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	tf_efficientnet_b6_ns	97.020	2.980	99.710	0.290	43.04	528	0.942	bicubic
6	ig_resnext101_32x48d	96.970	3.030	99.670	0.330	828.41	224	0.875	bilinear
7	tf_efficientnet_b5_ns	96.870	3.130	99.640	0.360	30.39	456	0.934	bicubic
8	ig_resnext101_32x32d	96.780	3.220	99.530	0.470	468.53	224	0.875	bilinear
9	tf_efficientnet_b4_ns	96.710	3.290	99.640	0.360	19.34	380	0.922	bicubic
10	tf_efficientnet_b8	96.700	3.300	99.530	0.470	87.41	672	0.954	bicubic
11	tf_efficientnet_b7	96.580	3.420	99.510	0.490	66.35	600	0.949	bicubic
12	tf_efficientnet_b8_ap	96.550	3.450	99.540	0.460	87.41	672	0.954	bicubic
13	ig_resnext101_32x16d	96.440	3.560	99.540	0.460	194.03	224	0.875	bilinear
14	tf_efficientnet_b6_ap	96.370	3.630	99.550	0.450	43.04	528	0.942	bicubic
15	tf_efficientnet_b7_ap	96.350	3.650	99.590	0.410	66.35	600	0.949	bicubic
16	tf_efficientnet_b6	96.290	3.710	99.520	0.480	43.04	528	0.942	bicubic
17	swsl_resnext101_32x16d	96.270	3.730	99.500	0.500	194.03	224	0.875	bilinear
18	swsl_resnext101_32x8d	96.240	3.760	99.590	0.410	88.79	224	0.875	bilinear
19	resnest269e	96.120	3.880	99.520	0.480	110.93	416	0.928	bicubic
20	tf_efficientnet_b3_ns	96.100	3.900	99.480	0.520	12.23	300	0.904	bicubic
21	tf_efficientnet_b5_ap	96.080	3.920	99.540	0.460	30.39	456	0.934	bicubic
22	resnest200e	96.070	3.930	99.480	0.520	70.20	320	0.909	bicubic
23	swsl_resnext101_32x4d	96.050	3.950	99.530	0.470	44.18	224	0.875	bilinear
24	tf_efficientnet_b5	95.980	4.020	99.450	0.550	30.39	456	0.934	bicubic
25	ig_resnext101_32x8d	95.930	4.070	99.380	0.620	88.79	224	0.875	bilinear
26	swsl_resnext50_32x4d	95.620	4.380	99.440	0.560	25.03	224	0.875	bilinear
27	tf_efficientnet_b4	95.590	4.410	99.330	0.670	19.34	380	0.922	bicubic
28	resnest101e	95.570	4.430	99.270	0.730	48.28	256	0.875	bilinear
29	tf_efficientnet_b2_ns	95.520	4.480	99.340	0.660	9.11	260	0.890	bicubic
30	tresnet_xl_448	95.510	4.490	99.340	0.660	78.44	448	0.875	bilinear
31	tf_efficientnet_b4_ap	95.490	4.510	99.390	0.610	19.34	380	0.922	bicubic
32	tresnet_l_448	95.410	4.590	99.300	0.700	55.99	448	0.875	bilinear
33	ssl_resnext101_32x16d	95.410	4.590	99.410	0.590	194.03	224	0.875	bilinear
34	pnasnet5large	95.360	4.640	99.130	0.870	86.06	331	0.911	bicubic
35	ssl_resnext101_32x8d	95.340	4.660	99.320	0.680	88.79	224	0.875	bilinear
36	swsl_resnet50	95.200	4.800	99.390	0.610	25.56	224	0.875	bilinear
37	ecaresnet101d	95.160	4.840	99.230	0.770	44.57	224	0.875	bicubic
38	ssl_resnext101_32x4d	95.160	4.840	99.300	0.700	44.18	224	0.875	bilinear
39	nasnetalarge	95.150	4.850	99.130	0.870	88.75	331	0.911	bicubic
40	efficientnet_b3a	95.120	4.880	99.120	0.880	12.23	320	1.000	bicubic
41	tresnet_xl	95.060	4.940	99.260	0.740	78.44	224	0.875	bilinear
42	tf_efficientnet_b3_ap	94.970	5.030	99.110	0.890	12.23	300	0.904	bicubic
