.350	0.650	110.93	416	0.928	bicubic
32	swsl_resnext101_32x4d	96.410	3.590	99.470	0.530	44.18	224	0.875	bilinear
33	tf_efficientnet_b3_ns	96.370	3.630	99.350	0.650	12.23	300	0.904	bicubic
34	resnet152d_320	96.350	3.650	99.390	0.610	60.21	320	1.000	bicubic
35	tf_efficientnet_b5	96.340	3.660	99.310	0.690	30.39	456	0.934	bicubic
36	ig_resnext101_32x8d	96.310	3.690	99.430	0.570	88.79	224	0.875	bilinear
37	resnet101d_320	96.290	3.710	99.230	0.770	44.57	320	1.000	bicubic
38	resnet200d	96.290	3.710	99.250	0.750	64.69	256	0.940	bicubic
39	tf_efficientnet_b4_ap	96.170	3.830	99.280	0.720	19.34	380	0.922	bicubic
40	resnetv2_101x1_bitm	96.070	3.930	99.300	0.700	44.54	480	1.000	bilinear
41	resnet152d	95.990	4.010	99.260	0.740	60.21	256	0.940	bicubic
42	tresnet_xl_448	95.970	4.030	99.120	0.880	78.44	448	0.875	bilinear
43	vit_large_patch16_224	95.940	4.060	99.240	0.760	304.33	224	0.900	bicubic
44	resnet101d	95.930	4.070	99.070	0.930	44.57	256	0.940	bicubic
45	tf_efficientnet_b4	95.890	4.110	99.170	0.830	19.34	380	0.922	bicubic
46	swsl_resnext50_32x4d	95.880	4.120	99.250	0.750	25.03	224	0.875	bilinear
47	tresnet_l_448	95.860	4.140	99.120	0.880	55.99	448	0.875	bilinear
48	resnest101e	95.850	4.150	99.210	0.790	48.28	256	0.875	bilinear
49	ssl_resnext101_32x16d	95.810	4.190	99.180	0.820	194.03	224	0.875	bilinear
50	vit_base_patch32_384	95.810	4.190	99.150	0.850	88.30	384	1.000	bicubic
51	tf_efficientnet_b2_ns	95.740	4.260	99.120	0.880	9.11	260	0.890	bicubic
52	efficientnet_b3a	95.720	4.280	99.040	0.960	12.23	320	1.000	bicubic
53	nasnetalarge	95.680	4.320	98.930	1.070	88.75	331	0.911	bicubic
54	efficientnet_b3	95.580	4.420	99.100	0.900	12.23	300	0.904	bicubic
55	ecaresnet101d	95.540	4.460	99.130	0.870	44.57	224	0.875	bicubic
56	ssl_resnext101_32x8d	95.470	4.530	99.110	0.890	88.79	224	0.875	bilinear
57	tresnet_xl	95.440	4.560	99.060	0.940	78.44	224	0.875	bilinear
58	ssl_resnext101_32x4d	95.430	4.570	99.130	0.870	44.18	224	0.875	bilinear
59	swsl_resnet50	95.410	4.590	99.280	0.720	25.56	224	0.875	bilinear
60	regnety_032	95.360	4.640	99.100	0.900	19.44	224	0.875	bicubic
61	vit_base_patch16_224	95.340	4.660	99.000	1.000	86.57	224	0.900	bicubic
62	tf_efficientnet_b3_ap	95.320	4.680	98.900	1.100	12.23	300	0.904	bicubic
63	tresnet_l	95.290	4.710	99.010	0.990	55.99	224	0.875	bilinear
64	tf_efficientnet_b1_ns	95.170	4.830	99.110	0.890	7.79	240	0.882	bicubic
65	ecaresnet101d_pruned	95.070	4.930	98.980	1.020	24.88	224	0.875	bicubic
66	wide_resnet50_2	95.070	4.930	98.970	1.030	68.88	224	0.875	bicubic
67	legacy_senet154	95.060	4.940	98.830	1.170	115.09	224	0.875	bilinear
68	gluon_resnet152_v1s	95.050	4.950	98.930	1.070	60.32	224	0.875	bicubic
69	resnetv2_50x1_bitm	95.040	4.960	99.160	0.840	25.55	480	1.000	bilinear
70	seresnext50_32x4d	95.040	4.960	98.890	1.110	27.56	224	0.875	bicubic
71	tf_efficientnet_b3	95.010	4.990	98.910	1.090	12.23	300	0.904	bicubic
