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	tf_efficientnet_b7_ns	97.910	2.090	99.720	0.280	66.35	600	0.949	bicubic
5	tf_efficientnet_b6_ns	97.630	2.370	99.580	0.420	43.04	528	0.942	bicubic
6	ig_resnext101_32x48d	97.620	2.380	99.700	0.300	828.41	224	0.875	bilinear
7	tf_efficientnet_b5_ns	97.500	2.500	99.630	0.370	30.39	456	0.934	bicubic
8	ig_resnext101_32x32d	97.360	2.640	99.680	0.320	468.53	224	0.875	bilinear
9	swsl_resnext101_32x8d	97.200	2.800	99.570	0.430	88.79	224	0.875	bilinear
10	tf_efficientnet_b7_ap	97.200	2.800	99.540	0.460	66.35	600	0.949	bicubic
11	tf_efficientnet_b8	97.200	2.800	99.500	0.500	87.41	672	0.954	bicubic
12	tf_efficientnet_b8_ap	97.110	2.890	99.660	0.340	87.41	672	0.954	bicubic
13	tf_efficientnet_b6_ap	97.080	2.920	99.620	0.380	43.04	528	0.942	bicubic
14	tf_efficientnet_b7	97.010	2.990	99.520	0.480	66.35	600	0.949	bicubic
15	tf_efficientnet_b4_ns	96.950	3.050	99.580	0.420	19.34	380	0.922	bicubic
16	ig_resnext101_32x16d	96.820	3.180	99.590	0.410	194.03	224	0.875	bilinear
17	tf_efficientnet_b5_ap	96.680	3.320	99.460	0.540	30.39	456	0.934	bicubic
18	tf_efficientnet_b6	96.670	3.330	99.370	0.630	43.04	528	0.942	bicubic
19	resnest200e	96.610	3.390	99.350	0.650	70.20	320	0.909	bicubic
20	swsl_resnext101_32x16d	96.600	3.400	99.520	0.480	194.03	224	0.875	bilinear
21	resnest269e	96.520	3.480	99.350	0.650	110.93	416	0.928	bicubic
22	swsl_resnext101_32x4d	96.420	3.580	99.470	0.530	44.18	224	0.875	bilinear
23	tf_efficientnet_b3_ns	96.390	3.610	99.350	0.650	12.23	300	0.904	bicubic
24	tf_efficientnet_b5	96.350	3.650	99.310	0.690	30.39	456	0.934	bicubic
25	ig_resnext101_32x8d	96.320	3.680	99.430	0.570	88.79	224	0.875	bilinear
26	tf_efficientnet_b4_ap	96.160	3.840	99.280	0.720	19.34	380	0.922	bicubic
27	tresnet_xl_448	95.970	4.030	99.130	0.870	78.44	448	0.875	bilinear
28	tf_efficientnet_b4	95.900	4.100	99.170	0.830	19.34	380	0.922	bicubic
29	swsl_resnext50_32x4d	95.870	4.130	99.250	0.750	25.03	224	0.875	bilinear
30	resnest101e	95.860	4.140	99.210	0.790	48.28	256	0.875	bilinear
31	tresnet_l_448	95.860	4.140	99.120	0.880	55.99	448	0.875	bilinear
32	ssl_resnext101_32x16d	95.800	4.200	99.180	0.820	194.03	224	0.875	bilinear
33	tf_efficientnet_b2_ns	95.770	4.230	99.120	0.880	9.11	260	0.890	bicubic
34	pnasnet5large	95.710	4.290	98.920	1.080	86.06	331	0.911	bicubic
35	nasnetalarge	95.680	4.320	98.930	1.070	88.75	331	0.911	bicubic
36	ecaresnet101d	95.530	4.470	99.130	0.870	44.57	224	0.875	bicubic
37	ssl_resnext101_32x8d	95.470	4.530	99.110	0.890	88.79	224	0.875	bilinear
38	ssl_resnext101_32x4d	95.440	4.560	99.130	0.870	44.18	224	0.875	bilinear
39	tresnet_xl	95.440	4.560	99.050	0.950	78.44	224	0.875	bilinear
40	swsl_resnet50	95.410	4.590	99.290	0.710	25.56	224	0.875	bilinear
41	tf_efficientnet_b3_ap	95.320	4.680	98.900	1.100	12.23	300	0.904	bicubic
