File size: 21,180 Bytes
bd87ca1 340931e bd87ca1 988d8a1 bd87ca1 988d8a1 bd87ca1 988d8a1 6fee2d2 988d8a1 6fee2d2 988d8a1 a702506 988d8a1 a702506 988d8a1 cabe6fa 988d8a1 cabe6fa 988d8a1 1590036 988d8a1 1590036 988d8a1 5f9eaad ad3fd58 bcea238 0240d39 d9166b8 1d97c76 af2a09e 340931e bd87ca1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
---
license: other
tags:
- generated_from_keras_callback
model-index:
- name: nateraw/mit-b0-finetuned-sidewalks-v2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nateraw/mit-b0-finetuned-sidewalks-v2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3146
- Validation Loss: 0.4175
- Validation Mean Iou: 0.3339
- Validation Mean Accuracy: 0.3995
- Validation Overall Accuracy: 0.8745
- Validation Per Category Iou: [0. 0.81054591 0.88286867 0.68551149 0.86089895 0.4562385
nan 0.4522713 0.55496016 0.01456189 0.83576109 0.
0. 0. 0. 0.50709788 0. 0.
0.73464008 0.00175153 0.35021502 0.57263292 0. nan
0. 0.25185222 0.14419755 0. 0.85952374 0.70281003
0.9270307 0.17660456 0.04867831 0.18762581 0. ]
- Validation Per Category Accuracy: [0. 0.9092016 0.94168672 0.86545289 0.89611216 0.55273728
nan 0.61409823 0.76682349 0.01569689 0.92776282 0.
0. 0. 0. 0.59972229 0. 0.
0.86700656 0.00175747 0.54181633 0.67419762 0. nan
0. 0.3252672 0.14789466 0. 0.9316378 0.88743565
0.97060047 0.33277846 0.15319149 0.25967892 0. ]
- Epoch: 13
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Per Category Iou | Validation Per Category Accuracy | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----:|
| 1.4089 | 0.8220 | 0.1975 | 0.2427 | 0.7701 | [0. 0.58353931 0.7655921 0.04209491 0.53135026 0.11779776
nan 0.07709853 0.15950712 0. 0.69634813 0.
0. 0. 0. 0. 0. 0.
0.61456822 0. 0.24971248 0.27129675 0. nan
0. 0.07697324 0. 0. 0.78576516 0.61267064
0.84564576 0. 0. 0.08904216 0. ] | [0. 0.88026971 0.93475302 0.04216372 0.5484085 0.13285614
nan 0.08669707 0.19044773 0. 0.90089024 0.
0. 0. 0. 0. 0. 0.
0.76783975 0. 0.42102101 0.28659817 0. nan
0. 0.08671771 0. 0. 0.89590301 0.74932576
0.9434814 0. 0. 0.14245566 0. ] | 0 |
| 0.8462 | 0.6135 | 0.2551 | 0.2960 | 0.8200 | [0. 0.66967645 0.80571406 0.56416239 0.66692248 0.24744912
nan 0.23994505 0.28962463 0. 0.76504783 0.
0. 0. 0. 0.14111353 0. 0.
0.6924468 0. 0.27988701 0.41876094 0. nan
0. 0.14755829 0. 0. 0.81614463 0.68429711
0.87710938 0. 0. 0.11234171 0. ] | [0. 0.83805933 0.94928385 0.59586511 0.72913519 0.30595504
nan 0.3128234 0.34805831 0. 0.87847495 0.
0. 0. 0. 0.14205167 0. 0.
0.87543619 0. 0.36001144 0.49498574 0. nan
0. 0.18179115 0. 0. 0.92867923 0.7496178
0.92220166 0. 0. 0.15398549 0. ] | 1 |
| 0.7134 | 0.5660 | 0.2780 | 0.3320 | 0.8286 | [0. 0.64791461 0.83800512 0.67301044 0.68120631 0.27361472
nan 0.26715802 0.43596999 0. 0.78649287 0.
0. 0. 0. 0.41256964 0. 0.