72	tresnet_m_448	94.990	5.010	98.980	1.020	31.39	448	0.875	bilinear
73	resnest50d_4s2x40d	94.950	5.050	99.070	0.930	30.42	224	0.875	bicubic
74	gluon_seresnext101_64x4d	94.950	5.050	98.820	1.180	88.23	224	0.875	bicubic
75	gluon_senet154	94.930	5.070	98.770	1.230	115.09	224	0.875	bicubic
76	rexnet_200	94.930	5.070	99.000	1.000	16.37	224	0.875	bicubic
77	gluon_seresnext101_32x4d	94.920	5.080	98.810	1.190	48.96	224	0.875	bicubic
78	ssl_resnext50_32x4d	94.860	5.140	98.870	1.130	25.03	224	0.875	bilinear
79	tf_efficientnet_lite4	94.860	5.140	99.020	0.980	13.01	380	0.920	bilinear
80	resnest50d	94.820	5.180	98.880	1.120	27.48	224	0.875	bilinear
81	ecaresnetlight	94.780	5.220	98.800	1.200	30.16	224	0.875	bicubic
82	resnest50d_1s4x24d	94.760	5.240	98.980	1.020	25.68	224	0.875	bicubic
83	gluon_resnet152_v1d	94.750	5.250	98.740	1.260	60.21	224	0.875	bicubic
84	efficientnet_b2	94.710	5.290	98.670	1.330	9.11	260	0.875	bicubic
85	gluon_resnet101_v1s	94.700	5.300	98.820	1.180	44.67	224	0.875	bicubic
86	cspdarknet53	94.670	5.330	98.810	1.190	27.64	256	0.887	bilinear
87	gluon_resnext101_64x4d	94.640	5.360	98.660	1.340	83.46	224	0.875	bicubic
88	efficientnet_b2a	94.630	5.370	98.710	1.290	9.11	288	1.000	bicubic
89	efficientnet_b3_pruned	94.620	5.380	98.770	1.230	9.86	300	0.904	bicubic
90	ecaresnet50d	94.620	5.380	98.890	1.110	25.58	224	0.875	bicubic
91	tf_efficientnet_el	94.610	5.390	98.750	1.250	10.59	300	0.904	bicubic
92	tresnet_m	94.610	5.390	98.550	1.450	31.39	224	0.875	bilinear
93	seresnet50	94.560	5.440	98.750	1.250	28.09	224	0.875	bicubic
94	regnety_320	94.550	5.450	98.840	1.160	145.05	224	0.875	bicubic
95	gluon_resnext101_32x4d	94.520	5.480	98.630	1.370	44.18	224	0.875	bicubic
96	ssl_resnet50	94.490	5.510	98.920	1.080	25.56	224	0.875	bilinear
97	tf_efficientnet_b2_ap	94.490	5.510	98.620	1.380	9.11	260	0.890	bicubic
98	cspresnext50	94.480	5.520	98.680	1.320	20.57	224	0.875	bilinear
99	regnety_120	94.480	5.520	98.810	1.190	51.82	224	0.875	bicubic
100	rexnet_150	94.470	5.530	98.790	1.210	9.73	224	0.875	bicubic
101	regnetx_320	94.440	5.560	98.730	1.270	107.81	224	0.875	bicubic
102	tf_efficientnet_b2	94.370	5.630	98.610	1.390	9.11	260	0.890	bicubic
103	legacy_seresnext101_32x4d	94.360	5.640	98.640	1.360	48.96	224	0.875	bilinear
104	gluon_seresnext50_32x4d	94.350	5.650	98.620	1.380	27.56	224	0.875	bicubic
105	regnety_160	94.350	5.650	98.850	1.150	83.59	224	0.875	bicubic
106	dpn107	94.310	5.690	98.480	1.520	86.92	224	0.875	bicubic
107	xception71	94.290	5.710	98.640	1.360	42.34	299	0.903	bicubic
108	resnet50d	94.270	5.730	98.720	1.280	25.58	224	0.875	bicubic
109	gluon_xception65	94.260	5.740	98.570	1.430	39.92	299	0.903	bicubic
110	skresnext50_32x4d	94.260	5.740	98.470	1.530	27.48	224	0.875	bicubic
111	regnetx_120	94.240	5.760	98.670	1.330	46.11	224	0.875	bicubic
112	dpn92	94.220	5.780	98.730	1.270	37.67	224	0.875	bicubic