42	tresnet_l	95.290	4.710	99.010	0.990	55.99	224	0.875	bilinear
43	efficientnet_b3a	95.260	4.740	98.930	1.070	12.23	320	1.000	bicubic
44	tf_efficientnet_b1_ns	95.170	4.830	99.110	0.890	7.79	240	0.882	bicubic
45	ecaresnet101d_pruned	95.080	4.920	98.980	1.020	24.88	224	0.875	bicubic
46	efficientnet_b3	95.080	4.920	98.980	1.020	12.23	300	0.904	bicubic
47	gluon_resnet152_v1s	95.040	4.960	98.930	1.070	60.32	224	0.875	bicubic
48	tf_efficientnet_b3	95.010	4.990	98.910	1.090	12.23	300	0.904	bicubic
49	tresnet_m_448	94.990	5.010	98.980	1.020	31.39	448	0.875	bilinear
50	resnest50d_4s2x40d	94.960	5.040	99.070	0.930	30.42	224	0.875	bicubic
51	rexnet_200	94.940	5.060	99.010	0.990	16.37	224	0.875	bicubic
52	gluon_seresnext101_64x4d	94.930	5.070	98.830	1.170	88.23	224	0.875	bicubic
53	gluon_senet154	94.920	5.080	98.760	1.240	115.09	224	0.875	bicubic
54	gluon_seresnext101_32x4d	94.920	5.080	98.810	1.190	48.96	224	0.875	bicubic
55	tf_efficientnet_lite4	94.890	5.110	99.020	0.980	13.01	380	0.920	bilinear
56	ssl_resnext50_32x4d	94.870	5.130	98.880	1.120	25.03	224	0.875	bilinear
57	resnest50d	94.830	5.170	98.880	1.120	27.48	224	0.875	bilinear
58	ecaresnetlight	94.770	5.230	98.800	1.200	30.16	224	0.875	bicubic
59	resnest50d_1s4x24d	94.750	5.250	98.980	1.020	25.68	224	0.875	bicubic
60	gluon_resnet152_v1d	94.740	5.260	98.740	1.260	60.21	224	0.875	bicubic
61	gluon_resnet101_v1s	94.720	5.280	98.820	1.180	44.67	224	0.875	bicubic
62	efficientnet_b2	94.700	5.300	98.670	1.330	9.11	260	0.875	bicubic
63	gluon_resnext101_64x4d	94.670	5.330	98.650	1.350	83.46	224	0.875	bicubic
64	cspdarknet53	94.660	5.340	98.800	1.200	27.64	256	0.887	bilinear
65	ecaresnet50d	94.630	5.370	98.890	1.110	25.58	224	0.875	bicubic
66	efficientnet_b3_pruned	94.630	5.370	98.760	1.240	9.86	300	0.904	bicubic
67	tresnet_m	94.620	5.380	98.550	1.450	31.39	224	0.875	bilinear
68	efficientnet_b2a	94.610	5.390	98.710	1.290	9.11	288	1.000	bicubic
69	tf_efficientnet_el	94.590	5.410	98.750	1.250	10.59	300	0.904	bicubic
70	seresnet50	94.550	5.450	98.750	1.250	28.09	224	0.875	bicubic
71	inception_resnet_v2	94.540	5.460	98.790	1.210	55.84	299	0.897	bicubic
72	regnety_320	94.540	5.460	98.850	1.150	145.05	224	0.875	bicubic
73	gluon_resnext101_32x4d	94.530	5.470	98.630	1.370	44.18	224	0.875	bicubic
74	tf_efficientnet_b2_ap	94.490	5.510	98.620	1.380	9.11	260	0.890	bicubic
75	regnety_120	94.480	5.520	98.810	1.190	51.82	224	0.875	bicubic
76	rexnet_150	94.480	5.520	98.790	1.210	9.73	224	0.875	bicubic
77	cspresnext50	94.480	5.520	98.680	1.320	20.57	224	0.875	bilinear
78	regnetx_320	94.460	5.540	98.740	1.260	107.81	224	0.875	bicubic
79	ssl_resnet50	94.450	5.550	98.920	1.080	25.56	224	0.875	bilinear
80	inception_v4	94.380	5.620	98.580	1.420	42.68	299	0.875	bicubic