0.71114766 0. 0.31646321 0.44682442 0. nan
0. 0.17132551 0. 0. 0.81845697 0.67536699
0.88940936 0. 0. 0.1304862 0. ] | [0. 0.85958877 0.92084269 0.82341633 0.74725972 0.33495972
nan 0.40755277 0.56591531 0. 0.90641721 0.
0. 0. 0. 0.48144408 0. 0.
0.88294811 0. 0.46962078 0.47517397 0. nan
0. 0.20631607 0. 0. 0.90956851 0.85856042
0.94107052 0. 0. 0.16669713 0. ] | 2 |
| 0.6320 | 0.5173 | 0.2894 | 0.3454 | 0.8435 | [0. 0.70789146 0.84902296 0.65266358 0.76099965 0.32934391
nan 0.29576422 0.43988204 0. 0.79276447 0.
0. 0. 0. 0.42668367 0. 0.
0.71717911 0. 0.32151249 0.50084444 0. nan
0. 0.18711455 0. 0. 0.82903803 0.68990498
0.8990059 0. 0.00213015 0.14819771 0. ] | [0. 0.84048763 0.93514369 0.68355212 0.88302113 0.458816
nan 0.38623272 0.69456442 0. 0.92379471 0.
0. 0. 0. 0.50677438 0. 0.
0.90362965 0. 0.4662386 0.57368294 0. nan
0. 0.23281768 0. 0. 0.9001526 0.86786434
0.95195314 0. 0.00333751 0.18532191 0. ] | 3 |
| 0.5609 | 0.5099 | 0.2920 | 0.3599 | 0.8385 | [0. 0.70817583 0.84131144 0.66573523 0.81449696 0.38891117
nan 0.28124784 0.42659255 0. 0.80855146 0.
0. 0. 0. 0.46011866 0. 0.
0.65458792 0. 0.28411565 0.46758138 0. nan
0. 0.21849067 0. 0. 0.83829062 0.71207623
0.89929169 0. 0.02846127 0.13782635 0. ] | [0. 0.88632871 0.91269832 0.79044294 0.88368528 0.57405218
nan 0.35035973 0.77610775 0. 0.8889696 0.
0. 0. 0. 0.6020786 0. 0.
0.74586521 0. 0.61602403 0.54519561 0. nan
0. 0.28447396 0. 0. 0.94520232 0.85544414
0.95994042 0. 0.04680851 0.21407134 0. ] | 4 |
| 0.5256 | 0.4741 | 0.3045 | 0.3598 | 0.8558 | [0.00000000e+00 7.50159008e-01 8.53654462e-01 6.44928131e-01
7.90455244e-01 4.33599913e-01 nan 3.33472954e-01
4.74502513e-01 0.00000000e+00 8.01366017e-01 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 4.67653814e-01
0.00000000e+00 0.00000000e+00 7.27412479e-01 0.00000000e+00
4.18946113e-01 5.04714837e-01 0.00000000e+00 nan
0.00000000e+00 2.00373855e-01 0.00000000e+00 0.00000000e+00
8.50200795e-01 7.41636173e-01 9.08320534e-01 2.77259907e-04
0.00000000e+00 1.45430716e-01 0.00000000e+00] | [0.00000000e+00 8.86487233e-01 9.05201886e-01 7.23139265e-01
8.91929263e-01 7.26675641e-01 nan 4.36386295e-01
6.64378543e-01 0.00000000e+00 8.89056843e-01 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 5.65450644e-01
0.00000000e+00 0.00000000e+00 9.27446136e-01 0.00000000e+00
5.36031025e-01 5.84198054e-01 0.00000000e+00 nan
0.00000000e+00 2.42514534e-01 0.00000000e+00 0.00000000e+00
9.31954754e-01 8.26849708e-01 9.59880377e-01 2.79039335e-04
0.00000000e+00 1.77106051e-01 0.00000000e+00] | 5 |
| 0.4761 | 0.4922 | 0.3036 | 0.3754 | 0.8517 | [0.00000000e+00 7.18490241e-01 8.54701589e-01 5.90903088e-01
8.21902743e-01 4.76229883e-01 nan 3.32447673e-01
4.80642540e-01 0.00000000e+00 8.02904449e-01 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 4.73285636e-01
