license: other
tags:
- generated_from_keras_callback
model-index:
- name: nateraw/mit-b0-finetuned-sidewalks-v2
results: []
nateraw/mit-b0-finetuned-sidewalks-v2
This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.3736
- Validation Loss: 0.4515
- Validation Mean Iou: 0.3180
- Validation Mean Accuracy: 0.3859
- Validation Overall Accuracy: 0.8600
- Validation Per Category Iou: [0. 0.77296038 0.8679117 0.60122746 0.84573808 0.42877201 nan 0.40372521 0.5356554 0. 0.82057963 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. ]
- Validation Per Category Accuracy: [0. 0.90081404 0.93156248 0.71723323 0.91251575 0.57187527 nan 0.53665381 0.74547838 0. 0.93718616 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. ]
- Epoch: 10
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.
0.61456822 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 |
Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.0
- Tokenizers 0.13.2