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	tf_efficientnet_b7_ns	97.200	2.800	99.700	0.300	66.35	600	0.949	bicubic
5	swin_large_patch4_window12_384	97.170	2.830	99.680	0.320	196.74	384	1.000	bicubic
6	swin_base_patch4_window12_384	97.120	2.880	99.780	0.220	87.90	384	1.000	bicubic
7	tf_efficientnet_b6_ns	97.020	2.980	99.710	0.290	43.04	528	0.942	bicubic
8	dm_nfnet_f6	96.990	3.010	99.740	0.260	438.36	576	0.956	bicubic
9	ig_resnext101_32x48d	96.970	3.030	99.670	0.330	828.41	224	0.875	bilinear
10	swin_large_patch4_window7_224	96.950	3.050	99.660	0.340	196.53	224	0.900	bicubic
11	resnetv2_152x4_bitm	96.880	3.120	99.660	0.340	936.53	480	1.000	bilinear
12	tf_efficientnet_b5_ns	96.870	3.130	99.640	0.360	30.39	456	0.934	bicubic
13	dm_nfnet_f4	96.820	3.180	99.600	0.400	316.07	512	0.951	bicubic
14	ig_resnext101_32x32d	96.780	3.220	99.530	0.470	468.53	224	0.875	bilinear
15	dm_nfnet_f5	96.710	3.290	99.680	0.320	377.21	544	0.954	bicubic
16	tf_efficientnet_b4_ns	96.710	3.290	99.640	0.360	19.34	380	0.922	bicubic
17	tf_efficientnet_b8	96.700	3.300	99.530	0.470	87.41	672	0.954	bicubic
18	swin_base_patch4_window7_224	96.680	3.320	99.660	0.340	87.77	224	0.900	bicubic
19	dm_nfnet_f3	96.630	3.370	99.640	0.360	254.92	416	0.940	bicubic
20	tf_efficientnet_b7	96.580	3.420	99.510	0.490	66.35	600	0.949	bicubic
21	tf_efficientnet_b8_ap	96.550	3.450	99.540	0.460	87.41	672	0.954	bicubic
22	vit_deit_base_distilled_patch16_384	96.510	3.490	99.590	0.410	87.63	384	1.000	bicubic
23	dm_nfnet_f2	96.500	3.500	99.570	0.430	193.78	352	0.920	bicubic
24	resnetv2_152x2_bitm	96.500	3.500	99.620	0.380	236.34	480	1.000	bilinear
25	ecaresnet269d	96.460	3.540	99.610	0.390	102.09	352	1.000	bicubic
26	vit_base_r50_s16_384	96.450	3.550	99.660	0.340	98.95	384	1.000	bicubic
27	ig_resnext101_32x16d	96.440	3.560	99.540	0.460	194.03	224	0.875	bilinear
28	dm_nfnet_f1	96.370	3.630	99.470	0.530	132.63	320	0.910	bicubic
29	tf_efficientnet_b6_ap	96.370	3.630	99.550	0.450	43.04	528	0.942	bicubic
30	resnetv2_101x3_bitm	96.360	3.640	99.600	0.400	387.93	480	1.000	bilinear
31	vit_large_patch16_384	96.360	3.640	99.630	0.370	304.72	384	1.000	bicubic
32	tf_efficientnet_b7_ap	96.350	3.650	99.590	0.410	66.35	600	0.949	bicubic
33	seresnet152d	96.310	3.690	99.510	0.490	66.84	320	1.000	bicubic
34	tf_efficientnet_b6	96.290	3.710	99.520	0.480	43.04	528	0.942	bicubic
35	swsl_resnext101_32x16d	96.270	3.730	99.500	0.500	194.03	224	0.875	bilinear
36	swsl_resnext101_32x8d	96.240	3.760	99.590	0.410	88.79	224	0.875	bilinear
37	vit_base_patch16_384	96.190	3.810	99.530	0.470	86.86	384	1.000	bicubic
38	resnetv2_50x3_bitm	96.140	3.860	99.620	0.380	217.32	480	1.000	bilinear
39	resnest269e	96.120	3.880	99.520	0.480	110.93	416	0.928	bicubic
40	resnet200d	96.110	3.890	99.460	0.540	64.69	320	1.000	bicubic
41	tf_efficientnet_b3_ns	96.100	3.900	99.480	0.520	12.23	300	0.904	bicubic
42	tf_efficientnet_b5_ap	96.080	3.920	99.540	0.460	30.39	456	0.934	bicubic
43	resnest200e	96.070	3.930	99.480	0.520	70.20	320	0.909	bicubic
