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--- |
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license: other |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: sayakpaul/mit-b0-finetuned-sidewalks |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# sayakpaul/mit-b0-finetuned-sidewalks |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.4936 |
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- Validation Loss: 0.6299 |
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- Validation Mean Iou: 0.2980 |
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- Validation Mean Accuracy: 0.3599 |
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- Validation Overall Accuracy: 0.8264 |
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- Validation Per Category Iou: [0. 0.66237142 0.83413529 0.50181208 0.52374508 0.34163702 |
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nan 0.35933641 0.43258492 0. 0.76814068 0. |
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0. nan 0. 0.49822203 0. 0. |
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0.65539745 0. 0.3955574 0.32740018 0. nan |
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0. 0.31514128 0.04382747 0. 0.84497596 0.79425761 |
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0.89798116 0.00201253 0.18109898 0.15596963 0. ] |
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- Validation Per Category Accuracy: [0. 0.72498823 0.94241029 0.63156495 0.72628664 0.44784858 |
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nan 0.50327208 0.59434829 0. 0.91352866 0. |
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0. nan 0. 0.62105434 0. 0. |
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0.90132969 0. 0.4624487 0.42016669 0. nan |
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0. 0.37948533 0.04393027 0. 0.94520928 0.88447616 |
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0.94729824 0.00247937 0.23226938 0.19132714 0. ] |
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- Epoch: 6 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Per Category Iou | Validation Per Category Accuracy | Epoch | |
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|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----:| |
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| 1.5309 | 0.9380 | 0.1674 | 0.2153 | 0.7545 | [0.00000000e+00 5.50637719e-01 7.61499932e-01 6.48396077e-04 |
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3.56923200e-01 9.75833116e-02 0.00000000e+00 2.82588573e-02 |
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5.28802378e-02 0.00000000e+00 5.93637894e-01 0.00000000e+00 |
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0.00000000e+00 nan 0.00000000e+00 0.00000000e+00 |
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0.00000000e+00 0.00000000e+00 5.53393589e-01 0.00000000e+00 |
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1.50378244e-01 1.75413833e-02 0.00000000e+00 nan |
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0.00000000e+00 2.76097981e-02 0.00000000e+00 0.00000000e+00 |
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7.86211179e-01 7.05492777e-01 8.34315629e-01 0.00000000e+00 |
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0.00000000e+00 7.43899822e-03 0.00000000e+00] | [0.00000000e+00 7.08723416e-01 9.71019213e-01 6.48665345e-04 |
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4.09438347e-01 1.09468057e-01 nan 3.05932982e-02 |
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5.44133505e-02 0.00000000e+00 8.74063503e-01 0.00000000e+00 |
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0.00000000e+00 nan 0.00000000e+00 0.00000000e+00 |
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0.00000000e+00 0.00000000e+00 8.66648886e-01 0.00000000e+00 |
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1.61194155e-01 1.77691783e-02 0.00000000e+00 nan |
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0.00000000e+00 2.81195635e-02 0.00000000e+00 0.00000000e+00 |
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9.17500033e-01 8.30294930e-01 9.02491399e-01 0.00000000e+00 |
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0.00000000e+00 7.77243386e-03 0.00000000e+00] | 0 | |
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| 0.8850 | 0.7741 | 0.2215 | 0.2711 | 0.7807 | [0. 0.56319416 0.79436978 0.22447649 0.37746306 0.2182132 |
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nan 0.17433499 0.35240193 0. 0.64391654 0. |
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0. nan 0. 0. 0. 0. |
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0.61824159 0. 0.36925143 0.09610409 0. nan |
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0. 0.23049759 0. 0. 0.79662258 0.72121144 |
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0.85940151 0. 0.00144295 0.04769959 0. ] | [0. 0.7334003 0.94650286 0.24751216 0.4478931 0.27208929 |
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nan 0.22448353 0.45667969 0. 0.92555657 0. |
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0. nan 0. 0. 0. 0. |
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0.85871282 0. 0.43579563 0.09928831 0. nan |
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0. 0.25660062 0. 0. 0.93127726 0.85151776 |
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0.9323404 0. 0.00144459 0.05442322 0. ] | 1 | |
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| 0.7280 | 0.6948 | 0.2608 | 0.3178 | 0.8009 | [0. 0.5833859 0.81088427 0.37870695 0.42920979 0.26901769 |
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nan 0.26967864 0.37309309 0. 0.73143999 0. |
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0. nan 0. 0.30875952 0. 0. |