113	ecaresnet50d_pruned	94.210	5.790	98.730	1.270	19.94	224	0.875	bicubic
114	gluon_resnet101_v1d	94.200	5.800	98.560	1.440	44.57	224	0.875	bicubic
115	mixnet_xl	94.190	5.810	98.340	1.660	11.90	224	0.875	bicubic
116	resnext50d_32x4d	94.190	5.810	98.580	1.420	25.05	224	0.875	bicubic
117	tf_efficientnet_lite3	94.190	5.810	98.640	1.360	8.20	300	0.904	bilinear
118	regnety_080	94.180	5.820	98.680	1.320	39.18	224	0.875	bicubic
119	gluon_resnet152_v1c	94.160	5.840	98.640	1.360	60.21	224	0.875	bicubic
120	regnety_064	94.150	5.850	98.730	1.270	30.58	224	0.875	bicubic
121	efficientnet_b2_pruned	94.140	5.860	98.520	1.480	8.31	260	0.890	bicubic
122	dpn98	94.130	5.870	98.570	1.430	61.57	224	0.875	bicubic
123	regnetx_160	94.120	5.880	98.740	1.260	54.28	224	0.875	bicubic
124	resnext50_32x4d	94.110	5.890	98.350	1.650	25.03	224	0.875	bicubic
125	ese_vovnet39b	94.090	5.910	98.660	1.340	24.57	224	0.875	bicubic
126	gluon_resnet152_v1b	94.090	5.910	98.450	1.550	60.19	224	0.875	bicubic
127	dpn131	94.000	6.000	98.720	1.280	79.25	224	0.875	bicubic
128	hrnet_w64	93.990	6.010	98.610	1.390	128.06	224	0.875	bilinear
129	dla102x2	93.950	6.050	98.490	1.510	41.28	224	0.875	bilinear
130	resnetblur50	93.950	6.050	98.590	1.410	25.56	224	0.875	bicubic
131	hrnet_w48	93.940	6.060	98.610	1.390	77.47	224	0.875	bilinear
132	tf_efficientnet_cc_b1_8e	93.910	6.090	98.250	1.750	39.72	240	0.882	bicubic
133	rexnet_130	93.900	6.100	98.400	1.600	7.56	224	0.875	bicubic
134	regnetx_064	93.890	6.110	98.630	1.370	26.21	224	0.875	bicubic
135	regnetx_080	93.860	6.140	98.520	1.480	39.57	224	0.875	bicubic
136	regnety_040	93.860	6.140	98.640	1.360	20.65	224	0.875	bicubic
137	efficientnet_em	93.850	6.150	98.810	1.190	6.90	240	0.882	bicubic
138	gluon_resnext50_32x4d	93.820	6.180	98.410	1.590	25.03	224	0.875	bicubic
139	resnet50	93.810	6.190	98.380	1.620	25.56	224	0.875	bicubic
140	resnext101_32x8d	93.800	6.200	98.580	1.420	88.79	224	0.875	bilinear
141	gluon_resnet50_v1d	93.780	6.220	98.400	1.600	25.58	224	0.875	bicubic
142	xception65	93.770	6.230	98.360	1.640	39.92	299	0.903	bicubic
143	cspresnet50	93.750	6.250	98.630	1.370	21.62	256	0.887	bilinear
144	gluon_resnet101_v1b	93.730	6.270	98.400	1.600	44.55	224	0.875	bicubic
145	res2net101_26w_4s	93.730	6.270	98.320	1.680	45.21	224	0.875	bilinear
146	wide_resnet101_2	93.730	6.270	98.540	1.460	126.89	224	0.875	bilinear
147	legacy_seresnext50_32x4d	93.720	6.280	98.580	1.420	27.56	224	0.875	bilinear
148	tf_efficientnet_b1_ap	93.710	6.290	98.360	1.640	7.79	240	0.882	bicubic
149	dpn68b	93.680	6.320	98.520	1.480	12.61	224	0.875	bicubic
150	gluon_resnet101_v1c	93.670	6.330	98.410	1.590	44.57	224	0.875	bicubic
151	gluon_resnet50_v1s	93.630	6.370	98.470	1.530	25.68	224	0.875	bicubic
152	tf_efficientnet_b0_ns	93.620	6.380	98.640	1.360	5.29	224	0.875	bicubic
153	hrnet_w44	93.580	6.420	98.700	1.300	67.06	224	0.875	bilinear
154	regnetx_040	93.560	6.440	98.550	1.450	22.12	224	0.875	bicubic
