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	98.550	1.450	99.820	0.180	480.31	800	0.960	bicubic
3	tf_efficientnet_l2_ns_475	98.500	1.500	99.830	0.170	480.31	475	0.936	bicubic
4	swin_large_patch4_window12_384	98.040	1.960	99.690	0.310	196.74	384	1.000	bicubic
5	tf_efficientnet_b7_ns	97.910	2.090	99.720	0.280	66.35	600	0.949	bicubic
6	swin_base_patch4_window12_384	97.890	2.110	99.710	0.290	87.90	384	1.000	bicubic
7	dm_nfnet_f6	97.730	2.270	99.580	0.420	438.36	576	0.956	bicubic
8	swin_large_patch4_window7_224	97.650	2.350	99.580	0.420	196.53	224	0.900	bicubic
9	tf_efficientnet_b6_ns	97.630	2.370	99.580	0.420	43.04	528	0.942	bicubic
10	ig_resnext101_32x48d	97.620	2.380	99.700	0.300	828.41	224	0.875	bilinear
11	dm_nfnet_f5	97.600	2.400	99.550	0.450	377.21	544	0.954	bicubic
12	dm_nfnet_f4	97.570	2.430	99.520	0.480	316.07	512	0.951	bicubic
13	tf_efficientnet_b5_ns	97.500	2.500	99.630	0.370	30.39	456	0.934	bicubic
14	resnetv2_152x4_bitm	97.490	2.510	99.600	0.400	936.53	480	1.000	bilinear
15	dm_nfnet_f3	97.360	2.640	99.580	0.420	254.92	416	0.940	bicubic
16	ig_resnext101_32x32d	97.360	2.640	99.680	0.320	468.53	224	0.875	bilinear
17	swin_base_patch4_window7_224	97.250	2.750	99.530	0.470	87.77	224	0.900	bicubic
18	swsl_resnext101_32x8d	97.200	2.800	99.570	0.430	88.79	224	0.875	bilinear
19	tf_efficientnet_b7_ap	97.200	2.800	99.540	0.460	66.35	600	0.949	bicubic
20	tf_efficientnet_b8	97.200	2.800	99.500	0.500	87.41	672	0.954	bicubic
21	vit_base_r50_s16_384	97.180	2.820	99.560	0.440	98.95	384	1.000	bicubic
22	resnetv2_152x2_bitm	97.150	2.850	99.590	0.410	236.34	480	1.000	bilinear
23	vit_large_patch16_384	97.110	2.890	99.640	0.360	304.72	384	1.000	bicubic
24	tf_efficientnet_b8_ap	97.110	2.890	99.660	0.340	87.41	672	0.954	bicubic
25	tf_efficientnet_b6_ap	97.080	2.920	99.620	0.380	43.04	528	0.942	bicubic
26	ecaresnet269d	97.080	2.920	99.470	0.530	102.09	352	1.000	bicubic
27	resnetv2_101x3_bitm	97.050	2.950	99.520	0.480	387.93	480	1.000	bilinear
28	tf_efficientnet_b7	97.010	2.990	99.520	0.480	66.35	600	0.949	bicubic
29	dm_nfnet_f2	96.960	3.040	99.450	0.550	193.78	352	0.920	bicubic
30	vit_deit_base_distilled_patch16_384	96.960	3.040	99.480	0.520	87.63	384	1.000	bicubic
31	tf_efficientnet_b4_ns	96.950	3.050	99.580	0.420	19.34	380	0.922	bicubic
32	dm_nfnet_f1	96.920	3.080	99.390	0.610	132.63	320	0.910	bicubic
33	ig_resnext101_32x16d	96.820	3.180	99.590	0.410	194.03	224	0.875	bilinear
34	seresnet152d	96.770	3.230	99.450	0.550	66.84	320	1.000	bicubic
35	resnetv2_50x3_bitm	96.770	3.230	99.430	0.570	217.32	480	1.000	bilinear
36	resnet200d	96.720	3.280	99.330	0.670	64.69	320	1.000	bicubic
37	eca_nfnet_l1	96.700	3.300	99.270	0.730	41.41	320	1.000	bicubic
38	vit_base_patch16_384	96.700	3.300	99.510	0.490	86.86	384	1.000	bicubic
39	pit_b_distilled_224	96.680	3.320	99.350	0.650	74.79	224	0.900	bicubic
40	tf_efficientnet_b5_ap	96.680	3.320	99.460	0.540	30.39	456	0.934	bicubic
41	tf_efficientnet_b6	96.670	3.330	99.370	0.630	43.04	528	0.942	bicubic