0.00000000e+00 0.00000000e+00 7.16608930e-01 0.00000000e+00
3.16598081e-01 5.12540924e-01 0.00000000e+00 nan
0.00000000e+00 2.27702968e-01 0.00000000e+00 0.00000000e+00
8.51831675e-01 7.39827330e-01 9.07152231e-01 5.59070700e-04
3.70370370e-02 1.56538301e-01 0.00000000e+00] | [0.00000000e+00 9.20834531e-01 8.92075255e-01 7.48664032e-01
9.03709011e-01 7.40703529e-01 nan 4.40828188e-01
7.92719139e-01 0.00000000e+00 9.21593374e-01 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 6.90292855e-01
0.00000000e+00 0.00000000e+00 8.42229041e-01 0.00000000e+00
4.75170857e-01 6.72591473e-01 0.00000000e+00 nan
0.00000000e+00 2.94713089e-01 0.00000000e+00 0.00000000e+00
9.26034809e-01 8.39522012e-01 9.66679296e-01 6.06188900e-04
1.12807676e-01 2.07280968e-01 0.00000000e+00] | 6 |
| 0.4495 | 0.4797 | 0.3035 | 0.3702 | 0.8468 | [0.00000000e+00 7.52163526e-01 8.46563375e-01 7.16396797e-01
7.38850637e-01 3.93073019e-01 nan 3.31795957e-01
4.92991567e-01 0.00000000e+00 8.11302090e-01 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 5.16059849e-01
0.00000000e+00 0.00000000e+00 6.56058294e-01 1.25948501e-02
2.66942435e-01 5.34406894e-01 0.00000000e+00 nan
0.00000000e+00 2.27750085e-01 4.86381323e-04 0.00000000e+00
8.48618960e-01 7.25828093e-01 9.17747637e-01 8.28380212e-03
6.74590297e-02 1.51281596e-01 0.00000000e+00] | [0.00000000e+00 8.75360044e-01 9.43650850e-01 8.78658645e-01
7.76578096e-01 4.85757596e-01 nan 4.30901582e-01
7.54126335e-01 0.00000000e+00 9.30112537e-01 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 6.42914247e-01
0.00000000e+00 0.00000000e+00 7.57605356e-01 1.27102686e-02
6.50888458e-01 6.94757080e-01 0.00000000e+00 nan
0.00000000e+00 2.91727649e-01 4.86381323e-04 0.00000000e+00
9.42251577e-01 8.60753175e-01 9.56778008e-01 8.51551074e-03
1.38756779e-01 1.83583708e-01 0.00000000e+00] | 7 |
| 0.4193 | 0.4487 | 0.3073 | 0.3633 | 0.8594 | [0. 0.77081114 0.86089485 0.64464211 0.82962632 0.36186873
nan 0.39092332 0.5399988 0. 0.81734925 0.
0. 0. 0. 0.50271555 0. 0.
0.70239658 0. 0.30875695 0.52195319 0. nan
0. 0.20124517 0.00696273 0. 0.84526591 0.72563399
0.91703372 0. 0.03526147 0.15693635 0. ] | [0. 0.8654775 0.95711297 0.70665759 0.93130714 0.42436958
nan 0.52892143 0.69243377 0. 0.91682626 0.
0. 0. 0. 0.62315913 0. 0.
0.86251114 0. 0.5607807 0.70416055 0. nan
0. 0.24483525 0.00698305 0. 0.921099 0.81848055
0.96789871 0. 0.06891948 0.18778302 0. ] | 8 |
| 0.3883 | 0.4824 | 0.3086 | 0.3690 | 0.8527 | [0. 0.76454291 0.86544951 0.70501066 0.77912256 0.39088976
nan 0.40275725 0.53334923 0. 0.82777802 0.
0. 0. 0. 0.49916177 0. 0.
0.68780083 0.01500768 0.31589145 0.53805504 0. nan
0. 0.22450413 0.03544121 0. 0.82663975 0.60689445
0.91513911 0.12702194 0.0163284 0.10604071 0. ] | [0. 0.86846682 0.93345513 0.77258597 0.90365389 0.54440067
nan 0.51997559 0.73323435 0. 0.92499729 0.
0. 0. 0. 0.62015064 0. 0.