81	tf_efficientnet_b2	94.360	5.640	98.610	1.390	9.11	260	0.890	bicubic
82	gluon_seresnext50_32x4d	94.340	5.660	98.610	1.390	27.56	224	0.875	bicubic
83	regnety_160	94.340	5.660	98.850	1.150	83.59	224	0.875	bicubic
84	dpn107	94.310	5.690	98.480	1.520	86.92	224	0.875	bicubic
85	xception71	94.280	5.720	98.640	1.360	42.34	299	0.903	bicubic
86	gluon_xception65	94.260	5.740	98.570	1.430	39.92	299	0.903	bicubic
87	skresnext50_32x4d	94.260	5.740	98.460	1.540	27.48	224	0.875	bicubic
88	regnetx_120	94.240	5.760	98.650	1.350	46.11	224	0.875	bicubic
89	dpn92	94.230	5.770	98.730	1.270	37.67	224	0.875	bicubic
90	ecaresnet50d_pruned	94.220	5.780	98.730	1.270	19.94	224	0.875	bicubic
91	gluon_resnet101_v1d	94.220	5.780	98.550	1.450	44.57	224	0.875	bicubic
92	tf_efficientnet_lite3	94.200	5.800	98.640	1.360	8.20	300	0.904	bilinear
93	mixnet_xl	94.190	5.810	98.340	1.660	11.90	224	0.875	bicubic
94	resnext50d_32x4d	94.180	5.820	98.570	1.430	25.05	224	0.875	bicubic
95	regnety_080	94.170	5.830	98.680	1.320	39.18	224	0.875	bicubic
96	ens_adv_inception_resnet_v2	94.160	5.840	98.600	1.400	55.84	299	0.897	bicubic
97	gluon_resnet152_v1c	94.160	5.840	98.640	1.360	60.21	224	0.875	bicubic
98	regnety_064	94.150	5.850	98.730	1.270	30.58	224	0.875	bicubic
99	efficientnet_b2_pruned	94.140	5.860	98.530	1.470	8.31	260	0.890	bicubic
100	dpn98	94.130	5.870	98.570	1.430	61.57	224	0.875	bicubic
101	regnetx_160	94.120	5.880	98.750	1.250	54.28	224	0.875	bicubic
102	resnext50_32x4d	94.100	5.900	98.350	1.650	25.03	224	0.875	bicubic
103	ese_vovnet39b	94.090	5.910	98.660	1.340	24.57	224	0.875	bicubic
104	gluon_resnet152_v1b	94.080	5.920	98.450	1.550	60.19	224	0.875	bicubic
105	dpn131	94.010	5.990	98.720	1.280	79.25	224	0.875	bicubic
106	hrnet_w64	94.010	5.990	98.610	1.390	128.06	224	0.875	bilinear
107	resnetblur50	93.960	6.040	98.590	1.410	25.56	224	0.875	bicubic
108	dla102x2	93.950	6.050	98.490	1.510	41.28	224	0.875	bilinear
109	hrnet_w48	93.920	6.080	98.610	1.390	77.47	224	0.875	bilinear
110	rexnet_130	93.900	6.100	98.400	1.600	7.56	224	0.875	bicubic
111	tf_efficientnet_cc_b1_8e	93.900	6.100	98.260	1.740	39.72	240	0.882	bicubic
112	regnetx_064	93.890	6.110	98.630	1.370	26.21	224	0.875	bicubic
113	regnetx_080	93.870	6.130	98.520	1.480	39.57	224	0.875	bicubic
114	regnety_040	93.860	6.140	98.650	1.350	20.65	224	0.875	bicubic
115	resnext101_32x8d	93.830	6.170	98.580	1.420	88.79	224	0.875	bilinear
116	gluon_resnext50_32x4d	93.810	6.190	98.410	1.590	25.03	224	0.875	bicubic
117	resnet50	93.810	6.190	98.390	1.610	25.56	224	0.875	bicubic
118	gluon_resnet50_v1d	93.770	6.230	98.390	1.610	25.58	224	0.875	bicubic
119	xception65	93.760	6.240	98.370	1.630	39.92	299	0.903	bicubic
120	gluon_resnet101_v1b	93.750	6.250	98.380	1.620	44.55	224	0.875	bicubic
121	res2net101_26w_4s	93.750	6.250	98.310	1.690	45.21	224	0.875	bilinear