43	efficientnet_b3	94.960	5.040	99.200	0.800	12.23	300	0.904	bicubic
44	tf_efficientnet_b3	94.910	5.090	99.110	0.890	12.23	300	0.904	bicubic
45	tresnet_l	94.900	5.100	99.030	0.970	55.99	224	0.875	bilinear
46	tf_efficientnet_lite4	94.870	5.130	99.090	0.910	13.01	380	0.920	bilinear
47	tf_efficientnet_b1_ns	94.860	5.140	99.250	0.750	7.79	240	0.882	bicubic
48	gluon_resnet152_v1s	94.720	5.280	99.060	0.940	60.32	224	0.875	bicubic
49	gluon_senet154	94.710	5.290	98.970	1.030	115.09	224	0.875	bicubic
50	resnest50d_4s2x40d	94.710	5.290	99.130	0.870	30.42	224	0.875	bicubic
51	ssl_resnext50_32x4d	94.700	5.300	99.240	0.760	25.03	224	0.875	bilinear
52	rexnet_200	94.660	5.340	99.090	0.910	16.37	224	0.875	bicubic
53	tresnet_m_448	94.660	5.340	99.150	0.850	31.39	448	0.875	bilinear
54	gluon_seresnext101_64x4d	94.650	5.350	98.980	1.020	88.23	224	0.875	bicubic
55	resnest50d	94.620	5.380	99.030	0.970	27.48	224	0.875	bilinear
56	efficientnet_b3_pruned	94.580	5.420	99.070	0.930	9.86	300	0.904	bicubic
57	regnety_320	94.520	5.480	99.170	0.830	145.05	224	0.875	bicubic
58	ecaresnet101d_pruned	94.450	5.550	99.100	0.900	24.88	224	0.875	bicubic
59	gluon_seresnext101_32x4d	94.450	5.550	99.090	0.910	48.96	224	0.875	bicubic
60	tf_efficientnet_el	94.450	5.550	99.080	0.920	10.59	300	0.904	bicubic
61	gluon_resnet152_v1d	94.440	5.560	99.010	0.990	60.21	224	0.875	bicubic
62	resnest50d_1s4x24d	94.390	5.610	99.070	0.930	25.68	224	0.875	bicubic
63	inception_v4	94.380	5.620	98.820	1.180	42.68	299	0.875	bicubic
64	efficientnet_b2a	94.370	5.630	99.050	0.950	9.11	288	1.000	bicubic
65	gluon_resnext101_64x4d	94.350	5.650	98.880	1.120	83.46	224	0.875	bicubic
66	efficientnet_b2	94.340	5.660	99.100	0.900	9.11	260	0.875	bicubic
67	inception_resnet_v2	94.340	5.660	98.800	1.200	55.84	299	0.897	bicubic
68	ssl_resnet50	94.310	5.690	99.150	0.850	25.56	224	0.875	bilinear
69	regnetx_120	94.270	5.730	99.190	0.810	46.11	224	0.875	bicubic
70	rexnet_150	94.270	5.730	99.080	0.920	9.73	224	0.875	bicubic
71	tf_efficientnet_b2_ap	94.270	5.730	98.950	1.050	9.11	260	0.890	bicubic
72	mixnet_xl	94.230	5.770	98.820	1.180	11.90	224	0.875	bicubic
73	regnetx_320	94.210	5.790	99.050	0.950	107.81	224	0.875	bicubic
74	tf_efficientnet_b2	94.210	5.790	99.030	0.970	9.11	260	0.890	bicubic
75	dpn92	94.190	5.810	98.930	1.070	37.67	224	0.875	bicubic
76	ecaresnet50d	94.190	5.810	99.020	0.980	25.58	224	0.875	bicubic
77	gluon_seresnext50_32x4d	94.170	5.830	98.910	1.090	27.56	224	0.875	bicubic
78	gluon_resnet101_v1d	94.170	5.830	98.940	1.060	44.57	224	0.875	bicubic
79	gluon_resnet101_v1s	94.170	5.830	99.010	0.990	44.67	224	0.875	bicubic
80	ecaresnetlight	94.140	5.860	98.950	1.050	30.16	224	0.875	bicubic
81	regnety_064	94.140	5.860	99.030	0.970	30.58	224	0.875	bicubic