42	resnest200e	96.610	3.390	99.350	0.650	70.20	320	0.909	bicubic
43	swsl_resnext101_32x16d	96.600	3.400	99.520	0.480	194.03	224	0.875	bilinear
44	resnest269e	96.520	3.480	99.350	0.650	110.93	416	0.928	bicubic
45	swsl_resnext101_32x4d	96.420	3.580	99.470	0.530	44.18	224	0.875	bilinear
46	tf_efficientnet_b3_ns	96.390	3.610	99.350	0.650	12.23	300	0.904	bicubic
47	resnet152d	96.360	3.640	99.390	0.610	60.21	320	1.000	bicubic
48	regnety_160	96.350	3.650	99.330	0.670	83.59	288	1.000	bicubic
49	tf_efficientnet_b5	96.350	3.650	99.310	0.690	30.39	456	0.934	bicubic
50	ig_resnext101_32x8d	96.320	3.680	99.430	0.570	88.79	224	0.875	bilinear
51	resnet101d	96.290	3.710	99.230	0.770	44.57	320	1.000	bicubic
52	tf_efficientnet_b4_ap	96.160	3.840	99.280	0.720	19.34	380	0.922	bicubic
53	vit_deit_base_patch16_384	96.150	3.850	99.140	0.860	86.86	384	1.000	bicubic
54	dm_nfnet_f0	96.140	3.860	99.240	0.760	71.49	256	0.900	bicubic
55	vit_deit_base_distilled_patch16_224	96.090	3.910	99.190	0.810	87.34	224	0.900	bicubic
56	nfnet_l0	96.090	3.910	99.260	0.740	35.07	288	1.000	bicubic
57	resnetv2_101x1_bitm	96.090	3.910	99.300	0.700	44.54	480	1.000	bilinear
58	regnety_032	95.970	4.030	99.190	0.810	19.44	288	1.000	bicubic
59	tresnet_xl_448	95.970	4.030	99.130	0.870	78.44	448	0.875	bilinear
60	vit_large_patch16_224	95.950	4.050	99.240	0.760	304.33	224	0.900	bicubic
61	eca_nfnet_l0	95.930	4.070	99.210	0.790	24.14	288	1.000	bicubic
62	swin_small_patch4_window7_224	95.910	4.090	99.020	0.980	49.61	224	0.900	bicubic
63	tf_efficientnet_b4	95.900	4.100	99.170	0.830	19.34	380	0.922	bicubic
64	swsl_resnext50_32x4d	95.870	4.130	99.250	0.750	25.03	224	0.875	bilinear
65	tresnet_l_448	95.860	4.140	99.120	0.880	55.99	448	0.875	bilinear
66	resnest101e	95.860	4.140	99.210	0.790	48.28	256	0.875	bilinear
67	vit_large_patch32_384	95.830	4.170	99.150	0.850	306.63	384	1.000	bicubic
68	vit_base_patch32_384	95.810	4.190	99.150	0.850	88.30	384	1.000	bicubic
69	ssl_resnext101_32x16d	95.800	4.200	99.180	0.820	194.03	224	0.875	bilinear
70	tf_efficientnet_b2_ns	95.770	4.230	99.120	0.880	9.11	260	0.890	bicubic
71	efficientnet_b3a	95.710	4.290	99.040	0.960	12.23	320	1.000	bicubic
72	pnasnet5large	95.710	4.290	98.920	1.080	86.06	331	0.911	bicubic
73	nasnetalarge	95.680	4.320	98.930	1.070	88.75	331	0.911	bicubic
74	pit_b_224	95.640	4.360	98.660	1.340	73.76	224	0.900	bicubic
75	efficientnet_b3	95.580	4.420	99.100	0.900	12.23	300	0.904	bicubic
76	efficientnet_v2s	95.540	4.460	98.960	1.040	23.94	224	1.000	bicubic
77	ecaresnet101d	95.530	4.470	99.130	0.870	44.57	224	0.875	bicubic
78	ecaresnet50t	95.510	4.490	99.120	0.880	25.57	320	0.950	bicubic
79	ssl_resnext101_32x8d	95.470	4.530	99.110	0.890	88.79	224	0.875	bilinear
80	ssl_resnext101_32x4d	95.440	4.560	99.130	0.870	44.18	224	0.875	bilinear
81	tresnet_xl	95.440	4.560	99.050	0.950	78.44	224	0.875	bilinear
82	vit_deit_base_patch16_224	95.440	4.560	98.840	1.160	86.57	224	0.900	bicubic
83	swsl_resnet50	95.410	4.590	99.290	0.710	25.56	224	0.875	bilinear