122	wide_resnet101_2	93.720	6.280	98.540	1.460	126.89	224	0.875	bilinear
123	dpn68b	93.690	6.310	98.510	1.490	12.61	224	0.875	bicubic
124	tf_efficientnet_b1_ap	93.690	6.310	98.360	1.640	7.79	240	0.882	bicubic
125	gluon_resnet101_v1c	93.670	6.330	98.420	1.580	44.57	224	0.875	bicubic
126	tf_efficientnet_b0_ns	93.630	6.370	98.640	1.360	5.29	224	0.875	bicubic
127	gluon_resnet50_v1s	93.620	6.380	98.460	1.540	25.68	224	0.875	bicubic
128	regnetx_040	93.560	6.440	98.540	1.460	22.12	224	0.875	bicubic
129	hrnet_w44	93.550	6.450	98.700	1.300	67.06	224	0.875	bilinear
130	res2net50_26w_8s	93.540	6.460	98.260	1.740	48.40	224	0.875	bilinear
131	hrnet_w32	93.530	6.470	98.450	1.550	41.23	224	0.875	bilinear
132	dla102x	93.520	6.480	98.510	1.490	26.31	224	0.875	bilinear
133	tf_efficientnet_b1	93.500	6.500	98.360	1.640	7.79	240	0.882	bicubic
134	hrnet_w40	93.490	6.510	98.580	1.420	57.56	224	0.875	bilinear
135	tf_efficientnet_em	93.480	6.520	98.440	1.560	6.90	240	0.882	bicubic
136	gluon_inception_v3	93.460	6.540	98.570	1.430	23.83	299	0.875	bicubic
137	xception	93.460	6.540	98.530	1.470	22.86	299	0.897	bicubic
138	mixnet_l	93.450	6.550	98.220	1.780	7.33	224	0.875	bicubic
139	xception41	93.430	6.570	98.430	1.570	26.97	299	0.903	bicubic
140	res2net50_26w_6s	93.410	6.590	98.280	1.720	37.05	224	0.875	bilinear
141	regnety_032	93.390	6.610	98.640	1.360	19.44	224	0.875	bicubic
142	dla169	93.340	6.660	98.600	1.400	53.39	224	0.875	bilinear
143	resnest26d	93.330	6.670	98.630	1.370	17.07	224	0.875	bilinear
144	tf_inception_v3	93.320	6.680	98.030	1.970	23.83	299	0.875	bicubic
145	tf_mixnet_l	93.310	6.690	98.030	1.970	7.33	224	0.875	bicubic
146	resnet152	93.300	6.700	98.390	1.610	60.19	224	0.875	bilinear
147	selecsls60b	93.300	6.700	98.280	1.720	32.77	224	0.875	bicubic
148	efficientnet_b1	93.260	6.740	98.170	1.830	7.79	240	0.875	bicubic
149	hrnet_w30	93.200	6.800	98.410	1.590	37.71	224	0.875	bilinear
150	dla60_res2net	93.180	6.820	98.420	1.580	20.85	224	0.875	bilinear
151	dla60_res2next	93.180	6.820	98.410	1.590	17.03	224	0.875	bilinear
152	wide_resnet50_2	93.170	6.830	98.350	1.650	68.88	224	0.875	bilinear
153	efficientnet_es	93.140	6.860	98.420	1.580	5.44	224	0.875	bicubic
154	dla60x	93.120	6.880	98.510	1.490	17.35	224	0.875	bilinear
155	regnetx_032	93.120	6.880	98.390	1.610	15.30	224	0.875	bicubic
156	seresnext26t_32x4d	93.070	6.930	98.110	1.890	16.82	224	0.875	bicubic
157	dla102	93.060	6.940	98.540	1.460	33.27	224	0.875	bilinear
158	gluon_resnet50_v1c	93.030	6.970	98.390	1.610	25.58	224	0.875	bicubic
159	regnety_016	93.030	6.970	98.360	1.640	11.20	224	0.875	bicubic
160	rexnet_100	93.030	6.970	98.190	1.810	4.80	224	0.875	bicubic
161	selecsls60	93.030	6.970	98.300	1.700	30.67	224	0.875	bicubic
162	adv_inception_v3	92.880	7.120	98.140	1.860	23.83	299	0.875	bicubic