155	res2net50_26w_8s	93.540	6.460	98.270	1.730	48.40	224	0.875	bilinear
156	hrnet_w32	93.520	6.480	98.430	1.570	41.23	224	0.875	bilinear
157	tf_efficientnet_b1	93.520	6.480	98.360	1.640	7.79	240	0.882	bicubic
158	dla102x	93.520	6.480	98.500	1.500	26.31	224	0.875	bilinear
159	tf_efficientnet_em	93.500	6.500	98.440	1.560	6.90	240	0.882	bicubic
160	hrnet_w40	93.490	6.510	98.590	1.410	57.56	224	0.875	bilinear
161	xception	93.470	6.530	98.530	1.470	22.86	299	0.897	bicubic
162	gluon_inception_v3	93.460	6.540	98.560	1.440	23.83	299	0.875	bicubic
163	mixnet_l	93.430	6.570	98.220	1.780	7.33	224	0.875	bicubic
164	legacy_seresnet152	93.420	6.580	98.350	1.650	66.82	224	0.875	bilinear
165	xception41	93.410	6.590	98.420	1.580	26.97	299	0.903	bicubic
166	res2net50_26w_6s	93.400	6.600	98.280	1.720	37.05	224	0.875	bilinear
167	resnest26d	93.370	6.630	98.640	1.360	17.07	224	0.875	bilinear
168	dla169	93.340	6.660	98.600	1.400	53.39	224	0.875	bilinear
169	tf_inception_v3	93.330	6.670	98.040	1.960	23.83	299	0.875	bicubic
170	tv_resnet152	93.320	6.680	98.390	1.610	60.19	224	0.875	bilinear
171	tf_mixnet_l	93.300	6.700	98.030	1.970	7.33	224	0.875	bicubic
172	legacy_seresnet101	93.290	6.710	98.500	1.500	49.33	224	0.875	bilinear
173	selecsls60b	93.290	6.710	98.280	1.720	32.77	224	0.875	bicubic
174	efficientnet_b1	93.270	6.730	98.190	1.810	7.79	240	0.875	bicubic
175	hrnet_w30	93.210	6.790	98.410	1.590	37.71	224	0.875	bilinear
176	efficientnet_es	93.200	6.800	98.410	1.590	5.44	224	0.875	bicubic
177	dla60_res2next	93.180	6.820	98.410	1.590	17.03	224	0.875	bilinear
178	dla60_res2net	93.150	6.850	98.420	1.580	20.85	224	0.875	bilinear
179	regnetx_032	93.120	6.880	98.390	1.610	15.30	224	0.875	bicubic
180	dla60x	93.100	6.900	98.490	1.510	17.35	224	0.875	bilinear
181	dla102	93.090	6.910	98.550	1.450	33.27	224	0.875	bilinear
182	seresnext26t_32x4d	93.060	6.940	98.100	1.900	16.82	224	0.875	bicubic
183	selecsls60	93.050	6.950	98.300	1.700	30.67	224	0.875	bicubic
184	gluon_resnet50_v1c	93.030	6.970	98.370	1.630	25.58	224	0.875	bicubic
185	regnety_016	93.030	6.970	98.360	1.640	11.20	224	0.875	bicubic
186	rexnet_100	93.030	6.970	98.190	1.810	4.80	224	0.875	bicubic
187	legacy_seresnet50	92.950	7.050	98.190	1.810	28.09	224	0.875	bilinear
188	adv_inception_v3	92.890	7.110	98.130	1.870	23.83	299	0.875	bicubic
189	res2next50	92.830	7.170	98.170	1.830	24.67	224	0.875	bilinear
190	tf_efficientnet_cc_b0_8e	92.820	7.180	98.180	1.820	24.01	224	0.875	bicubic
191	seresnext26tn_32x4d	92.810	7.190	98.370	1.630	16.81	224	0.875	bicubic
192	tv_resnet101	92.800	7.200	98.230	1.770	44.55	224	0.875	bilinear
193	efficientnet_b1_pruned	92.770	7.230	98.040	1.960	6.33	240	0.882	bicubic
194	vit_small_patch16_224	92.770	7.230	97.930	2.070	48.75	224	0.900	bicubic
195	tv_resnext50_32x4d	92.760	7.240	98.270	1.730	25.03	224	0.875	bilinear