84	vit_base_patch16_224	95.330	4.670	99.000	1.000	86.57	224	0.900	bicubic
85	tf_efficientnet_b3_ap	95.320	4.680	98.900	1.100	12.23	300	0.904	bicubic
86	tresnet_l	95.290	4.710	99.010	0.990	55.99	224	0.875	bilinear
87	pit_s_distilled_224	95.240	4.760	99.050	0.950	24.04	224	0.900	bicubic
88	tf_efficientnet_b1_ns	95.170	4.830	99.110	0.890	7.79	240	0.882	bicubic
89	swin_tiny_patch4_window7_224	95.140	4.860	98.850	1.150	28.29	224	0.900	bicubic
90	efficientnet_el	95.120	4.880	98.990	1.010	10.59	300	0.904	bicubic
91	gernet_l	95.090	4.910	98.900	1.100	31.08	256	0.875	bilinear
92	ecaresnet101d_pruned	95.080	4.920	98.980	1.020	24.88	224	0.875	bicubic
93	wide_resnet50_2	95.080	4.920	98.970	1.030	68.88	224	0.875	bicubic
94	legacy_senet154	95.070	4.930	98.830	1.170	115.09	224	0.875	bilinear
95	resnetv2_50x1_bitm	95.050	4.950	99.160	0.840	25.55	480	1.000	bilinear
96	seresnext50_32x4d	95.040	4.960	98.880	1.120	27.56	224	0.875	bicubic
97	tnt_s_patch16_224	95.040	4.960	98.830	1.170	23.76	224	0.900	bicubic
98	gluon_resnet152_v1s	95.040	4.960	98.930	1.070	60.32	224	0.875	bicubic
99	tf_efficientnet_b3	95.010	4.990	98.910	1.090	12.23	300	0.904	bicubic
100	tresnet_m_448	94.990	5.010	98.980	1.020	31.39	448	0.875	bilinear
101	resnest50d_4s2x40d	94.960	5.040	99.070	0.930	30.42	224	0.875	bicubic
102	rexnet_200	94.940	5.060	99.010	0.990	16.37	224	0.875	bicubic
103	gluon_seresnext101_64x4d	94.930	5.070	98.830	1.170	88.23	224	0.875	bicubic
104	gluon_senet154	94.920	5.080	98.760	1.240	115.09	224	0.875	bicubic
105	gluon_seresnext101_32x4d	94.920	5.080	98.810	1.190	48.96	224	0.875	bicubic
106	tf_efficientnet_lite4	94.890	5.110	99.020	0.980	13.01	380	0.920	bilinear
107	ssl_resnext50_32x4d	94.870	5.130	98.880	1.120	25.03	224	0.875	bilinear
108	resnest50d	94.830	5.170	98.880	1.120	27.48	224	0.875	bilinear
109	ecaresnetlight	94.770	5.230	98.800	1.200	30.16	224	0.875	bicubic
110	resnest50d_1s4x24d	94.750	5.250	98.980	1.020	25.68	224	0.875	bicubic
111	gluon_resnet152_v1d	94.740	5.260	98.740	1.260	60.21	224	0.875	bicubic
112	gluon_resnet101_v1s	94.720	5.280	98.820	1.180	44.67	224	0.875	bicubic
113	vit_deit_small_distilled_patch16_224	94.710	5.290	99.030	0.970	22.44	224	0.900	bicubic
114	efficientnet_b2	94.700	5.300	98.670	1.330	9.11	260	0.875	bicubic
115	gluon_resnext101_64x4d	94.670	5.330	98.650	1.350	83.46	224	0.875	bicubic
116	cspdarknet53	94.660	5.340	98.800	1.200	27.64	256	0.887	bilinear
117	ecaresnet50d	94.630	5.370	98.890	1.110	25.58	224	0.875	bicubic
118	efficientnet_b3_pruned	94.630	5.370	98.760	1.240	9.86	300	0.904	bicubic
119	tresnet_m	94.620	5.380	98.550	1.450	31.39	224	0.875	bilinear
120	gernet_m	94.620	5.380	98.860	1.140	21.14	224	0.875	bilinear
121	efficientnet_b2a	94.610	5.390	98.710	1.290	9.11	288	1.000	bicubic
122	nf_resnet50	94.590	5.410	98.810	1.190	25.56	288	0.940	bicubic
123	pit_s_224	94.590	5.410	98.710	1.290	23.46	224	0.900	bicubic
124	repvgg_b3	94.570	5.430	98.780	1.220	123.09	224	0.875	bilinear
125	seresnet50	94.550	5.450	98.750	1.250	28.09	224	0.875	bicubic