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0.64460152 0. 0.36681761 0.20754432 0. nan |
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0. 0.27251923 0.01267829 0. 0.82057447 0.76705857 |
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0.86538224 0. 0.13369659 0.09996937 0. ] | [0. 0.71016102 0.95278314 0.44786052 0.50520329 0.32109583 |
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nan 0.37049571 0.63857903 0. 0.88589428 0. |
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0. nan 0. 0.34012586 0. 0. |
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0.88972948 0. 0.49551485 0.25354461 0. nan |
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0. 0.3309279 0.01267829 0. 0.93305477 0.86649237 |
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0.94355496 0. 0.14937745 0.12218876 0. ] | 2 | |
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| 0.6505 | 0.6601 | 0.2774 | 0.3400 | 0.8158 | [0. 0.67274147 0.83098512 0.46721789 0.48492165 0.28810209 |
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nan 0.30676731 0.41116935 0. 0.73679658 0. |
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0. nan 0. 0.47421792 0. 0. |
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0.66232704 0. 0.40729478 0.27226345 0. nan |
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0. 0.22211219 0.00310618 0. 0.81170746 0.73786496 |
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0.88368738 0. 0.07716099 0.12776685 0. ] | [0. 0.80048159 0.93309497 0.558633 0.56439564 0.38053253 |
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nan 0.46424754 0.60183499 0. 0.92479351 0. |
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0. nan 0. 0.60493457 0. 0. |
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0.88399244 0. 0.55428873 0.34754253 0. nan |
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0. 0.25438648 0.00310618 0. 0.90931833 0.91190458 |
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0.94609539 0. 0.08323588 0.15250888 0. ] | 3 | |
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| 0.5810 | 0.6610 | 0.2893 | 0.3501 | 0.8173 | [0. 0.64601276 0.81866457 0.46535767 0.50543168 0.28373075 |
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nan 0.33004533 0.40404147 0. 0.76223358 0. |
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0. nan 0. 0.52641725 0. 0. |
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0.65767205 0. 0.39175791 0.25442534 0. nan |
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0. 0.30521727 0.03951998 0. 0.82740493 0.75297779 |
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0.88342457 0. 0.20580056 0.19670152 0. ] | [0. 0.73558164 0.95660246 0.55532275 0.61966264 0.32151473 |
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nan 0.47707119 0.54010289 0. 0.91394179 0. |
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0. nan 0. 0.63758616 0. 0. |
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0.88501875 0. 0.56845175 0.29123233 0. nan |
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0. 0.38980592 0.03987322 0. 0.92751577 0.84695264 |
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0.93293488 0. 0.32582376 0.23722217 0. ] | 4 | |
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| 0.5288 | 0.6364 | 0.3033 | 0.3717 | 0.8260 | [0. 0.64487768 0.8377146 0.48707167 0.50884928 0.34176886 |
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nan 0.34887555 0.45218372 0. 0.75715898 0. |
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0. nan 0. 0.5222127 0. 0. |
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0.69808156 0. 0.42644563 0.35474225 0. nan |
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0. 0.28867161 0.03742875 0. 0.83332433 0.7818028 |
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0.88638015 0.00137015 0.2124537 0.28445749 0. ] | [0. 0.74189035 0.92893266 0.625763 0.62296571 0.54003942 |
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nan 0.49591369 0.64509343 0. 0.92976992 0. |
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0. nan 0. 0.66209669 0. 0. |
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0.86461114 0. 0.54041026 0.4796133 0. nan |
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0. 0.33899822 0.03746434 0. 0.92987636 0.92582211 |
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0.96099073 0.00151698 0.26040449 0.36377671 0. ] | 5 | |
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| 0.4936 | 0.6299 | 0.2980 | 0.3599 | 0.8264 | [0. 0.66237142 0.83413529 0.50181208 0.52374508 0.34163702 |
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nan 0.35933641 0.43258492 0. 0.76814068 0. |
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0. nan 0. 0.49822203 0. 0. |
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0.65539745 0. 0.3955574 0.32740018 0. nan |
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0. 0.31514128 0.04382747 0. 0.84497596 0.79425761 |
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0.89798116 0.00201253 0.18109898 0.15596963 0. ] | [0. 0.72498823 0.94241029 0.63156495 0.72628664 0.44784858 |
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nan 0.50327208 0.59434829 0. 0.91352866 0. |
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0. nan 0. 0.62105434 0. 0. |
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0.90132969 0. 0.4624487 0.42016669 0. nan |
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0. 0.37948533 0.04393027 0. 0.94520928 0.88447616 |
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0.94729824 0.00247937 0.23226938 0.19132714 0. ] | 6 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- TensorFlow 2.9.2 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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