metadata

license: other
tags:
  - generated_from_keras_callback
model-index:
  - name: sayakpaul/mit-b0-finetuned-sidewalks
    results: []

sayakpaul/mit-b0-finetuned-sidewalks

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: 0.3970
Validation Loss: 0.6094
Validation Mean Iou: 0.3154
Validation Mean Accuracy: 0.3845
Validation Overall Accuracy: 0.8365
Validation Per Category Iou: [0. 0.70298046 0.85101569 0.48546331 0.53875077 0.30506197 nan 0.36967252 0.46956636 0. 0.78487773 0.

           nan 0.         0.54510928 0.         0.

0.69555357 0.0474544 0.43944078 0.34243937 0. nan 0. 0.31970314 0.07436481 0. 0.84065947 0.79505994 0.90045732 0.0134226 0.27125836 0.30161838 0. ]

Validation Per Category Accuracy: [0. 0.78336023 0.94550091 0.59681159 0.65835184 0.43832978 nan 0.56126034 0.72465395 0. 0.92553299 0.

           nan 0.         0.67204825 0.         0.

0.84423958 0.04768476 0.62117922 0.41246864 0. nan 0. 0.38035291 0.07866878 0. 0.9420769 0.91782163 0.95911561 0.01988386 0.38917934 0.38662485 0. ]

Epoch: 9

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
1.5309	0.9380	0.1674	0.2153	0.7545	[0.00000000e+00 5.50637719e-01 7.61499932e-01 6.48396077e-04
3.56923200e-01 9.75833116e-02 0.00000000e+00 2.82588573e-02
5.28802378e-02 0.00000000e+00 5.93637894e-01 0.00000000e+00
0.00000000e+00 nan 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 5.53393589e-01 0.00000000e+00
1.50378244e-01 1.75413833e-02 0.00000000e+00 nan
0.00000000e+00 2.76097981e-02 0.00000000e+00 0.00000000e+00
7.86211179e-01 7.05492777e-01 8.34315629e-01 0.00000000e+00
0.00000000e+00 7.43899822e-03 0.00000000e+00]	[0.00000000e+00 7.08723416e-01 9.71019213e-01 6.48665345e-04
4.09438347e-01 1.09468057e-01 nan 3.05932982e-02
5.44133505e-02 0.00000000e+00 8.74063503e-01 0.00000000e+00
0.00000000e+00 nan 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 8.66648886e-01 0.00000000e+00
1.61194155e-01 1.77691783e-02 0.00000000e+00 nan
0.00000000e+00 2.81195635e-02 0.00000000e+00 0.00000000e+00
9.17500033e-01 8.30294930e-01 9.02491399e-01 0.00000000e+00
0.00000000e+00 7.77243386e-03 0.00000000e+00]	0
0.8850	0.7741	0.2215	0.2711	0.7807	[0. 0.56319416 0.79436978 0.22447649 0.37746306 0.2182132

    nan 0.17433499 0.35240193 0.         0.64391654 0.

           nan 0.         0.         0.         0.