44	pit_b_distilled_224	96.070	3.930	99.380	0.620	74.79	224	0.900	bicubic
45	swsl_resnext101_32x4d	96.050	3.950	99.530	0.470	44.18	224	0.875	bilinear
46	tf_efficientnet_b5	95.980	4.020	99.450	0.550	30.39	456	0.934	bicubic
47	eca_nfnet_l1	95.930	4.070	99.500	0.500	41.41	320	1.000	bicubic
48	ig_resnext101_32x8d	95.930	4.070	99.380	0.620	88.79	224	0.875	bilinear
49	regnety_160	95.880	4.120	99.560	0.440	83.59	288	1.000	bicubic
50	resnet152d	95.870	4.130	99.430	0.570	60.21	320	1.000	bicubic
51	resnet101d	95.750	4.250	99.440	0.560	44.57	320	1.000	bicubic
52	vit_deit_base_distilled_patch16_224	95.750	4.250	99.280	0.720	87.34	224	0.900	bicubic
53	swin_small_patch4_window7_224	95.720	4.280	99.290	0.710	49.61	224	0.900	bicubic
54	vit_deit_base_patch16_384	95.650	4.350	99.240	0.760	86.86	384	1.000	bicubic
55	dm_nfnet_f0	95.630	4.370	99.300	0.700	71.49	256	0.900	bicubic
56	swsl_resnext50_32x4d	95.620	4.380	99.440	0.560	25.03	224	0.875	bilinear
57	tf_efficientnet_b4	95.590	4.410	99.330	0.670	19.34	380	0.922	bicubic
58	resnest101e	95.570	4.430	99.270	0.730	48.28	256	0.875	bilinear
59	tf_efficientnet_b2_ns	95.520	4.480	99.340	0.660	9.11	260	0.890	bicubic
60	resnetv2_101x1_bitm	95.510	4.490	99.510	0.490	44.54	480	1.000	bilinear
61	tresnet_xl_448	95.510	4.490	99.340	0.660	78.44	448	0.875	bilinear
62	tf_efficientnet_b4_ap	95.490	4.510	99.390	0.610	19.34	380	0.922	bicubic
63	eca_nfnet_l0	95.470	4.530	99.380	0.620	24.14	288	1.000	bicubic
64	regnety_032	95.470	4.530	99.320	0.680	19.44	288	1.000	bicubic
65	tresnet_l_448	95.410	4.590	99.300	0.700	55.99	448	0.875	bilinear
66	ssl_resnext101_32x16d	95.410	4.590	99.410	0.590	194.03	224	0.875	bilinear
67	nfnet_l0	95.390	4.610	99.420	0.580	35.07	288	1.000	bicubic
68	pnasnet5large	95.360	4.640	99.130	0.870	86.06	331	0.911	bicubic
69	ssl_resnext101_32x8d	95.340	4.660	99.320	0.680	88.79	224	0.875	bilinear
70	vit_large_patch16_224	95.290	4.710	99.310	0.690	304.33	224	0.900	bicubic
71	vit_base_patch32_384	95.260	4.740	99.180	0.820	88.30	384	1.000	bicubic
72	vit_large_patch32_384	95.240	4.760	99.320	0.680	306.63	384	1.000	bicubic
73	vit_base_patch16_224	95.210	4.790	99.230	0.770	86.57	224	0.900	bicubic
74	swsl_resnet50	95.200	4.800	99.390	0.610	25.56	224	0.875	bilinear
75	ecaresnet101d	95.160	4.840	99.230	0.770	44.57	224	0.875	bicubic
76	ssl_resnext101_32x4d	95.160	4.840	99.300	0.700	44.18	224	0.875	bilinear
77	nasnetalarge	95.150	4.850	99.130	0.870	88.75	331	0.911	bicubic
78	efficientnet_b3a	95.140	4.860	99.210	0.790	12.23	320	1.000	bicubic
79	ecaresnet50t	95.070	4.930	99.290	0.710	25.57	320	0.950	bicubic
80	tresnet_xl	95.060	4.940	99.260	0.740	78.44	224	0.875	bilinear
81	vit_deit_base_patch16_224	95.010	4.990	98.980	1.020	86.57	224	0.900	bicubic
82	efficientnet_v2s	94.990	5.010	99.080	0.920	23.94	224	1.000	bicubic
83	efficientnet_b3	94.970	5.030	99.230	0.770	12.23	300	0.904	bicubic
84	tf_efficientnet_b3_ap	94.970	5.030	99.110	0.890	12.23	300	0.904	bicubic