82	ens_adv_inception_resnet_v2	94.130	5.870	98.790	1.210	55.84	299	0.897	bicubic
83	tf_efficientnet_lite3	94.130	5.870	98.960	1.040	8.20	300	0.904	bilinear
84	gluon_resnext101_32x4d	94.120	5.880	98.930	1.070	44.18	224	0.875	bicubic
85	regnety_160	94.120	5.880	99.020	0.980	83.59	224	0.875	bicubic
86	cspdarknet53	94.090	5.910	98.980	1.020	27.64	256	0.887	bilinear
87	seresnet50	94.080	5.920	98.970	1.030	28.09	224	0.875	bicubic
88	tresnet_m	94.070	5.930	98.830	1.170	31.39	224	0.875	bilinear
89	gluon_resnet152_v1b	94.030	5.970	98.740	1.260	60.19	224	0.875	bicubic
90	hrnet_w48	94.030	5.970	99.040	0.960	77.47	224	0.875	bilinear
91	regnety_120	94.010	5.990	99.030	0.970	51.82	224	0.875	bicubic
92	gluon_xception65	94.010	5.990	99.020	0.980	39.92	299	0.903	bicubic
93	dla102x2	94.000	6.000	99.030	0.970	41.28	224	0.875	bilinear
94	dpn107	93.960	6.040	98.840	1.160	86.92	224	0.875	bicubic
95	skresnext50_32x4d	93.950	6.050	98.820	1.180	27.48	224	0.875	bicubic
96	dpn98	93.940	6.060	98.920	1.080	61.57	224	0.875	bicubic
97	regnety_080	93.890	6.110	99.000	1.000	39.18	224	0.875	bicubic
98	xception71	93.890	6.110	98.950	1.050	42.34	299	0.903	bicubic
99	gluon_resnet152_v1c	93.880	6.120	98.800	1.200	60.21	224	0.875	bicubic
100	regnetx_160	93.880	6.120	99.090	0.910	54.28	224	0.875	bicubic
101	ese_vovnet39b	93.850	6.150	98.900	1.100	24.57	224	0.875	bicubic
102	resnext50_32x4d	93.840	6.160	98.830	1.170	25.03	224	0.875	bicubic
103	hrnet_w64	93.830	6.170	98.930	1.070	128.06	224	0.875	bilinear
104	ecaresnet50d_pruned	93.820	6.180	99.000	1.000	19.94	224	0.875	bicubic
105	resnext50d_32x4d	93.810	6.190	98.740	1.260	25.05	224	0.875	bicubic
106	efficientnet_b2_pruned	93.800	6.200	98.910	1.090	8.31	260	0.890	bicubic
107	dla169	93.800	6.200	98.840	1.160	53.39	224	0.875	bilinear
108	regnetx_080	93.790	6.210	98.910	1.090	39.57	224	0.875	bicubic
109	resnext101_32x8d	93.770	6.230	98.950	1.050	88.79	224	0.875	bilinear
110	cspresnext50	93.760	6.240	98.840	1.160	20.57	224	0.875	bilinear
111	dpn131	93.760	6.240	98.800	1.200	79.25	224	0.875	bicubic
112	gluon_resnet101_v1b	93.760	6.240	98.700	1.300	44.55	224	0.875	bicubic
113	xception65	93.760	6.240	98.860	1.140	39.92	299	0.903	bicubic
114	tf_efficientnet_b0_ns	93.740	6.260	98.980	1.020	5.29	224	0.875	bicubic
115	wide_resnet101_2	93.730	6.270	98.810	1.190	126.89	224	0.875	bilinear
116	hrnet_w40	93.710	6.290	98.800	1.200	57.56	224	0.875	bilinear
117	resnetblur50	93.710	6.290	98.810	1.190	25.56	224	0.875	bicubic
118	tf_efficientnet_b1	93.710	6.290	98.800	1.200	7.79	240	0.882	bicubic
119	gluon_resnet101_v1c	93.690	6.310	98.760	1.240	44.57	224	0.875	bicubic
120	regnetx_040	93.680	6.320	98.940	1.060	22.12	224	0.875	bicubic
121	rexnet_130	93.670	6.330	98.710	1.290	7.56	224	0.875	bicubic