126	inception_resnet_v2	94.540	5.460	98.790	1.210	55.84	299	0.897	bicubic
127	regnety_320	94.540	5.460	98.850	1.150	145.05	224	0.875	bicubic
128	gluon_resnext101_32x4d	94.530	5.470	98.630	1.370	44.18	224	0.875	bicubic
129	repvgg_b3g4	94.520	5.480	98.970	1.030	83.83	224	0.875	bilinear
130	tf_efficientnet_b2_ap	94.490	5.510	98.620	1.380	9.11	260	0.890	bicubic
131	cspresnext50	94.480	5.520	98.680	1.320	20.57	224	0.875	bilinear
132	regnety_120	94.480	5.520	98.810	1.190	51.82	224	0.875	bicubic
133	rexnet_150	94.480	5.520	98.790	1.210	9.73	224	0.875	bicubic
134	regnetx_320	94.460	5.540	98.740	1.260	107.81	224	0.875	bicubic
135	ssl_resnet50	94.450	5.550	98.920	1.080	25.56	224	0.875	bilinear
136	tf_efficientnet_el	94.410	5.590	98.710	1.290	10.59	300	0.904	bicubic
137	efficientnet_el_pruned	94.400	5.600	98.740	1.260	10.59	300	0.904	bicubic
138	vit_deit_small_patch16_224	94.400	5.600	98.690	1.310	22.05	224	0.900	bicubic
139	inception_v4	94.380	5.620	98.580	1.420	42.68	299	0.875	bicubic
140	legacy_seresnext101_32x4d	94.370	5.630	98.650	1.350	48.96	224	0.875	bilinear
141	tf_efficientnet_b2	94.360	5.640	98.610	1.390	9.11	260	0.890	bicubic
142	gluon_seresnext50_32x4d	94.340	5.660	98.610	1.390	27.56	224	0.875	bicubic
143	dpn107	94.310	5.690	98.480	1.520	86.92	224	0.875	bicubic
144	ecaresnet26t	94.310	5.690	98.720	1.280	16.01	320	0.950	bicubic
145	xception71	94.280	5.720	98.640	1.360	42.34	299	0.903	bicubic
146	gluon_xception65	94.260	5.740	98.570	1.430	39.92	299	0.903	bicubic
147	resnet50d	94.260	5.740	98.720	1.280	25.58	224	0.875	bicubic
148	skresnext50_32x4d	94.260	5.740	98.460	1.540	27.48	224	0.875	bicubic
149	regnetx_120	94.240	5.760	98.650	1.350	46.11	224	0.875	bicubic
150	dpn92	94.230	5.770	98.730	1.270	37.67	224	0.875	bicubic
151	gluon_resnet101_v1d	94.220	5.780	98.550	1.450	44.57	224	0.875	bicubic
152	ecaresnet50d_pruned	94.220	5.780	98.730	1.270	19.94	224	0.875	bicubic
153	tf_efficientnet_lite3	94.200	5.800	98.640	1.360	8.20	300	0.904	bilinear
154	mixnet_xl	94.190	5.810	98.340	1.660	11.90	224	0.875	bicubic
155	resnext50d_32x4d	94.180	5.820	98.570	1.430	25.05	224	0.875	bicubic
156	regnety_080	94.170	5.830	98.680	1.320	39.18	224	0.875	bicubic
157	ens_adv_inception_resnet_v2	94.160	5.840	98.600	1.400	55.84	299	0.897	bicubic
158	gluon_resnet152_v1c	94.160	5.840	98.640	1.360	60.21	224	0.875	bicubic
159	regnety_064	94.150	5.850	98.730	1.270	30.58	224	0.875	bicubic
160	efficientnet_b2_pruned	94.140	5.860	98.530	1.470	8.31	260	0.890	bicubic
161	nf_regnet_b1	94.130	5.870	98.630	1.370	10.22	288	0.900	bicubic
162	dpn98	94.130	5.870	98.570	1.430	61.57	224	0.875	bicubic
163	regnetx_160	94.120	5.880	98.750	1.250	54.28	224	0.875	bicubic
164	resnext50_32x4d	94.100	5.900	98.350	1.650	25.03	224	0.875	bicubic
165	ese_vovnet39b	94.090	5.910	98.660	1.340	24.57	224	0.875	bicubic
166	gluon_resnet152_v1b	94.080	5.920	98.450	1.550	60.19	224	0.875	bicubic
167	dpn131	94.010	5.990	98.720	1.280	79.25	224	0.875	bicubic
168	hrnet_w64	94.010	5.990	98.610	1.390	128.06	224	0.875	bilinear