163	res2next50	92.840	7.160	98.180	1.820	24.67	224	0.875	bilinear
164	tf_efficientnet_cc_b0_8e	92.830	7.170	98.180	1.820	24.01	224	0.875	bicubic
165	seresnext26tn_32x4d	92.820	7.180	98.370	1.630	16.81	224	0.875	bicubic
166	resnet101	92.810	7.190	98.250	1.750	44.55	224	0.875	bilinear
167	efficientnet_b1_pruned	92.770	7.230	98.040	1.960	6.33	240	0.882	bicubic
168	densenet201	92.750	7.250	98.230	1.770	20.01	224	0.875	bicubic
169	res2net50_14w_8s	92.740	7.260	98.180	1.820	25.06	224	0.875	bilinear
170	tv_resnext50_32x4d	92.740	7.260	98.270	1.730	25.03	224	0.875	bilinear
171	inception_v3	92.720	7.280	97.970	2.030	23.83	299	0.875	bicubic
172	seresnext26d_32x4d	92.700	7.300	98.150	1.850	16.81	224	0.875	bicubic
173	efficientnet_b0	92.690	7.310	98.070	1.930	5.29	224	0.875	bicubic
174	tf_efficientnet_lite2	92.650	7.350	98.230	1.770	6.09	260	0.890	bicubic
175	tf_efficientnet_lite1	92.620	7.380	98.080	1.920	5.42	240	0.882	bicubic
176	tf_efficientnet_cc_b0_4e	92.590	7.410	98.080	1.920	13.31	224	0.875	bicubic
177	res2net50_48w_2s	92.550	7.450	98.080	1.920	25.29	224	0.875	bilinear
178	tf_efficientnet_es	92.550	7.450	98.280	1.720	5.44	224	0.875	bicubic
179	gluon_resnet50_v1b	92.540	7.460	98.170	1.830	25.56	224	0.875	bicubic
180	densenet161	92.500	7.500	98.290	1.710	28.68	224	0.875	bicubic
181	res2net50_26w_4s	92.500	7.500	98.060	1.940	25.70	224	0.875	bilinear
182	mixnet_m	92.430	7.570	97.870	2.130	5.01	224	0.875	bicubic
183	mobilenetv2_120d	92.400	7.600	98.050	1.950	5.83	224	0.875	bicubic
184	skresnet34	92.390	7.610	98.150	1.850	22.28	224	0.875	bicubic
185	tf_mixnet_m	92.330	7.670	97.890	2.110	5.01	224	0.875	bicubic
186	hrnet_w18	92.320	7.680	98.240	1.760	21.30	224	0.875	bilinear
187	ese_vovnet19b_dw	92.290	7.710	98.090	1.910	6.54	224	0.875	bicubic
188	selecsls42b	92.280	7.720	98.150	1.850	32.46	224	0.875	bicubic
189	tf_efficientnet_b0	92.250	7.750	98.000	2.000	5.29	224	0.875	bicubic
190	dla60	92.230	7.770	98.110	1.890	22.04	224	0.875	bilinear
191	tf_efficientnet_b0_ap	92.200	7.800	98.020	1.980	5.29	224	0.875	bicubic
192	regnetx_016	92.170	7.830	98.210	1.790	9.19	224	0.875	bicubic
193	resnet26d	92.070	7.930	97.960	2.040	16.01	224	0.875	bicubic
194	dpn68	92.010	7.990	98.050	1.950	12.61	224	0.875	bicubic
195	densenet169	91.930	8.070	98.100	1.900	14.15	224	0.875	bicubic
196	densenetblur121d	91.910	8.090	98.070	1.930	8.00	224	0.875	bicubic
197	tv_resnet50	91.880	8.120	98.040	1.960	25.56	224	0.875	bilinear
198	mobilenetv2_140	91.830	8.170	97.860	2.140	6.11	224	0.875	bicubic
199	mixnet_s	91.830	8.170	97.690	2.310	4.13	224	0.875	bicubic
200	regnety_008	91.750	8.250	98.180	1.820	6.26	224	0.875	bicubic
201	resnest14d	91.720	8.280	97.870	2.130	10.61	224	0.875	bilinear
202	densenet121	91.570	8.430	98.030	1.970	7.98	224	0.875	bicubic