0.8190305 0.01503264 0.61258781 0.62514291 0. nan
0. 0.28141855 0.03574903 0. 0.95838638 0.66828866
0.96505306 0.19804095 0.04463913 0.1315269 0. ] | 9 |
| 0.3736 | 0.4515 | 0.3180 | 0.3859 | 0.8600 | [0. 0.77296038 0.8679117 0.60122746 0.84573808 0.42877201
nan 0.40372521 0.5356554 0. 0.82057963 0.
0. 0. 0. 0.48309209 0. 0.
0.70156487 0.07165346 0.31172072 0.45383525 0. nan
0. 0.26337213 0.07457255 0. 0.85227381 0.7079085
0.92271657 0.20363628 0.03853875 0.13249146 0. ] | [0. 0.90081404 0.93156248 0.71723323 0.91251575 0.57187527
nan 0.53665381 0.74547838 0. 0.93718616 0.
0. 0. 0. 0.6410839 0. 0.
0.80529967 0.07249561 0.6074764 0.5775282 0. nan
0. 0.34898163 0.07545859 0. 0.95221746 0.80297775
0.96768443 0.26155608 0.19382562 0.17354842 0. ] | 10 |
| 0.3487 | 0.4486 | 0.3181 | 0.3898 | 0.8637 | [0. 0.79416982 0.87767891 0.70942695 0.81634288 0.46749785
nan 0.42873013 0.48671464 0. 0.82752704 0.
0. 0. 0. 0.50844774 0. 0.
0.68070149 0.03976498 0.29304387 0.46322705 0. nan
0. 0.24856882 0.12795031 0. 0.84646906 0.71781094
0.92550642 0.04810685 0.04610752 0.14423047 0. ] | [0. 0.86951324 0.95247608 0.82408892 0.90393017 0.59760857
nan 0.5760741 0.83602638 0. 0.93420702 0.
0. 0. 0. 0.63502483 0. 0.
0.76902695 0.04024918 0.57179186 0.75842139 0. nan
0. 0.30837498 0.13239994 0. 0.95283514 0.78607095
0.96594744 0.05354669 0.18906967 0.2060098 0. ] | 11 |
| 0.3460 | 0.4342 | 0.3234 | 0.3852 | 0.8669 | [0. 0.76828673 0.86958873 0.66044471 0.84588115 0.46323947
nan 0.41208499 0.54202812 0. 0.82543751 0.
0. 0. 0. 0.50071248 0. 0.
0.72333932 0.0173886 0.36535728 0.5284402 0. nan
0. 0.24239821 0.13456635 0. 0.86084123 0.73217705
0.92386442 0.09545854 0.04193608 0.11945951 0. ] | [0. 0.92666259 0.91906703 0.74134089 0.92518489 0.60022437
nan 0.56316038 0.77045814 0. 0.93600314 0.
0. 0. 0. 0.61358664 0. 0.
0.87835072 0.01757469 0.57608316 0.64108174 0. nan
0. 0.30432247 0.13750695 0. 0.93332326 0.85806371
0.96442783 0.10753599 0.15152274 0.14552189 0. ] | 12 |
| 0.3146 | 0.4175 | 0.3339 | 0.3995 | 0.8745 | [0. 0.81054591 0.88286867 0.68551149 0.86089895 0.4562385
nan 0.4522713 0.55496016 0.01456189 0.83576109 0.
0. 0. 0. 0.50709788 0. 0.
0.73464008 0.00175153 0.35021502 0.57263292 0. nan
0. 0.25185222 0.14419755 0. 0.85952374 0.70281003
0.9270307 0.17660456 0.04867831 0.18762581 0. ] | [0. 0.9092016 0.94168672 0.86545289 0.89611216 0.55273728
nan 0.61409823 0.76682349 0.01569689 0.92776282 0.
0. 0. 0. 0.59972229 0. 0.
0.86700656 0.00175747 0.54181633 0.67419762 0. nan
0. 0.3252672 0.14789466 0. 0.9316378 0.88743565
0.97060047 0.33277846 0.15319149 0.25967892 0. ] | 13 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.0
- Tokenizers 0.13.2
|