169	resnetblur50	93.960	6.040	98.590	1.410	25.56	224	0.875	bicubic
170	dla102x2	93.950	6.050	98.490	1.510	41.28	224	0.875	bilinear
171	hrnet_w48	93.920	6.080	98.610	1.390	77.47	224	0.875	bilinear
172	tf_efficientnet_cc_b1_8e	93.900	6.100	98.260	1.740	39.72	240	0.882	bicubic
173	rexnet_130	93.900	6.100	98.400	1.600	7.56	224	0.875	bicubic
174	regnetx_064	93.890	6.110	98.630	1.370	26.21	224	0.875	bicubic
175	regnetx_080	93.870	6.130	98.520	1.480	39.57	224	0.875	bicubic
176	regnety_040	93.860	6.140	98.650	1.350	20.65	224	0.875	bicubic
177	repvgg_b2g4	93.860	6.140	98.590	1.410	61.76	224	0.875	bilinear
178	efficientnet_em	93.840	6.160	98.810	1.190	6.90	240	0.882	bicubic
179	resnext101_32x8d	93.830	6.170	98.580	1.420	88.79	224	0.875	bilinear
180	gluon_resnext50_32x4d	93.810	6.190	98.410	1.590	25.03	224	0.875	bicubic
181	pit_xs_distilled_224	93.810	6.190	98.670	1.330	11.00	224	0.900	bicubic
182	resnet50	93.810	6.190	98.390	1.610	25.56	224	0.875	bicubic
183	gluon_resnet50_v1d	93.770	6.230	98.390	1.610	25.58	224	0.875	bicubic
184	xception65	93.760	6.240	98.370	1.630	39.92	299	0.903	bicubic
185	res2net101_26w_4s	93.750	6.250	98.310	1.690	45.21	224	0.875	bilinear
186	gluon_resnet101_v1b	93.750	6.250	98.380	1.620	44.55	224	0.875	bicubic
187	cspresnet50	93.740	6.260	98.640	1.360	21.62	256	0.887	bilinear
188	legacy_seresnext50_32x4d	93.730	6.270	98.580	1.420	27.56	224	0.875	bilinear
189	wide_resnet101_2	93.720	6.280	98.540	1.460	126.89	224	0.875	bilinear
190	dpn68b	93.690	6.310	98.510	1.490	12.61	224	0.875	bicubic
191	tf_efficientnet_b1_ap	93.690	6.310	98.360	1.640	7.79	240	0.882	bicubic
192	gluon_resnet101_v1c	93.670	6.330	98.420	1.580	44.57	224	0.875	bicubic
193	tf_efficientnet_b0_ns	93.630	6.370	98.640	1.360	5.29	224	0.875	bicubic
194	gluon_resnet50_v1s	93.620	6.380	98.460	1.540	25.68	224	0.875	bicubic
195	regnetx_040	93.560	6.440	98.540	1.460	22.12	224	0.875	bicubic
196	hrnet_w44	93.550	6.450	98.700	1.300	67.06	224	0.875	bilinear
197	res2net50_26w_8s	93.540	6.460	98.260	1.740	48.40	224	0.875	bilinear
198	hrnet_w32	93.530	6.470	98.450	1.550	41.23	224	0.875	bilinear
199	dla102x	93.520	6.480	98.510	1.490	26.31	224	0.875	bilinear
200	repvgg_b2	93.500	6.500	98.730	1.270	89.02	224	0.875	bilinear
201	tf_efficientnet_b1	93.500	6.500	98.360	1.640	7.79	240	0.882	bicubic
202	hrnet_w40	93.490	6.510	98.580	1.420	57.56	224	0.875	bilinear
203	gluon_inception_v3	93.460	6.540	98.570	1.430	23.83	299	0.875	bicubic
204	xception	93.460	6.540	98.530	1.470	22.86	299	0.897	bicubic
205	mixnet_l	93.450	6.550	98.220	1.780	7.33	224	0.875	bicubic
206	xception41	93.430	6.570	98.430	1.570	26.97	299	0.903	bicubic
207	res2net50_26w_6s	93.410	6.590	98.280	1.720	37.05	224	0.875	bilinear
208	legacy_seresnet152	93.400	6.600	98.350	1.650	66.82	224	0.875	bilinear
209	dla169	93.340	6.660	98.600	1.400	53.39	224	0.875	bilinear
210	repvgg_b1	93.330	6.670	98.510	1.490	57.42	224	0.875	bilinear
211	resnest26d	93.330	6.670	98.630	1.370	17.07	224	0.875	bilinear