196	densenet201	92.750	7.250	98.240	1.760	20.01	224	0.875	bicubic
197	seresnext26d_32x4d	92.730	7.270	98.150	1.850	16.81	224	0.875	bicubic
198	inception_v3	92.720	7.280	97.960	2.040	23.83	299	0.875	bicubic
199	res2net50_14w_8s	92.720	7.280	98.190	1.810	25.06	224	0.875	bilinear
200	resnet34d	92.690	7.310	98.300	1.700	21.82	224	0.875	bicubic
201	efficientnet_b0	92.670	7.330	98.080	1.920	5.29	224	0.875	bicubic
202	tf_efficientnet_lite2	92.660	7.340	98.230	1.770	6.09	260	0.890	bicubic
203	legacy_seresnext26_32x4d	92.630	7.370	98.120	1.880	16.79	224	0.875	bicubic
204	tf_efficientnet_lite1	92.620	7.380	98.080	1.920	5.42	240	0.882	bicubic
205	tf_efficientnet_cc_b0_4e	92.610	7.390	98.080	1.920	13.31	224	0.875	bicubic
206	res2net50_48w_2s	92.560	7.440	98.070	1.930	25.29	224	0.875	bilinear
207	tf_efficientnet_es	92.550	7.450	98.290	1.710	5.44	224	0.875	bicubic
208	gluon_resnet50_v1b	92.540	7.460	98.190	1.810	25.56	224	0.875	bicubic
209	res2net50_26w_4s	92.490	7.510	98.060	1.940	25.70	224	0.875	bilinear
210	densenet161	92.490	7.510	98.290	1.710	28.68	224	0.875	bicubic
211	mixnet_m	92.440	7.560	97.870	2.130	5.01	224	0.875	bicubic
212	mobilenetv2_120d	92.400	7.600	98.050	1.950	5.83	224	0.875	bicubic
213	skresnet34	92.380	7.620	98.140	1.860	22.28	224	0.875	bicubic
214	tf_mixnet_m	92.330	7.670	97.890	2.110	5.01	224	0.875	bicubic
215	hrnet_w18	92.310	7.690	98.240	1.760	21.30	224	0.875	bilinear
216	selecsls42b	92.300	7.700	98.150	1.850	32.46	224	0.875	bicubic
217	ese_vovnet19b_dw	92.290	7.710	98.100	1.900	6.54	224	0.875	bicubic
218	tf_efficientnet_b0	92.230	7.770	98.000	2.000	5.29	224	0.875	bicubic
219	tf_efficientnet_b0_ap	92.220	7.780	98.020	1.980	5.29	224	0.875	bicubic
220	dla60	92.220	7.780	98.110	1.890	22.04	224	0.875	bilinear
221	regnetx_016	92.170	7.830	98.210	1.790	9.19	224	0.875	bicubic
222	resnet26d	92.050	7.950	97.950	2.050	16.01	224	0.875	bicubic
223	dpn68	92.010	7.990	98.040	1.960	12.61	224	0.875	bicubic
224	densenetblur121d	91.920	8.080	98.090	1.910	8.00	224	0.875	bicubic
225	densenet169	91.910	8.090	98.100	1.900	14.15	224	0.875	bicubic
226	tv_resnet50	91.880	8.120	98.040	1.960	25.56	224	0.875	bilinear
227	mobilenetv2_140	91.820	8.180	97.860	2.140	6.11	224	0.875	bicubic
228	mixnet_s	91.820	8.180	97.690	2.310	4.13	224	0.875	bicubic
229	regnety_008	91.740	8.260	98.180	1.820	6.26	224	0.875	bicubic
230	resnest14d	91.740	8.260	97.870	2.130	10.61	224	0.875	bilinear
231	densenet121	91.560	8.440	98.020	1.980	7.98	224	0.875	bicubic
232	tf_mixnet_s	91.510	8.490	97.610	2.390	4.13	224	0.875	bicubic
233	regnety_006	91.370	8.630	97.710	2.290	6.06	224	0.875	bicubic
234	mobilenetv3_large_100	91.330	8.670	97.710	2.290	5.48	224	0.875	bicubic
235	semnasnet_100	91.290	8.710	97.570	2.430	3.89	224	0.875	bicubic
236	tf_mobilenetv3_large_100	91.230	8.770	97.660	2.340	5.48	224	0.875	bilinear
237	mobilenetv3_rw	91.210	8.790	97.660	2.340	5.48	224	0.875	bicubic
