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---
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: 0.6505
- Validation Loss: 0.6601
- Validation Mean Iou: 0.2774
- Validation Mean Accuracy: 0.3400
- Validation Overall Accuracy: 0.8158
- Validation Per Category Iou: [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. ]
- Validation Per Category Accuracy: [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. ]
- Epoch: 3
## 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 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
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