212	tf_inception_v3	93.320	6.680	98.030	1.970	23.83	299	0.875	bicubic
213	tf_mixnet_l	93.310	6.690	98.030	1.970	7.33	224	0.875	bicubic
214	selecsls60b	93.300	6.700	98.280	1.720	32.77	224	0.875	bicubic
215	tv_resnet152	93.300	6.700	98.390	1.610	60.19	224	0.875	bilinear
216	legacy_seresnet101	93.280	6.720	98.510	1.490	49.33	224	0.875	bilinear
217	efficientnet_b1	93.260	6.740	98.170	1.830	7.79	240	0.875	bicubic
218	hrnet_w30	93.200	6.800	98.410	1.590	37.71	224	0.875	bilinear
219	dla60_res2net	93.180	6.820	98.420	1.580	20.85	224	0.875	bilinear
220	dla60_res2next	93.180	6.820	98.410	1.590	17.03	224	0.875	bilinear
221	efficientnet_es	93.140	6.860	98.420	1.580	5.44	224	0.875	bicubic
222	dla60x	93.120	6.880	98.510	1.490	17.35	224	0.875	bilinear
223	regnetx_032	93.120	6.880	98.390	1.610	15.30	224	0.875	bicubic
224	pit_xs_224	93.110	6.890	98.310	1.690	10.62	224	0.900	bicubic
225	dla102	93.060	6.940	98.540	1.460	33.27	224	0.875	bilinear
226	rexnet_100	93.030	6.970	98.190	1.810	4.80	224	0.875	bicubic
227	selecsls60	93.030	6.970	98.300	1.700	30.67	224	0.875	bicubic
228	regnety_016	93.030	6.970	98.360	1.640	11.20	224	0.875	bicubic
229	gluon_resnet50_v1c	93.030	6.970	98.390	1.610	25.58	224	0.875	bicubic
230	repvgg_b1g4	92.980	7.020	98.430	1.570	39.97	224	0.875	bilinear
231	legacy_seresnet50	92.960	7.040	98.190	1.810	28.09	224	0.875	bilinear
232	hardcorenas_f	92.950	7.050	98.160	1.840	8.20	224	0.875	bilinear
233	tf_efficientnet_em	92.930	7.070	98.190	1.810	6.90	240	0.882	bicubic
234	adv_inception_v3	92.880	7.120	98.140	1.860	23.83	299	0.875	bicubic
235	res2next50	92.840	7.160	98.180	1.820	24.67	224	0.875	bilinear
236	tf_efficientnet_cc_b0_8e	92.830	7.170	98.180	1.820	24.01	224	0.875	bicubic
237	seresnext26t_32x4d	92.820	7.180	98.370	1.630	16.81	224	0.875	bicubic
238	tv_resnet101	92.810	7.190	98.250	1.750	44.55	224	0.875	bilinear
239	efficientnet_b1_pruned	92.770	7.230	98.040	1.960	6.33	240	0.882	bicubic
240	vit_small_patch16_224	92.760	7.240	97.930	2.070	48.75	224	0.900	bicubic
241	densenet201	92.750	7.250	98.230	1.770	20.01	224	0.875	bicubic
242	res2net50_14w_8s	92.740	7.260	98.180	1.820	25.06	224	0.875	bilinear
243	tv_resnext50_32x4d	92.740	7.260	98.270	1.730	25.03	224	0.875	bilinear
244	inception_v3	92.720	7.280	97.970	2.030	23.83	299	0.875	bicubic
245	seresnext26d_32x4d	92.700	7.300	98.150	1.850	16.81	224	0.875	bicubic
246	efficientnet_b0	92.690	7.310	98.070	1.930	5.29	224	0.875	bicubic
247	resnet34d	92.680	7.320	98.310	1.690	21.82	224	0.875	bicubic
248	tf_efficientnet_lite2	92.650	7.350	98.230	1.770	6.09	260	0.890	bicubic
249	legacy_seresnext26_32x4d	92.640	7.360	98.130	1.870	16.79	224	0.875	bicubic
250	tf_efficientnet_lite1	92.620	7.380	98.080	1.920	5.42	240	0.882	bicubic
251	tf_efficientnet_cc_b0_4e	92.590	7.410	98.080	1.920	13.31	224	0.875	bicubic
252	hardcorenas_e	92.570	7.430	98.110	1.890	8.07	224	0.875	bilinear
253	res2net50_48w_2s	92.550	7.450	98.080	1.920	25.29	224	0.875	bilinear
254	gluon_resnet50_v1b	92.540	7.460	98.170	1.830	25.56	224	0.875	bicubic