85	gernet_l	94.930	5.070	99.200	0.800	31.08	256	0.875	bilinear
86	tf_efficientnet_b3	94.910	5.090	99.110	0.890	12.23	300	0.904	bicubic
87	tresnet_l	94.900	5.100	99.030	0.970	55.99	224	0.875	bilinear
88	tf_efficientnet_lite4	94.870	5.130	99.090	0.910	13.01	380	0.920	bilinear
89	tf_efficientnet_b1_ns	94.860	5.140	99.250	0.750	7.79	240	0.882	bicubic
90	seresnext50_32x4d	94.820	5.180	99.130	0.870	27.56	224	0.875	bicubic
91	pit_b_224	94.790	5.210	98.820	1.180	73.76	224	0.900	bicubic
92	legacy_senet154	94.730	5.270	99.100	0.900	115.09	224	0.875	bilinear
93	pit_s_distilled_224	94.730	5.270	99.190	0.810	24.04	224	0.900	bicubic
94	gluon_resnet152_v1s	94.720	5.280	99.060	0.940	60.32	224	0.875	bicubic
95	gluon_senet154	94.710	5.290	98.970	1.030	115.09	224	0.875	bicubic
96	resnest50d_4s2x40d	94.710	5.290	99.130	0.870	30.42	224	0.875	bicubic
97	ssl_resnext50_32x4d	94.700	5.300	99.240	0.760	25.03	224	0.875	bilinear
98	efficientnet_el	94.670	5.330	99.130	0.870	10.59	300	0.904	bicubic
99	wide_resnet50_2	94.670	5.330	99.050	0.950	68.88	224	0.875	bicubic
100	tresnet_m_448	94.660	5.340	99.150	0.850	31.39	448	0.875	bilinear
101	rexnet_200	94.660	5.340	99.090	0.910	16.37	224	0.875	bicubic
102	gluon_seresnext101_64x4d	94.650	5.350	98.980	1.020	88.23	224	0.875	bicubic
103	resnest50d	94.620	5.380	99.030	0.970	27.48	224	0.875	bilinear
104	swin_tiny_patch4_window7_224	94.620	5.380	99.120	0.880	28.29	224	0.900	bicubic
105	pit_s_224	94.590	5.410	98.930	1.070	23.46	224	0.900	bicubic
106	vit_deit_small_distilled_patch16_224	94.590	5.410	99.100	0.900	22.44	224	0.900	bicubic
107	efficientnet_b3_pruned	94.580	5.420	99.070	0.930	9.86	300	0.904	bicubic
108	tnt_s_patch16_224	94.580	5.420	99.180	0.820	23.76	224	0.900	bicubic
109	resnetv2_50x1_bitm	94.550	5.450	99.250	0.750	25.55	480	1.000	bilinear
110	repvgg_b3	94.550	5.450	98.910	1.090	123.09	224	0.875	bilinear
111	gernet_m	94.550	5.450	98.930	1.070	21.14	224	0.875	bilinear
112	regnety_320	94.520	5.480	99.170	0.830	145.05	224	0.875	bicubic
113	repvgg_b3g4	94.490	5.510	99.020	0.980	83.83	224	0.875	bilinear
114	ecaresnet101d_pruned	94.450	5.550	99.100	0.900	24.88	224	0.875	bicubic
115	gluon_seresnext101_32x4d	94.450	5.550	99.090	0.910	48.96	224	0.875	bicubic
116	gluon_resnet152_v1d	94.440	5.560	99.010	0.990	60.21	224	0.875	bicubic
117	nf_resnet50	94.410	5.590	99.100	0.900	25.56	288	0.940	bicubic
118	resnest50d_1s4x24d	94.390	5.610	99.070	0.930	25.68	224	0.875	bicubic
119	inception_v4	94.380	5.620	98.820	1.180	42.68	299	0.875	bicubic
120	efficientnet_b2a	94.370	5.630	99.050	0.950	9.11	288	1.000	bicubic
121	tf_efficientnet_el	94.360	5.640	99.100	0.900	10.59	300	0.904	bicubic
122	gluon_resnext101_64x4d	94.350	5.650	98.880	1.120	83.46	224	0.875	bicubic
123	efficientnet_b2	94.340	5.660	99.100	0.900	9.11	260	0.875	bicubic
124	inception_resnet_v2	94.340	5.660	98.800	1.200	55.84	299	0.897	bicubic
125	ssl_resnet50	94.310	5.690	99.150	0.850	25.56	224	0.875	bilinear
126	regnetx_120	94.270	5.730	99.190	0.810	46.11	224	0.875	bicubic
