sam-vit-base


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sam-vit-base Model Set Plots

sam-vit-base Model Selected Details
id layer_type N M Q alpha D alpha-hat num_spikes warning
1 conv2d 768 3 256.0 3.042517 0.103096 2.004841 160
2 dense 256 256 1.0 4.172722 0.074769 3.531106 11
3 dense 256 128 2.0 2.066019 0.130797 2.361441 43
4 dense 256 128 2.0 1.724653 0.041400 5.510688 26 over-trained
5 dense 256 128 2.0 3.264305 0.107006 5.029072 13
6 dense 256 128 2.0 2.523440 0.071641 1.289890 20
7 dense 256 128 2.0 2.813345 0.044632 3.401366 27
8 dense 256 128 2.0 1.355054 0.087435 2.915138 74 over-trained
9 dense 256 128 2.0 1.503236 0.145696 1.779074 97 over-trained
10 dense 2048 256 8.0 3.642927 0.059507 7.389675 19
11 dense 2048 256 8.0 2.612072 0.076531 5.032424 62
12 dense 256 256 1.0 1.222138 0.108548 1.949231 183 over-trained
13 dense 256 256 1.0 1.967504 0.067876 2.564812 50 over-trained
14 dense 256 256 1.0 1.234004 0.103827 1.871403 174 over-trained
15 dense 256 256 1.0 1.579041 0.070959 2.081563 39 over-trained
16 dense 2304 768 3.0 2.694386 0.091250 6.738825 85
17 dense 3072 768 4.0 2.855879 0.079116 6.130265 222
18 dense 3072 768 4.0 2.961618 0.031491 6.508209 150
19 dense 256 128 2.0 1.430399 0.055038 3.070802 56 over-trained
20 dense 256 128 2.0 3.655754 0.095577 2.155930 11
21 dense 256 256 1.0 2.398184 0.056041 3.010687 26
22 dense 256 128 2.0 1.939390 0.086132 1.207315 41 over-trained
23 dense 256 128 2.0 1.327428 0.092171 1.565219 91 over-trained
24 dense 256 256 1.0 1.584787 0.072394 3.389656 87 over-trained
25 dense 256 256 1.0 1.390139 0.119729 2.979876 151 over-trained
26 dense 256 32 8.0 2.141660 0.129469 1.320213 18
27 dense 768 768 1.0 2.501964 0.070341 5.530125 186
28 dense 256 128 2.0 1.319163 0.108051 1.674044 94 over-trained
29 dense 3072 768 4.0 3.955896 0.055054 7.725048 112
30 dense 3072 768 4.0 4.497893 0.046740 8.923106 48
31 conv2d 768 256 3.0 4.262182 0.082305 4.685265 107
32 conv2d 256 64 4.0 3.056863 0.086868 -0.345854 74
33 dense 256 256 1.0 2.440162 0.097588 2.782695 46
34 dense 256 256 1.0 1.362763 0.112296 1.709529 126 over-trained
35 dense 256 256 1.0 2.475449 0.071744 3.106925 32
36 dense 256 256 1.0 1.353816 0.116006 1.746424 131 over-trained
37 dense 768 768 1.0 3.307309 0.043410 5.438157 171
38 dense 2048 256 8.0 3.429314 0.067047 6.577811 43
39 dense 256 128 2.0 3.353953 0.042499 2.388898 39
40 dense 2048 256 8.0 2.669861 0.058242 5.436719 57
41 dense 256 128 2.0 2.770029 0.063030 2.862245 33
42 dense 256 128 2.0 1.898343 0.070157 2.254959 38 over-trained
43 dense 256 128 2.0 2.781641 0.087913 2.102667 34
44 dense 256 128 2.0 2.395258 0.046361 2.545974 44
45 dense 256 128 2.0 2.753359 0.078251 2.872732 23
46 dense 256 128 2.0 1.745179 0.118462 1.809406 66 over-trained
47 dense 256 256 1.0 1.726891 0.087945 2.984996 58 over-trained
48 dense 256 256 1.0 1.373305 0.093560 2.197567 170 over-trained