255	densenet161	92.500	7.500	98.290	1.710	28.68	224	0.875	bicubic
256	res2net50_26w_4s	92.500	7.500	98.060	1.940	25.70	224	0.875	bilinear
257	mixnet_m	92.430	7.570	97.870	2.130	5.01	224	0.875	bicubic
258	mobilenetv2_120d	92.400	7.600	98.050	1.950	5.83	224	0.875	bicubic
259	hardcorenas_d	92.400	7.600	98.070	1.930	7.50	224	0.875	bilinear
260	skresnet34	92.390	7.610	98.150	1.850	22.28	224	0.875	bicubic
261	tf_mixnet_m	92.330	7.670	97.890	2.110	5.01	224	0.875	bicubic
262	hrnet_w18	92.320	7.680	98.240	1.760	21.30	224	0.875	bilinear
263	ese_vovnet19b_dw	92.290	7.710	98.090	1.910	6.54	224	0.875	bicubic
264	selecsls42b	92.280	7.720	98.150	1.850	32.46	224	0.875	bicubic
265	tf_efficientnet_b0	92.250	7.750	98.000	2.000	5.29	224	0.875	bicubic
266	dla60	92.230	7.770	98.110	1.890	22.04	224	0.875	bilinear
267	tf_efficientnet_b0_ap	92.200	7.800	98.020	1.980	5.29	224	0.875	bicubic
268	regnetx_016	92.170	7.830	98.210	1.790	9.19	224	0.875	bicubic
269	gernet_s	92.140	7.860	98.190	1.810	8.17	224	0.875	bilinear
270	resnet26d	92.070	7.930	97.960	2.040	16.01	224	0.875	bicubic
271	hardcorenas_c	92.020	7.980	97.840	2.160	5.52	224	0.875	bilinear
272	dpn68	92.010	7.990	98.050	1.950	12.61	224	0.875	bicubic
273	tf_efficientnet_es	91.980	8.020	97.860	2.140	5.44	224	0.875	bicubic
274	repvgg_a2	91.940	8.060	98.150	1.850	28.21	224	0.875	bilinear
275	densenet169	91.930	8.070	98.100	1.900	14.15	224	0.875	bicubic
276	densenetblur121d	91.910	8.090	98.070	1.930	8.00	224	0.875	bicubic
277	tv_resnet50	91.880	8.120	98.040	1.960	25.56	224	0.875	bilinear
278	mixnet_s	91.830	8.170	97.690	2.310	4.13	224	0.875	bicubic
279	mobilenetv2_140	91.830	8.170	97.860	2.140	6.11	224	0.875	bicubic
280	hardcorenas_b	91.770	8.230	97.780	2.220	5.18	224	0.875	bilinear
281	regnety_008	91.750	8.250	98.180	1.820	6.26	224	0.875	bicubic
282	resnest14d	91.720	8.280	97.870	2.130	10.61	224	0.875	bilinear
283	densenet121	91.570	8.430	98.030	1.970	7.98	224	0.875	bicubic
284	tf_mixnet_s	91.510	8.490	97.620	2.380	4.13	224	0.875	bicubic
285	repvgg_b0	91.430	8.570	97.990	2.010	15.82	224	0.875	bilinear
286	regnety_006	91.370	8.630	97.710	2.290	6.06	224	0.875	bicubic
287	hardcorenas_a	91.350	8.650	97.860	2.140	5.26	224	0.875	bilinear
288	mobilenetv3_large_100	91.320	8.680	97.710	2.290	5.48	224	0.875	bicubic
289	semnasnet_100	91.280	8.720	97.560	2.440	3.89	224	0.875	bicubic
290	tf_mobilenetv3_large_100	91.240	8.760	97.660	2.340	5.48	224	0.875	bilinear
291	mobilenetv3_rw	91.210	8.790	97.660	2.340	5.48	224	0.875	bicubic
292	hrnet_w18_small_v2	91.190	8.810	97.900	2.100	15.60	224	0.875	bilinear
293	efficientnet_es_pruned	91.180	8.820	97.750	2.250	5.44	224	0.875	bicubic
294	efficientnet_lite0	91.140	8.860	97.630	2.370	4.65	224	0.875	bicubic
295	resnet34	91.130	8.870	97.620	2.380	21.80	224	0.875	bilinear
296	resnet26	91.110	8.890	97.740	2.260	16.00	224	0.875	bicubic
297	regnetx_008	91.050	8.950	97.710	2.290	7.26	224	0.875	bicubic
298	tf_efficientnet_lite0	91.040	8.960	97.590	2.410	4.65	224	0.875	bicubic