127	rexnet_150	94.270	5.730	99.080	0.920	9.73	224	0.875	bicubic
128	tf_efficientnet_b2_ap	94.270	5.730	98.950	1.050	9.11	260	0.890	bicubic
129	mixnet_xl	94.230	5.770	98.820	1.180	11.90	224	0.875	bicubic
130	tf_efficientnet_b2	94.210	5.790	99.030	0.970	9.11	260	0.890	bicubic
131	regnetx_320	94.210	5.790	99.050	0.950	107.81	224	0.875	bicubic
132	dpn92	94.190	5.810	98.930	1.070	37.67	224	0.875	bicubic
133	ecaresnet50d	94.190	5.810	99.020	0.980	25.58	224	0.875	bicubic
134	gluon_resnet101_v1d	94.170	5.830	98.940	1.060	44.57	224	0.875	bicubic
135	gluon_resnet101_v1s	94.170	5.830	99.010	0.990	44.67	224	0.875	bicubic
136	gluon_seresnext50_32x4d	94.170	5.830	98.910	1.090	27.56	224	0.875	bicubic
137	ecaresnetlight	94.140	5.860	98.950	1.050	30.16	224	0.875	bicubic
138	regnety_064	94.140	5.860	99.030	0.970	30.58	224	0.875	bicubic
139	ens_adv_inception_resnet_v2	94.130	5.870	98.790	1.210	55.84	299	0.897	bicubic
140	legacy_seresnext101_32x4d	94.130	5.870	98.970	1.030	48.96	224	0.875	bilinear
141	tf_efficientnet_lite3	94.130	5.870	98.960	1.040	8.20	300	0.904	bilinear
142	gluon_resnext101_32x4d	94.120	5.880	98.930	1.070	44.18	224	0.875	bicubic
143	efficientnet_el_pruned	94.090	5.910	99.010	0.990	10.59	300	0.904	bicubic
144	cspdarknet53	94.090	5.910	98.980	1.020	27.64	256	0.887	bilinear
145	seresnet50	94.080	5.920	98.970	1.030	28.09	224	0.875	bicubic
146	resnet50d	94.070	5.930	98.920	1.080	25.58	224	0.875	bicubic
147	tresnet_m	94.070	5.930	98.830	1.170	31.39	224	0.875	bilinear
148	gluon_resnet152_v1b	94.030	5.970	98.740	1.260	60.19	224	0.875	bicubic
149	hrnet_w48	94.030	5.970	99.040	0.960	77.47	224	0.875	bilinear
150	gluon_xception65	94.010	5.990	99.020	0.980	39.92	299	0.903	bicubic
151	regnety_120	94.010	5.990	99.030	0.970	51.82	224	0.875	bicubic
152	vit_deit_small_patch16_224	94.000	6.000	98.960	1.040	22.05	224	0.900	bicubic
153	dla102x2	94.000	6.000	99.030	0.970	41.28	224	0.875	bilinear
154	dpn107	93.960	6.040	98.840	1.160	86.92	224	0.875	bicubic
155	skresnext50_32x4d	93.950	6.050	98.820	1.180	27.48	224	0.875	bicubic
156	dpn98	93.940	6.060	98.920	1.080	61.57	224	0.875	bicubic
157	ecaresnet26t	93.940	6.060	98.920	1.080	16.01	320	0.950	bicubic
158	nf_regnet_b1	93.890	6.110	98.750	1.250	10.22	288	0.900	bicubic
159	regnety_080	93.890	6.110	99.000	1.000	39.18	224	0.875	bicubic
160	xception71	93.890	6.110	98.950	1.050	42.34	299	0.903	bicubic
161	gluon_resnet152_v1c	93.880	6.120	98.800	1.200	60.21	224	0.875	bicubic
162	regnetx_160	93.880	6.120	99.090	0.910	54.28	224	0.875	bicubic
163	cspresnet50	93.860	6.140	98.870	1.130	21.62	256	0.887	bilinear
164	ese_vovnet39b	93.850	6.150	98.900	1.100	24.57	224	0.875	bicubic
165	resnext50_32x4d	93.840	6.160	98.830	1.170	25.03	224	0.875	bicubic
166	hrnet_w64	93.830	6.170	98.930	1.070	128.06	224	0.875	bilinear
167	ecaresnet50d_pruned	93.820	6.180	99.000	1.000	19.94	224	0.875	bicubic
168	repvgg_b2g4	93.820	6.180	98.930	1.070	61.76	224	0.875	bilinear
169	resnext50d_32x4d	93.810	6.190	98.740	1.260	25.05	224	0.875	bicubic