203	tf_mixnet_s	91.510	8.490	97.620	2.380	4.13	224	0.875	bicubic
204	regnety_006	91.370	8.630	97.710	2.290	6.06	224	0.875	bicubic
205	mobilenetv3_large_100	91.320	8.680	97.710	2.290	5.48	224	0.875	bicubic
206	semnasnet_100	91.280	8.720	97.560	2.440	3.89	224	0.875	bicubic
207	tf_mobilenetv3_large_100	91.240	8.760	97.660	2.340	5.48	224	0.875	bilinear
208	mobilenetv3_rw	91.210	8.790	97.660	2.340	5.48	224	0.875	bicubic
209	hrnet_w18_small_v2	91.190	8.810	97.900	2.100	15.60	224	0.875	bilinear
210	efficientnet_lite0	91.140	8.860	97.630	2.370	4.65	224	0.875	bicubic
211	resnet34	91.130	8.870	97.620	2.380	21.80	224	0.875	bilinear
212	resnet26	91.110	8.890	97.740	2.260	16.00	224	0.875	bicubic
213	regnetx_008	91.050	8.950	97.710	2.290	7.26	224	0.875	bicubic
214	tf_efficientnet_lite0	91.040	8.960	97.590	2.410	4.65	224	0.875	bicubic
215	gluon_resnet34_v1b	90.960	9.040	97.630	2.370	21.80	224	0.875	bicubic
216	mobilenetv2_110d	90.950	9.050	97.550	2.450	4.52	224	0.875	bicubic
217	tv_densenet121	90.890	9.110	97.710	2.290	7.98	224	0.875	bicubic
218	dla34	90.760	9.240	97.660	2.340	15.74	224	0.875	bilinear
219	fbnetc_100	90.700	9.300	97.210	2.790	5.57	224	0.875	bilinear
220	swsl_resnet18	90.690	9.310	97.700	2.300	11.69	224	0.875	bilinear
221	mnasnet_100	90.510	9.490	97.470	2.530	4.38	224	0.875	bicubic
222	regnety_004	90.500	9.500	97.540	2.460	4.34	224	0.875	bicubic
223	regnetx_006	90.350	9.650	97.430	2.570	6.20	224	0.875	bicubic
224	spnasnet_100	90.350	9.650	97.190	2.810	4.42	224	0.875	bilinear
225	ssl_resnet18	90.220	9.780	97.550	2.450	11.69	224	0.875	bilinear
226	tv_resnet34	89.940	10.060	97.340	2.660	21.80	224	0.875	bilinear
227	tf_mobilenetv3_large_075	89.680	10.320	97.210	2.790	3.99	224	0.875	bilinear
228	skresnet18	89.660	10.340	97.230	2.770	11.96	224	0.875	bicubic
229	mobilenetv2_100	89.600	10.400	97.140	2.860	3.50	224	0.875	bicubic
230	hrnet_w18_small	89.050	10.950	97.110	2.890	13.19	224	0.875	bilinear
231	tf_mobilenetv3_large_minimal_100	88.970	11.030	96.860	3.140	3.92	224	0.875	bilinear
232	regnetx_004	88.900	11.100	97.120	2.880	5.16	224	0.875	bicubic
233	gluon_resnet18_v1b	88.400	11.600	96.680	3.320	11.69	224	0.875	bicubic
234	resnet18	87.390	12.610	96.290	3.710	11.69	224	0.875	bilinear
235	regnety_002	87.380	12.620	96.590	3.410	3.16	224	0.875	bicubic
236	dla60x_c	86.290	13.710	96.160	3.840	1.32	224	0.875	bilinear
237	regnetx_002	86.190	13.810	95.980	4.020	2.68	224	0.875	bicubic
238	tf_mobilenetv3_small_100	85.190	14.810	95.770	4.230	2.54	224	0.875	bilinear
239	dla46x_c	84.250	15.750	95.270	4.730	1.07	224	0.875	bilinear
240	dla46_c	83.650	16.350	94.920	5.080	1.30	224	0.875	bilinear
241	tf_mobilenetv3_small_075	83.520	16.480	94.790	5.210	2.04	224	0.875	bilinear
242	tf_mobilenetv3_small_minimal_100	81.380	18.620	93.670	6.330	2.04	224	0.875	bilinear