122	gluon_resnext50_32x4d	93.650	6.350	98.690	1.310	25.03	224	0.875	bicubic
123	xception	93.640	6.360	98.770	1.230	22.86	299	0.897	bicubic
124	regnetx_064	93.630	6.370	99.050	0.950	26.21	224	0.875	bicubic
125	tf_efficientnet_b1_ap	93.630	6.370	98.800	1.200	7.79	240	0.882	bicubic
126	dpn68b	93.620	6.380	98.700	1.300	12.61	224	0.875	bicubic
127	hrnet_w44	93.620	6.380	98.960	1.040	67.06	224	0.875	bilinear
128	regnety_040	93.620	6.380	98.950	1.050	20.65	224	0.875	bicubic
129	gluon_resnet50_v1s	93.590	6.410	98.840	1.160	25.68	224	0.875	bicubic
130	res2net50_26w_6s	93.590	6.410	98.750	1.250	37.05	224	0.875	bilinear
131	dla60_res2next	93.570	6.430	98.800	1.200	17.03	224	0.875	bilinear
132	tf_efficientnet_cc_b1_8e	93.570	6.430	98.690	1.310	39.72	240	0.882	bicubic
133	gluon_inception_v3	93.540	6.460	98.830	1.170	23.83	299	0.875	bicubic
134	dla102x	93.530	6.470	98.850	1.150	26.31	224	0.875	bilinear
135	gluon_resnet50_v1d	93.530	6.470	98.710	1.290	25.58	224	0.875	bicubic
136	res2net101_26w_4s	93.520	6.480	98.600	1.400	45.21	224	0.875	bilinear
137	selecsls60b	93.500	6.500	98.840	1.160	32.77	224	0.875	bicubic
138	xception41	93.480	6.520	98.750	1.250	26.97	299	0.903	bicubic
139	tf_efficientnet_em	93.470	6.530	98.750	1.250	6.90	240	0.882	bicubic
140	regnety_032	93.460	6.540	98.950	1.050	19.44	224	0.875	bicubic
141	resnet50	93.460	6.540	98.600	1.400	25.56	224	0.875	bicubic
142	wide_resnet50_2	93.460	6.540	98.950	1.050	68.88	224	0.875	bilinear
143	res2net50_26w_8s	93.450	6.550	98.700	1.300	48.40	224	0.875	bilinear
144	dla60_res2net	93.380	6.620	98.860	1.140	20.85	224	0.875	bilinear
145	hrnet_w30	93.370	6.630	98.830	1.170	37.71	224	0.875	bilinear
146	dla102	93.260	6.740	98.780	1.220	33.27	224	0.875	bilinear
147	mixnet_l	93.260	6.740	98.700	1.300	7.33	224	0.875	bicubic
148	regnetx_032	93.250	6.750	98.730	1.270	15.30	224	0.875	bicubic
149	resnest26d	93.240	6.760	98.850	1.150	17.07	224	0.875	bilinear
150	resnet152	93.240	6.760	98.750	1.250	60.19	224	0.875	bilinear
151	tf_inception_v3	93.200	6.800	98.480	1.520	23.83	299	0.875	bicubic
152	dla60x	93.190	6.810	98.710	1.290	17.35	224	0.875	bilinear
153	res2net50_26w_4s	93.180	6.820	98.670	1.330	25.70	224	0.875	bilinear
154	res2next50	93.150	6.850	98.660	1.340	24.67	224	0.875	bilinear
155	efficientnet_b1	93.060	6.940	98.540	1.460	7.79	240	0.875	bicubic
156	tf_mixnet_l	93.040	6.960	98.540	1.460	7.33	224	0.875	bicubic
157	res2net50_14w_8s	93.030	6.970	98.700	1.300	25.06	224	0.875	bilinear
158	adv_inception_v3	93.010	6.990	98.490	1.510	23.83	299	0.875	bicubic
159	selecsls60	93.010	6.990	98.830	1.170	30.67	224	0.875	bicubic
160	regnety_016	93.000	7.000	98.680	1.320	11.20	224	0.875	bicubic
161	efficientnet_b1_pruned	92.980	7.020	98.530	1.470	6.33	240	0.882	bicubic
162	seresnext26t_32x4d	92.960	7.040	98.480	1.520	16.82	224	0.875	bicubic