170	dla169	93.800	6.200	98.840	1.160	53.39	224	0.875	bilinear
171	efficientnet_b2_pruned	93.800	6.200	98.910	1.090	8.31	260	0.890	bicubic
172	regnetx_080	93.790	6.210	98.910	1.090	39.57	224	0.875	bicubic
173	resnext101_32x8d	93.770	6.230	98.950	1.050	88.79	224	0.875	bilinear
174	gluon_resnet101_v1b	93.760	6.240	98.700	1.300	44.55	224	0.875	bicubic
175	xception65	93.760	6.240	98.860	1.140	39.92	299	0.903	bicubic
176	cspresnext50	93.760	6.240	98.840	1.160	20.57	224	0.875	bilinear
177	dpn131	93.760	6.240	98.800	1.200	79.25	224	0.875	bicubic
178	efficientnet_em	93.740	6.260	98.930	1.070	6.90	240	0.882	bicubic
179	tf_efficientnet_b0_ns	93.740	6.260	98.980	1.020	5.29	224	0.875	bicubic
180	wide_resnet101_2	93.730	6.270	98.810	1.190	126.89	224	0.875	bilinear
181	hrnet_w40	93.710	6.290	98.800	1.200	57.56	224	0.875	bilinear
182	resnetblur50	93.710	6.290	98.810	1.190	25.56	224	0.875	bicubic
183	tf_efficientnet_b1	93.710	6.290	98.800	1.200	7.79	240	0.882	bicubic
184	gluon_resnet101_v1c	93.690	6.310	98.760	1.240	44.57	224	0.875	bicubic
185	regnetx_040	93.680	6.320	98.940	1.060	22.12	224	0.875	bicubic
186	rexnet_130	93.670	6.330	98.710	1.290	7.56	224	0.875	bicubic
187	gluon_resnext50_32x4d	93.650	6.350	98.690	1.310	25.03	224	0.875	bicubic
188	xception	93.640	6.360	98.770	1.230	22.86	299	0.897	bicubic
189	regnetx_064	93.630	6.370	99.050	0.950	26.21	224	0.875	bicubic
190	tf_efficientnet_b1_ap	93.630	6.370	98.800	1.200	7.79	240	0.882	bicubic
191	dpn68b	93.620	6.380	98.700	1.300	12.61	224	0.875	bicubic
192	hrnet_w44	93.620	6.380	98.960	1.040	67.06	224	0.875	bilinear
193	regnety_040	93.620	6.380	98.950	1.050	20.65	224	0.875	bicubic
194	res2net50_26w_6s	93.590	6.410	98.750	1.250	37.05	224	0.875	bilinear
195	gluon_resnet50_v1s	93.590	6.410	98.840	1.160	25.68	224	0.875	bicubic
196	repvgg_b2	93.590	6.410	99.070	0.930	89.02	224	0.875	bilinear
197	dla60_res2next	93.570	6.430	98.800	1.200	17.03	224	0.875	bilinear
198	tf_efficientnet_cc_b1_8e	93.570	6.430	98.690	1.310	39.72	240	0.882	bicubic
199	gluon_inception_v3	93.540	6.460	98.830	1.170	23.83	299	0.875	bicubic
200	dla102x	93.530	6.470	98.850	1.150	26.31	224	0.875	bilinear
201	gluon_resnet50_v1d	93.530	6.470	98.710	1.290	25.58	224	0.875	bicubic
202	res2net101_26w_4s	93.520	6.480	98.600	1.400	45.21	224	0.875	bilinear
203	selecsls60b	93.500	6.500	98.840	1.160	32.77	224	0.875	bicubic
204	xception41	93.480	6.520	98.750	1.250	26.97	299	0.903	bicubic
205	resnet50	93.460	6.540	98.600	1.400	25.56	224	0.875	bicubic
206	res2net50_26w_8s	93.450	6.550	98.700	1.300	48.40	224	0.875	bilinear
207	legacy_seresnet152	93.440	6.560	98.850	1.150	66.82	224	0.875	bilinear
208	legacy_seresnext50_32x4d	93.430	6.570	98.800	1.200	27.56	224	0.875	bilinear
209	repvgg_b1	93.410	6.590	98.790	1.210	57.42	224	0.875	bilinear
210	dla60_res2net	93.380	6.620	98.860	1.140	20.85	224	0.875	bilinear
211	hrnet_w30	93.370	6.630	98.830	1.170	37.71	224	0.875	bilinear
212	dla102	93.260	6.740	98.780	1.220	33.27	224	0.875	bilinear