299	gluon_resnet34_v1b	90.960	9.040	97.630	2.370	21.80	224	0.875	bicubic
300	mobilenetv2_110d	90.950	9.050	97.550	2.450	4.52	224	0.875	bicubic
301	pit_ti_distilled_224	90.900	9.100	97.700	2.300	5.10	224	0.900	bicubic
302	legacy_seresnet34	90.890	9.110	97.580	2.420	21.96	224	0.875	bilinear
303	tv_densenet121	90.890	9.110	97.710	2.290	7.98	224	0.875	bicubic
304	dla34	90.760	9.240	97.660	2.340	15.74	224	0.875	bilinear
305	fbnetc_100	90.700	9.300	97.210	2.790	5.57	224	0.875	bilinear
306	vit_deit_tiny_distilled_patch16_224	90.700	9.300	97.570	2.430	5.91	224	0.900	bicubic
307	swsl_resnet18	90.690	9.310	97.700	2.300	11.69	224	0.875	bilinear
308	mnasnet_100	90.510	9.490	97.470	2.530	4.38	224	0.875	bicubic
309	regnety_004	90.500	9.500	97.540	2.460	4.34	224	0.875	bicubic
310	regnetx_006	90.350	9.650	97.430	2.570	6.20	224	0.875	bicubic
311	spnasnet_100	90.350	9.650	97.190	2.810	4.42	224	0.875	bilinear
312	ssl_resnet18	90.220	9.780	97.550	2.450	11.69	224	0.875	bilinear
313	vgg16_bn	90.090	9.910	97.370	2.630	138.37	224	0.875	bilinear
314	vgg19_bn	90.080	9.920	97.580	2.420	143.68	224	0.875	bilinear
315	pit_ti_224	89.940	10.060	97.450	2.550	4.85	224	0.900	bicubic
316	tv_resnet34	89.940	10.060	97.340	2.660	21.80	224	0.875	bilinear
317	tf_mobilenetv3_large_075	89.680	10.320	97.210	2.790	3.99	224	0.875	bilinear
318	vit_deit_tiny_patch16_224	89.670	10.330	97.450	2.550	5.72	224	0.900	bicubic
319	skresnet18	89.660	10.340	97.230	2.770	11.96	224	0.875	bicubic
320	mobilenetv2_100	89.600	10.400	97.140	2.860	3.50	224	0.875	bicubic
321	resnet18d	89.280	10.720	97.150	2.850	11.71	224	0.875	bicubic
322	hrnet_w18_small	89.050	10.950	97.110	2.890	13.19	224	0.875	bilinear
323	vgg19	89.040	10.960	96.870	3.130	143.67	224	0.875	bilinear
324	tf_mobilenetv3_large_minimal_100	88.970	11.030	96.860	3.140	3.92	224	0.875	bilinear
325	regnetx_004	88.900	11.100	97.120	2.880	5.16	224	0.875	bicubic
326	legacy_seresnet18	88.880	11.120	96.980	3.020	11.78	224	0.875	bicubic
327	vgg13_bn	88.760	11.240	96.970	3.030	133.05	224	0.875	bilinear
328	vgg16	88.550	11.450	96.790	3.210	138.36	224	0.875	bilinear
329	gluon_resnet18_v1b	88.400	11.600	96.680	3.320	11.69	224	0.875	bicubic
330	vgg11_bn	87.500	12.500	96.820	3.180	132.87	224	0.875	bilinear
331	resnet18	87.390	12.610	96.290	3.710	11.69	224	0.875	bilinear
332	regnety_002	87.380	12.620	96.590	3.410	3.16	224	0.875	bicubic
333	vgg13	87.050	12.950	96.320	3.680	133.05	224	0.875	bilinear
334	vgg11	86.550	13.450	96.280	3.720	132.86	224	0.875	bilinear
335	dla60x_c	86.290	13.710	96.160	3.840	1.32	224	0.875	bilinear
336	regnetx_002	86.190	13.810	95.980	4.020	2.68	224	0.875	bicubic
337	tf_mobilenetv3_small_100	85.190	14.810	95.770	4.230	2.54	224	0.875	bilinear
338	dla46x_c	84.250	15.750	95.270	4.730	1.07	224	0.875	bilinear
339	dla46_c	83.650	16.350	94.920	5.080	1.30	224	0.875	bilinear
340	tf_mobilenetv3_small_075	83.520	16.480	94.790	5.210	2.04	224	0.875	bilinear
341	tf_mobilenetv3_small_minimal_100	81.380	18.620	93.670	6.330	2.04	224	0.875	bilinear