File size: 18,791 Bytes
8042bb9 44131b5 94e99e2 44131b5 8042bb9 728c45a 5c12649 25c3525 efdee5c 3089cd5 f998caa 0b2de8f 098b7bf 46bbddf 94e99e2 44131b5 8042bb9 |
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 |
---
license: other
tags:
- generated_from_keras_callback
model-index:
- name: sayakpaul/mit-b0-finetuned-sidewalks
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. -->
# sayakpaul/mit-b0-finetuned-sidewalks
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: 0.3545
- Validation Loss: 0.6001
- Validation Mean Iou: 0.3183
- Validation Mean Accuracy: 0.3876
- Validation Overall Accuracy: 0.8403
- Validation Per Category Iou: [0. 0.72446673 0.85906725 0.50307184 0.59402909 0.33935205
nan 0.36653095 0.49281565 0. 0.79716382 0.
0. nan 0. 0.54502138 0. 0.
0.67058988 0.01229056 0.33404111 0.35615386 0. nan
0. 0.3402916 0.13274148 0. 0.84876007 0.80426728
0.90568008 0.00767061 0.27079805 0.280819 0. ]
- Validation Per Category Accuracy: [0. 0.80252235 0.93835903 0.64483513 0.75981034 0.47343152
nan 0.51602703 0.68908551 0. 0.91483973 0.
0. nan 0. 0.72268005 0. 0.
0.90438608 0.01229056 0.42963846 0.43749539 0. nan
0. 0.40012652 0.15828843 0. 0.92988659 0.92495156
0.96015873 0.01578965 0.43451194 0.3352385 0. ]
- Epoch: 11
## 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 |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----:|
| 1.5309 | 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.
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 |
| 0.3827 | 0.6001 | 0.3153 | 0.3830 | 0.8399 | [0.00000000e+00 7.18523940e-01 8.57241195e-01 4.87400335e-01
5.64909227e-01 3.46474032e-01 nan 3.52839201e-01
4.72771964e-01 2.26308345e-04 7.96560839e-01 0.00000000e+00
0.00000000e+00 nan 0.00000000e+00 5.60432300e-01
0.00000000e+00 0.00000000e+00 6.83278176e-01 3.50039777e-02
4.23414561e-01 3.13947966e-01 0.00000000e+00 nan
0.00000000e+00 3.22699644e-01 1.08833077e-01 0.00000000e+00
8.47684853e-01 8.05165122e-01 9.05812333e-01 6.78139068e-03
2.30358320e-01 2.48185158e-01 0.00000000e+00] | [0.00000000e+00 8.00405608e-01 9.43426295e-01 6.48294679e-01
6.81857870e-01 5.18050809e-01 nan 4.57279092e-01
6.27335726e-01 2.26885990e-04 9.00155725e-01 0.00000000e+00
0.00000000e+00 nan 0.00000000e+00 7.09644391e-01
0.00000000e+00 0.00000000e+00 8.82359726e-01 3.50045460e-02
6.04178245e-01 3.33862219e-01 0.00000000e+00 nan
0.00000000e+00 4.02788008e-01 1.21141046e-01 0.00000000e+00
9.46216343e-01 9.21245814e-01 9.55916812e-01 1.34407725e-02
4.56834285e-01 2.96304685e-01 0.00000000e+00] | 10 |
| 0.3545 | 0.6001 | 0.3183 | 0.3876 | 0.8403 | [0. 0.72446673 0.85906725 0.50307184 0.59402909 0.33935205
nan 0.36653095 0.49281565 0. 0.79716382 0.
0. nan 0. 0.54502138 0. 0.
0.67058988 0.01229056 0.33404111 0.35615386 0. nan
0. 0.3402916 0.13274148 0. 0.84876007 0.80426728
0.90568008 0.00767061 0.27079805 0.280819 0. ] | [0. 0.80252235 0.93835903 0.64483513 0.75981034 0.47343152
nan 0.51602703 0.68908551 0. 0.91483973 0.
0. nan 0. 0.72268005 0. 0.
0.90438608 0.01229056 0.42963846 0.43749539 0. nan
0. 0.40012652 0.15828843 0. 0.92988659 0.92495156
0.96015873 0.01578965 0.43451194 0.3352385 0. ] | 11 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|