213	legacy_seresnet101	93.260	6.740	98.740	1.260	49.33	224	0.875	bilinear
214	mixnet_l	93.260	6.740	98.700	1.300	7.33	224	0.875	bicubic
215	regnetx_032	93.250	6.750	98.730	1.270	15.30	224	0.875	bicubic
216	pit_xs_distilled_224	93.240	6.760	98.820	1.180	11.00	224	0.900	bicubic
217	tv_resnet152	93.240	6.760	98.750	1.250	60.19	224	0.875	bilinear
218	resnest26d	93.240	6.760	98.850	1.150	17.07	224	0.875	bilinear
219	tf_inception_v3	93.200	6.800	98.480	1.520	23.83	299	0.875	bicubic
220	dla60x	93.190	6.810	98.710	1.290	17.35	224	0.875	bilinear
221	res2net50_26w_4s	93.180	6.820	98.670	1.330	25.70	224	0.875	bilinear
222	tf_efficientnet_em	93.170	6.830	98.670	1.330	6.90	240	0.882	bicubic
223	res2next50	93.150	6.850	98.660	1.340	24.67	224	0.875	bilinear
224	efficientnet_b1	93.060	6.940	98.540	1.460	7.79	240	0.875	bicubic
225	tf_mixnet_l	93.040	6.960	98.540	1.460	7.33	224	0.875	bicubic
226	res2net50_14w_8s	93.030	6.970	98.700	1.300	25.06	224	0.875	bilinear
227	repvgg_b1g4	93.030	6.970	98.820	1.180	39.97	224	0.875	bilinear
228	adv_inception_v3	93.010	6.990	98.490	1.510	23.83	299	0.875	bicubic
229	selecsls60	93.010	6.990	98.830	1.170	30.67	224	0.875	bicubic
230	regnety_016	93.000	7.000	98.680	1.320	11.20	224	0.875	bicubic
231	efficientnet_b1_pruned	92.980	7.020	98.530	1.470	6.33	240	0.882	bicubic
232	hardcorenas_f	92.980	7.020	98.620	1.380	8.20	224	0.875	bilinear
233	hardcorenas_e	92.950	7.050	98.570	1.430	8.07	224	0.875	bilinear
234	hrnet_w32	92.950	7.050	98.840	1.160	41.23	224	0.875	bilinear
235	pit_xs_224	92.910	7.090	98.780	1.220	10.62	224	0.900	bicubic
236	gluon_resnet50_v1c	92.910	7.090	98.710	1.290	25.58	224	0.875	bicubic
237	efficientnet_es	92.910	7.090	98.690	1.310	5.44	224	0.875	bicubic
238	densenet161	92.900	7.100	98.810	1.190	28.68	224	0.875	bicubic
239	inception_v3	92.900	7.100	98.330	1.670	23.83	299	0.875	bicubic
240	tv_resnext50_32x4d	92.900	7.100	98.720	1.280	25.03	224	0.875	bilinear
241	tv_resnet101	92.880	7.120	98.660	1.340	44.55	224	0.875	bilinear
242	tf_efficientnet_cc_b0_8e	92.870	7.130	98.460	1.540	24.01	224	0.875	bicubic
243	rexnet_100	92.850	7.150	98.620	1.380	4.80	224	0.875	bicubic
244	tf_efficientnet_cc_b0_4e	92.840	7.160	98.440	1.560	13.31	224	0.875	bicubic
245	seresnext26t_32x4d	92.820	7.180	98.560	1.440	16.81	224	0.875	bicubic
246	res2net50_48w_2s	92.790	7.210	98.470	1.530	25.29	224	0.875	bilinear
247	hrnet_w18	92.760	7.240	98.660	1.340	21.30	224	0.875	bilinear
248	densenet201	92.690	7.310	98.650	1.350	20.01	224	0.875	bicubic
249	repvgg_a2	92.680	7.320	98.520	1.480	28.21	224	0.875	bilinear
250	dla60	92.670	7.330	98.630	1.370	22.04	224	0.875	bilinear
251	legacy_seresnet50	92.670	7.330	98.650	1.350	28.09	224	0.875	bilinear
252	resnet34d	92.640	7.360	98.420	1.580	21.82	224	0.875	bicubic
253	mobilenetv2_120d	92.610	7.390	98.510	1.490	5.83	224	0.875	bicubic
254	tf_efficientnet_b0_ap	92.610	7.390	98.370	1.630	5.29	224	0.875	bicubic
255	hardcorenas_d	92.600	7.400	98.430	1.570	7.50	224	0.875	bilinear