49 dense 256 32 8.0 1.552540 0.200765 1.585004 29 over-trained
50 dense 256 128 2.0 1.890496 0.048627 4.571199 21 over-trained
51 dense 2304 768 3.0 3.039297 0.034902 5.777761 127
52 dense 3072 768 4.0 4.407190 0.044030 7.571262 123
53 dense 2304 768 3.0 2.111841 0.048896 3.715805 344
54 dense 768 768 1.0 3.899502 0.041803 5.281112 55
55 dense 256 32 8.0 2.093781 0.104044 2.252008 14
56 dense 3072 768 4.0 2.891137 0.099451 4.860478 240
57 dense 256 256 1.0 1.356247 0.111632 2.219721 172 over-trained
58 dense 256 256 1.0 1.656183 0.083685 3.099083 73 over-trained
59 conv2d 64 32 2.0 2.245145 0.074508 0.027709 43
60 conv2d 256 256 1.0 1.667752 0.068860 1.205938 746 over-trained
61 conv2d 16 4 4.0 2.319450 0.210961 -3.558777 16
62 dense 256 32 8.0 1.487950 0.153524 1.511076 29 over-trained
63 dense 3072 768 4.0 3.212490 0.107254 5.330726 186
64 dense 3072 768 4.0 5.872039 0.061142 9.809075 75
65 dense 768 768 1.0 2.815180 0.050920 3.592877 193
66 conv2d 256 16 16.0 1.929798 0.132698 0.478927 15 over-trained
67 dense 2304 768 3.0 2.573423 0.092708 4.292309 178
68 dense 256 256 1.0 1.933232 0.068580 3.772985 29 over-trained
69 dense 256 256 1.0 1.366467 0.108576 2.265627 171 over-trained
70 dense 3072 768 4.0 4.639404 0.040114 6.911650 94
71 dense 768 768 1.0 4.643345 0.092693 4.567533 61
72 dense 2304 768 3.0 3.299982 0.080771 5.244790 107
73 dense 3072 768 4.0 3.331240 0.073986 5.165219 141
74 dense 3072 768 4.0 2.942048 0.075857 4.184213 198
75 dense 3072 768 4.0 3.757170 0.038852 5.581366 119
76 dense 2304 768 3.0 3.596974 0.072089 5.830212 68
77 dense 768 768 1.0 5.921619 0.062036 5.494586 33
78 dense 2304 768 3.0 2.447838 0.098171 4.027110 193
79 dense 768 768 1.0 4.921379 0.108456 4.414602 64
80 dense 3072 768 4.0 3.540000 0.059393 5.092976 93
81 dense 3072 768 4.0 3.541727 0.054026 5.538222 89
82 dense 3072 768 4.0 3.024779 0.038768 4.752520 115
83 dense 3072 768 4.0 3.097021 0.047859 5.408159 98
84 dense 768 768 1.0 3.169765 0.087432 2.772288 92
85 dense 2304 768 3.0 2.700499 0.080597 3.955871 134
86 dense 768 768 1.0 2.917295 0.075815 2.575058 94
87 dense 3072 768 4.0 3.169889 0.038997 6.094252 76
88 dense 2304 768 3.0 2.260475 0.090730 3.399012 212
89 dense 3072 768 4.0 2.809131 0.045376 4.813764 95
90 dense 768 768 1.0 4.546830 0.072198 3.309470 33
91 dense 2304 768 3.0 2.809771 0.083369 3.809122 104
92 dense 3072 768 4.0 3.235344 0.040007 6.534350 94
93 dense 3072 768 4.0 3.039623 0.047173 5.251066 75
94 dense 3072 768 4.0 3.429041 0.043157 7.328037 77
95 dense 3072 768 4.0 3.267077 0.041119 5.530372 76
96 dense 2304 768 3.0 3.976794 0.081304 5.869196 47
97 dense 768 768 1.0 2.622276 0.111671 2.209723 126
98 dense 3072 768 4.0 3.373822 0.044302 7.946429 72
99 dense 768 768 1.0 2.807713 0.103551 2.776419 54
100 dense 3072 768 4.0 3.140962 0.056019 5.282527 102
101 dense 2304 768 3.0 1.861137 0.105970 5.365754 369 over-trained