238	hrnet_w18_small_v2	91.190	8.810	97.900	2.100	15.60	224	0.875	bilinear
239	efficientnet_lite0	91.170	8.830	97.620	2.380	4.65	224	0.875	bicubic
240	resnet26	91.120	8.880	97.730	2.270	16.00	224	0.875	bicubic
241	resnet34	91.110	8.890	97.620	2.380	21.80	224	0.875	bilinear
242	regnetx_008	91.050	8.950	97.710	2.290	7.26	224	0.875	bicubic
243	tf_efficientnet_lite0	91.040	8.960	97.570	2.430	4.65	224	0.875	bicubic
244	gluon_resnet34_v1b	90.990	9.010	97.640	2.360	21.80	224	0.875	bicubic
245	mobilenetv2_110d	90.970	9.030	97.540	2.460	4.52	224	0.875	bicubic
246	tv_densenet121	90.900	9.100	97.710	2.290	7.98	224	0.875	bicubic
247	legacy_seresnet34	90.890	9.110	97.580	2.420	21.96	224	0.875	bilinear
248	dla34	90.760	9.240	97.650	2.350	15.74	224	0.875	bilinear
249	fbnetc_100	90.710	9.290	97.210	2.790	5.57	224	0.875	bilinear
250	swsl_resnet18	90.680	9.320	97.710	2.290	11.69	224	0.875	bilinear
251	mnasnet_100	90.510	9.490	97.470	2.530	4.38	224	0.875	bicubic
252	regnety_004	90.500	9.500	97.560	2.440	4.34	224	0.875	bicubic
253	spnasnet_100	90.350	9.650	97.190	2.810	4.42	224	0.875	bilinear
254	regnetx_006	90.330	9.670	97.430	2.570	6.20	224	0.875	bicubic
255	ssl_resnet18	90.230	9.770	97.560	2.440	11.69	224	0.875	bilinear
256	tv_resnet34	89.920	10.080	97.340	2.660	21.80	224	0.875	bilinear
257	tf_mobilenetv3_large_075	89.710	10.290	97.220	2.780	3.99	224	0.875	bilinear
258	skresnet18	89.660	10.340	97.220	2.780	11.96	224	0.875	bicubic
259	mobilenetv2_100	89.610	10.390	97.140	2.860	3.50	224	0.875	bicubic
260	resnet18d	89.300	10.700	97.150	2.850	11.71	224	0.875	bicubic
261	hrnet_w18_small	89.040	10.960	97.100	2.900	13.19	224	0.875	bilinear
262	tf_mobilenetv3_large_minimal_100	88.960	11.040	96.870	3.130	3.92	224	0.875	bilinear
263	regnetx_004	88.890	11.110	97.120	2.880	5.16	224	0.875	bicubic
264	legacy_seresnet18	88.880	11.120	96.970	3.030	11.78	224	0.875	bicubic
265	gluon_resnet18_v1b	88.370	11.630	96.690	3.310	11.69	224	0.875	bicubic
266	resnet18	87.370	12.630	96.260	3.740	11.69	224	0.875	bilinear
267	regnety_002	87.360	12.640	96.570	3.430	3.16	224	0.875	bicubic
268	dla60x_c	86.280	13.720	96.160	3.840	1.32	224	0.875	bilinear
269	regnetx_002	86.200	13.800	95.970	4.030	2.68	224	0.875	bicubic
270	tf_mobilenetv3_small_100	85.180	14.820	95.780	4.220	2.54	224	0.875	bilinear
271	dla46x_c	84.250	15.750	95.280	4.720	1.07	224	0.875	bilinear
272	dla46_c	83.640	16.360	94.910	5.090	1.30	224	0.875	bilinear
273	tf_mobilenetv3_small_075	83.490	16.510	94.790	5.210	2.04	224	0.875	bilinear
274	tf_mobilenetv3_small_minimal_100	81.380	18.620	93.670	6.330	2.04	224	0.875	bilinear
275	inception_resnet_v2	10.930	89.070	28.870	71.130	55.85	299	0.897	bicubic
276	pnasnet5large	10.690	89.310	31.050	68.950	86.06	331	0.911	bicubic
277	ens_adv_inception_resnet_v2	10.650	89.350	30.490	69.510	55.85	299	0.897	bicubic
278	inception_v4	10.570	89.430	28.260	71.740	42.68	299	0.875	bicubic