256	vit_small_patch16_224	92.600	7.400	98.390	1.610	48.75	224	0.900	bicubic
257	tf_efficientnet_lite2	92.590	7.410	98.550	1.450	6.09	260	0.890	bicubic
258	legacy_seresnext26_32x4d	92.570	7.430	98.420	1.580	16.79	224	0.875	bicubic
259	skresnet34	92.570	7.430	98.520	1.480	22.28	224	0.875	bicubic
260	gluon_resnet50_v1b	92.560	7.440	98.550	1.450	25.56	224	0.875	bicubic
261	regnetx_016	92.540	7.460	98.550	1.450	9.19	224	0.875	bicubic
262	efficientnet_b0	92.480	7.520	98.680	1.320	5.29	224	0.875	bicubic
263	selecsls42b	92.480	7.520	98.440	1.560	32.46	224	0.875	bicubic
264	gernet_s	92.440	7.560	98.500	1.500	8.17	224	0.875	bilinear
265	seresnext26d_32x4d	92.440	7.560	98.540	1.460	16.81	224	0.875	bicubic
266	densenetblur121d	92.400	7.600	98.410	1.590	8.00	224	0.875	bicubic
267	tf_efficientnet_b0	92.400	7.600	98.470	1.530	5.29	224	0.875	bicubic
268	hardcorenas_c	92.330	7.670	98.340	1.660	5.52	224	0.875	bilinear
269	tf_efficientnet_lite1	92.310	7.690	98.490	1.510	5.42	240	0.882	bicubic
270	densenet169	92.300	7.700	98.590	1.410	14.15	224	0.875	bicubic
271	mixnet_m	92.270	7.730	98.350	1.650	5.01	224	0.875	bicubic
272	dpn68	92.240	7.760	98.610	1.390	12.61	224	0.875	bicubic
273	resnet26d	92.230	7.770	98.450	1.550	16.01	224	0.875	bicubic
274	tf_mixnet_m	92.200	7.800	98.420	1.580	5.01	224	0.875	bicubic
275	tv_resnet50	92.140	7.860	98.420	1.580	25.56	224	0.875	bilinear
276	tf_efficientnet_es	92.100	7.900	98.440	1.560	5.44	224	0.875	bicubic
277	mobilenetv2_140	92.030	7.970	98.250	1.750	6.11	224	0.875	bicubic
278	ese_vovnet19b_dw	92.010	7.990	98.510	1.490	6.54	224	0.875	bicubic
279	hardcorenas_b	91.940	8.060	98.400	1.600	5.18	224	0.875	bilinear
280	densenet121	91.940	8.060	98.280	1.720	7.98	224	0.875	bicubic
281	regnety_008	91.900	8.100	98.420	1.580	6.26	224	0.875	bicubic
282	mixnet_s	91.780	8.220	98.300	1.700	4.13	224	0.875	bicubic
283	efficientnet_es_pruned	91.700	8.300	98.420	1.580	5.44	224	0.875	bicubic
284	repvgg_b0	91.680	8.320	98.450	1.550	15.82	224	0.875	bilinear
285	tf_mixnet_s	91.680	8.320	98.240	1.760	4.13	224	0.875	bicubic
286	semnasnet_100	91.660	8.340	98.270	1.730	3.89	224	0.875	bicubic
287	hardcorenas_a	91.620	8.380	98.170	1.830	5.26	224	0.875	bilinear
288	regnety_006	91.570	8.430	98.430	1.570	6.06	224	0.875	bicubic
289	mobilenetv3_rw	91.550	8.450	98.270	1.730	5.48	224	0.875	bicubic
290	mobilenetv3_large_100	91.480	8.520	98.320	1.680	5.48	224	0.875	bicubic
291	legacy_seresnet34	91.480	8.520	98.200	1.800	21.96	224	0.875	bilinear
292	resnet26	91.440	8.560	98.280	1.720	16.00	224	0.875	bicubic
293	tf_mobilenetv3_large_100	91.420	8.580	98.260	1.740	5.48	224	0.875	bilinear
294	tv_densenet121	91.400	8.600	98.250	1.750	7.98	224	0.875	bicubic
295	mobilenetv2_110d	91.350	8.650	98.190	1.810	4.52	224	0.875	bicubic
296	tf_efficientnet_lite0	91.300	8.700	98.090	1.910	4.65	224	0.875	bicubic
297	fbnetc_100	91.270	8.730	97.830	2.170	5.57	224	0.875	bilinear
298	efficientnet_lite0	91.260	8.740	98.250	1.750	4.65	224	0.875	bicubic