163	hrnet_w32	92.950	7.050	98.840	1.160	41.23	224	0.875	bilinear
164	efficientnet_es	92.910	7.090	98.690	1.310	5.44	224	0.875	bicubic
165	gluon_resnet50_v1c	92.910	7.090	98.710	1.290	25.58	224	0.875	bicubic
166	inception_v3	92.900	7.100	98.330	1.670	23.83	299	0.875	bicubic
167	tv_resnext50_32x4d	92.900	7.100	98.720	1.280	25.03	224	0.875	bilinear
168	densenet161	92.900	7.100	98.810	1.190	28.68	224	0.875	bicubic
169	resnet101	92.880	7.120	98.660	1.340	44.55	224	0.875	bilinear
170	tf_efficientnet_cc_b0_8e	92.870	7.130	98.460	1.540	24.01	224	0.875	bicubic
171	rexnet_100	92.850	7.150	98.620	1.380	4.80	224	0.875	bicubic
172	tf_efficientnet_cc_b0_4e	92.840	7.160	98.440	1.560	13.31	224	0.875	bicubic
173	seresnext26tn_32x4d	92.820	7.180	98.560	1.440	16.81	224	0.875	bicubic
174	res2net50_48w_2s	92.790	7.210	98.470	1.530	25.29	224	0.875	bilinear
175	hrnet_w18	92.760	7.240	98.660	1.340	21.30	224	0.875	bilinear
176	densenet201	92.690	7.310	98.650	1.350	20.01	224	0.875	bicubic
177	dla60	92.670	7.330	98.630	1.370	22.04	224	0.875	bilinear
178	mobilenetv2_120d	92.610	7.390	98.510	1.490	5.83	224	0.875	bicubic
179	tf_efficientnet_b0_ap	92.610	7.390	98.370	1.630	5.29	224	0.875	bicubic
180	tf_efficientnet_lite2	92.590	7.410	98.550	1.450	6.09	260	0.890	bicubic
181	skresnet34	92.570	7.430	98.520	1.480	22.28	224	0.875	bicubic
182	gluon_resnet50_v1b	92.560	7.440	98.550	1.450	25.56	224	0.875	bicubic
183	tf_efficientnet_es	92.550	7.450	98.510	1.490	5.44	224	0.875	bicubic
184	regnetx_016	92.540	7.460	98.550	1.450	9.19	224	0.875	bicubic
185	efficientnet_b0	92.480	7.520	98.680	1.320	5.29	224	0.875	bicubic
186	selecsls42b	92.480	7.520	98.440	1.560	32.46	224	0.875	bicubic
187	seresnext26d_32x4d	92.440	7.560	98.540	1.460	16.81	224	0.875	bicubic
188	densenetblur121d	92.400	7.600	98.410	1.590	8.00	224	0.875	bicubic
189	tf_efficientnet_b0	92.400	7.600	98.470	1.530	5.29	224	0.875	bicubic
190	tf_efficientnet_lite1	92.310	7.690	98.490	1.510	5.42	240	0.882	bicubic
191	densenet169	92.300	7.700	98.590	1.410	14.15	224	0.875	bicubic
192	mixnet_m	92.270	7.730	98.350	1.650	5.01	224	0.875	bicubic
193	dpn68	92.240	7.760	98.610	1.390	12.61	224	0.875	bicubic
194	resnet26d	92.230	7.770	98.450	1.550	16.01	224	0.875	bicubic
195	tf_mixnet_m	92.200	7.800	98.420	1.580	5.01	224	0.875	bicubic
196	tv_resnet50	92.140	7.860	98.420	1.580	25.56	224	0.875	bilinear
197	mobilenetv2_140	92.030	7.970	98.250	1.750	6.11	224	0.875	bicubic
198	ese_vovnet19b_dw	92.010	7.990	98.510	1.490	6.54	224	0.875	bicubic
199	densenet121	91.940	8.060	98.280	1.720	7.98	224	0.875	bicubic
200	regnety_008	91.900	8.100	98.420	1.580	6.26	224	0.875	bicubic
201	mixnet_s	91.780	8.220	98.300	1.700	4.13	224	0.875	bicubic
202	tf_mixnet_s	91.680	8.320	98.240	1.760	4.13	224	0.875	bicubic