0.61824159 0. 0.36925143 0.09610409 0. nan 0. 0.23049759 0. 0. 0.79662258 0.72121144 0.85940151 0. 0.00144295 0.04769959 0. ] | [0. 0.7334003 0.94650286 0.24751216 0.4478931 0.27208929 nan 0.22448353 0.45667969 0. 0.92555657 0. 0. nan 0. 0. 0. 0. 0.85871282 0. 0.43579563 0.09928831 0. nan 0. 0.25660062 0. 0. 0.93127726 0.85151776 0.9323404 0. 0.00144459 0.05442322 0. ] | 1 | | 0.7280 | 0.6948 | 0.2608 | 0.3178 | 0.8009 | [0. 0.5833859 0.81088427 0.37870695 0.42920979 0.26901769 nan 0.26967864 0.37309309 0. 0.73143999 0. 0. nan 0. 0.30875952 0. 0. 0.64460152 0. 0.36681761 0.20754432 0. nan 0. 0.27251923 0.01267829 0. 0.82057447 0.76705857 0.86538224 0. 0.13369659 0.09996937 0. ] | [0. 0.71016102 0.95278314 0.44786052 0.50520329 0.32109583 nan 0.37049571 0.63857903 0. 0.88589428 0. 0. nan 0. 0.34012586 0. 0. 0.88972948 0. 0.49551485 0.25354461 0. nan 0. 0.3309279 0.01267829 0. 0.93305477 0.86649237 0.94355496 0. 0.14937745 0.12218876 0. ] | 2 | | 0.6505 | 0.6601 | 0.2774 | 0.3400 | 0.8158 | [0. 0.67274147 0.83098512 0.46721789 0.48492165 0.28810209 nan 0.30676731 0.41116935 0. 0.73679658 0. 0. nan 0. 0.47421792 0. 0. 0.66232704 0. 0.40729478 0.27226345 0. nan 0. 0.22211219 0.00310618 0. 0.81170746 0.73786496 0.88368738 0. 0.07716099 0.12776685 0. ] | [0. 0.80048159 0.93309497 0.558633 0.56439564 0.38053253 nan 0.46424754 0.60183499 0. 0.92479351 0. 0. nan 0. 0.60493457 0. 0. 0.88399244 0. 0.55428873 0.34754253 0. nan 0. 0.25438648 0.00310618 0. 0.90931833 0.91190458 0.94609539 0. 0.08323588 0.15250888 0. ] | 3 | | 0.5810 | 0.6610 | 0.2893 | 0.3501 | 0.8173 | [0. 0.64601276 0.81866457 0.46535767 0.50543168 0.28373075 nan 0.33004533 0.40404147 0. 0.76223358 0. 0. nan 0. 0.52641725 0. 0. 0.65767205 0. 0.39175791 0.25442534 0. nan 0. 0.30521727 0.03951998 0. 0.82740493 0.75297779 0.88342457 0. 0.20580056 0.19670152 0. ] | [0. 0.73558164 0.95660246 0.55532275 0.61966264 0.32151473 nan 0.47707119 0.54010289 0. 0.91394179 0. 0. nan 0. 0.63758616 0. 0. 0.88501875 0. 0.56845175 0.29123233 0. nan 0. 0.38980592 0.03987322 0. 0.92751577 0.84695264 0.93293488 0. 0.32582376 0.23722217 0. ] | 4 | | 0.5288 | 0.6364 | 0.3033 | 0.3717 | 0.8260 | [0. 0.64487768 0.8377146 0.48707167 0.50884928 0.34176886 nan 0.34887555 0.45218372 0. 0.75715898 0. 0. nan 0. 0.5222127 0. 0. 0.69808156 0. 0.42644563 0.35474225 0. nan 0. 0.28867161 0.03742875 0. 0.83332433 0.7818028 0.88638015 0.00137015 0.2124537 0.28445749 0. ] | [0. 0.74189035 0.92893266 0.625763 0.62296571 0.54003942 nan 0.49591369 0.64509343 0. 0.92976992 0. 0. nan 0. 0.66209669 0. 0. 0.86461114 0. 0.54041026 0.4796133 0. nan 0. 0.33899822 0.03746434 0. 0.92987636 0.92582211 0.96099073 0.00151698 0.26040449 0.36377671 0. ] | 5 | | 0.4936 | 0.6299 | 0.2980 | 0.3599 | 0.8264 | [0. 0.66237142 0.83413529 0.50181208 0.52374508 0.34163702 nan 0.35933641 0.43258492 0. 0.76814068 0. 0. nan 0. 0.49822203 0. 0. 0.65539745 0. 0.3955574 0.32740018 0. nan 0. 0.31514128 0.04382747 0. 0.84497596 0.79425761 0.89798116 0.00201253 0.18109898 0.15596963 0. ] | [0. 0.72498823 0.94241029 0.63156495 0.72628664 0.44784858 nan 0.50327208 0.59434829 0. 0.91352866 0. 0. nan 0. 0.62105434 0. 0. 0.90132969 0. 0.4624487 0.42016669 0. nan 0. 0.37948533 0.04393027 0. 0.94520928 0.88447616 0.94729824 0.00247937 0.23226938 0.19132714 0. ] | 6 | | 0.4528 | 0.6340 | 0.2980 | 0.3664 | 0.8212 | [0. 0.60868123 0.83498291 0.18287132 0.46939835 0.31058578 nan 0.34162709 0.445366 0. 0.78966215 0. 0. nan 0. 0.53583212 0. 0. 0.71233622 0.03447214 0.47235409 0.37419598 0. nan 0. 0.32268508 0.05312127 0. 0.83874416 0.79217023 0.89975806 0.00192312 0.20492869 0.31166384 0. ] | [0. 0.70588336 0.94249106 0.19298309 0.73275474 0.44094168 nan 0.48341533 0.71859918 0. 0.90854187 0. 0. nan 0. 0.6752697 0. 0. 0.88312382 0.03451747 0.65575793 0.42127597 0. nan 0. 0.39094462 0.05356577 0. 0.95291962 0.86480438 0.95973926 0.00251199 0.29803261 0.40525743 0. ] | 7 | | 0.4458 | 0.6356 | 0.3066 | 0.3759 | 0.8311 | [0. 0.66186021 0.83364406 0.48364478 0.53627121 0.27582606 nan 0.35739504 0.41076225 0. 0.77850446 0. 0. nan 0. 0.50299945 0. 0. 0.70340595 0.01741996 0.40137463 0.32885851 0. nan 0. 0.32114603 0.05439069 0. 0.84655176 0.80081688 0.90314194 0.00292704 0.24599153 0.34524384 0. ] | [0. 0.74699777 0.95226432 0.65030589 0.70566846 0.35272168 nan 0.47019568 0.71372001 0. 0.91011138 0. 0. nan 0. 0.62189092 0. 0. 0.87945472 0.01742109 0.52016264 0.36958738 0. nan 0. 0.3993222 0.05508716 0. 0.928178 0.89821483 0.96116851 0.0033765 0.40579212 0.46633448 0. ] | 8 | | 0.3970 | 0.6094 | 0.3154 | 0.3845 | 0.8365 | [0. 0.70298046 0.85101569 0.48546331 0.53875077 0.30506197 nan 0.36967252 0.46956636 0. 0.78487773 0. 0. nan 0. 0.54510928 0. 0. 0.69555357 0.0474544 0.43944078 0.34243937 0. nan 0. 0.31970314 0.07436481 0. 0.84065947 0.79505994 0.90045732 0.0134226 0.27125836 0.30161838 0. ] | [0. 0.78336023 0.94550091 0.59681159 0.65835184 0.43832978 nan 0.56126034 0.72465395 0. 0.92553299 0. 0. nan 0. 0.67204825 0. 0. 0.84423958 0.04768476 0.62117922 0.41246864 0. nan 0. 0.38035291 0.07866878 0. 0.9420769 0.91782163 0.95911561 0.01988386 0.38917934 0.38662485 0. ] | 9 |

Framework versions

Transformers 4.24.0
TensorFlow 2.9.2
Datasets 2.6.1
Tokenizers 0.13.1