299	dla34	91.240	8.760	98.180	1.820	15.74	224	0.875	bilinear
300	resnet34	91.200	8.800	98.240	1.760	21.80	224	0.875	bilinear
301	mnasnet_100	91.200	8.800	98.050	1.950	4.38	224	0.875	bicubic
302	regnetx_008	91.180	8.820	98.380	1.620	7.26	224	0.875	bicubic
303	hrnet_w18_small_v2	91.170	8.830	98.340	1.660	15.60	224	0.875	bilinear
304	resnest14d	91.130	8.870	98.330	1.670	10.61	224	0.875	bilinear
305	gluon_resnet34_v1b	91.100	8.900	98.180	1.820	21.80	224	0.875	bicubic
306	vit_deit_tiny_distilled_patch16_224	91.100	8.900	98.270	1.730	5.91	224	0.900	bicubic
307	swsl_resnet18	91.090	8.910	98.210	1.790	11.69	224	0.875	bilinear
308	vgg19_bn	90.990	9.010	98.110	1.890	143.68	224	0.875	bilinear
309	pit_ti_distilled_224	90.900	9.100	98.220	1.780	5.10	224	0.900	bicubic
310	regnety_004	90.780	9.220	98.080	1.920	4.34	224	0.875	bicubic
311	regnetx_006	90.760	9.240	98.100	1.900	6.20	224	0.875	bicubic
312	ssl_resnet18	90.700	9.300	98.020	1.980	11.69	224	0.875	bilinear
313	spnasnet_100	90.610	9.390	97.950	2.050	4.42	224	0.875	bilinear
314	vgg16_bn	90.540	9.460	97.990	2.010	138.37	224	0.875	bilinear
315	pit_ti_224	90.420	9.580	98.010	1.990	4.85	224	0.900	bicubic
316	tf_mobilenetv3_large_075	90.320	9.680	97.870	2.130	3.99	224	0.875	bilinear
317	tv_resnet34	90.290	9.710	97.980	2.020	21.80	224	0.875	bilinear
318	skresnet18	90.160	9.840	97.780	2.220	11.96	224	0.875	bicubic
319	resnet18d	89.990	10.010	97.830	2.170	11.71	224	0.875	bicubic
320	hrnet_w18_small	89.880	10.120	97.900	2.100	13.19	224	0.875	bilinear
321	mobilenetv2_100	89.830	10.170	97.830	2.170	3.50	224	0.875	bicubic
322	vgg19	89.680	10.320	97.550	2.450	143.67	224	0.875	bilinear
323	vit_deit_tiny_patch16_224	89.620	10.380	97.960	2.040	5.72	224	0.900	bicubic
324	regnetx_004	89.460	10.540	97.770	2.230	5.16	224	0.875	bicubic
325	vgg16	89.360	10.640	97.520	2.480	138.36	224	0.875	bilinear
326	legacy_seresnet18	89.270	10.730	97.680	2.320	11.78	224	0.875	bicubic
327	vgg13_bn	89.200	10.800	97.530	2.470	133.05	224	0.875	bilinear
328	tf_mobilenetv3_large_minimal_100	89.180	10.820	97.320	2.680	3.92	224	0.875	bilinear
329	gluon_resnet18_v1b	88.660	11.340	97.100	2.900	11.69	224	0.875	bicubic
330	vgg11_bn	88.390	11.610	97.270	2.730	132.87	224	0.875	bilinear
331	regnety_002	88.200	11.800	97.430	2.570	3.16	224	0.875	bicubic
332	resnet18	88.150	11.850	97.120	2.880	11.69	224	0.875	bilinear
333	vgg13	87.570	12.430	97.120	2.880	133.05	224	0.875	bilinear
334	regnetx_002	87.380	12.620	96.990	3.010	2.68	224	0.875	bicubic
335	vgg11	87.340	12.660	97.110	2.890	132.86	224	0.875	bilinear
336	dla60x_c	87.110	12.890	97.140	2.860	1.32	224	0.875	bilinear
337	tf_mobilenetv3_small_100	85.960	14.040	96.400	3.600	2.54	224	0.875	bilinear
338	dla46x_c	85.480	14.520	96.440	3.560	1.07	224	0.875	bilinear
339	dla46_c	84.660	15.340	96.200	3.800	1.30	224	0.875	bilinear
340	tf_mobilenetv3_small_075	84.530	15.470	95.890	4.110	2.04	224	0.875	bilinear
341	tf_mobilenetv3_small_minimal_100	82.670	17.330	95.000	5.000	2.04	224	0.875	bilinear