203	semnasnet_100	91.660	8.340	98.270	1.730	3.89	224	0.875	bicubic
204	regnety_006	91.570	8.430	98.430	1.570	6.06	224	0.875	bicubic
205	mobilenetv3_rw	91.550	8.450	98.270	1.730	5.48	224	0.875	bicubic
206	mobilenetv3_large_100	91.480	8.520	98.320	1.680	5.48	224	0.875	bicubic
207	resnet26	91.440	8.560	98.280	1.720	16.00	224	0.875	bicubic
208	tf_mobilenetv3_large_100	91.420	8.580	98.260	1.740	5.48	224	0.875	bilinear
209	tv_densenet121	91.400	8.600	98.250	1.750	7.98	224	0.875	bicubic
210	mobilenetv2_110d	91.350	8.650	98.190	1.810	4.52	224	0.875	bicubic
211	tf_efficientnet_lite0	91.300	8.700	98.090	1.910	4.65	224	0.875	bicubic
212	fbnetc_100	91.270	8.730	97.830	2.170	5.57	224	0.875	bilinear
213	efficientnet_lite0	91.260	8.740	98.250	1.750	4.65	224	0.875	bicubic
214	dla34	91.240	8.760	98.180	1.820	15.74	224	0.875	bilinear
215	mnasnet_100	91.200	8.800	98.050	1.950	4.38	224	0.875	bicubic
216	resnet34	91.200	8.800	98.240	1.760	21.80	224	0.875	bilinear
217	regnetx_008	91.180	8.820	98.380	1.620	7.26	224	0.875	bicubic
218	hrnet_w18_small_v2	91.170	8.830	98.340	1.660	15.60	224	0.875	bilinear
219	resnest14d	91.130	8.870	98.330	1.670	10.61	224	0.875	bilinear
220	gluon_resnet34_v1b	91.100	8.900	98.180	1.820	21.80	224	0.875	bicubic
221	swsl_resnet18	91.090	8.910	98.210	1.790	11.69	224	0.875	bilinear
222	regnety_004	90.780	9.220	98.080	1.920	4.34	224	0.875	bicubic
223	regnetx_006	90.760	9.240	98.100	1.900	6.20	224	0.875	bicubic
224	ssl_resnet18	90.700	9.300	98.020	1.980	11.69	224	0.875	bilinear
225	spnasnet_100	90.610	9.390	97.950	2.050	4.42	224	0.875	bilinear
226	tf_mobilenetv3_large_075	90.320	9.680	97.870	2.130	3.99	224	0.875	bilinear
227	tv_resnet34	90.290	9.710	97.980	2.020	21.80	224	0.875	bilinear
228	skresnet18	90.160	9.840	97.780	2.220	11.96	224	0.875	bicubic
229	hrnet_w18_small	89.880	10.120	97.900	2.100	13.19	224	0.875	bilinear
230	mobilenetv2_100	89.830	10.170	97.830	2.170	3.50	224	0.875	bicubic
231	regnetx_004	89.460	10.540	97.770	2.230	5.16	224	0.875	bicubic
232	tf_mobilenetv3_large_minimal_100	89.180	10.820	97.320	2.680	3.92	224	0.875	bilinear
233	gluon_resnet18_v1b	88.660	11.340	97.100	2.900	11.69	224	0.875	bicubic
234	regnety_002	88.200	11.800	97.430	2.570	3.16	224	0.875	bicubic
235	resnet18	88.150	11.850	97.120	2.880	11.69	224	0.875	bilinear
236	regnetx_002	87.380	12.620	96.990	3.010	2.68	224	0.875	bicubic
237	dla60x_c	87.110	12.890	97.140	2.860	1.32	224	0.875	bilinear
238	tf_mobilenetv3_small_100	85.960	14.040	96.400	3.600	2.54	224	0.875	bilinear
239	dla46x_c	85.480	14.520	96.440	3.560	1.07	224	0.875	bilinear
240	dla46_c	84.660	15.340	96.200	3.800	1.30	224	0.875	bilinear
241	tf_mobilenetv3_small_075	84.530	15.470	95.890	4.110	2.04	224	0.875	bilinear
242	tf_mobilenetv3_small_minimal_100	82.670	17.330	95.000	5.000	2.04	224	0.875	bilinear