
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# testing_100_epoches

This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1400
- Mean Iou: 0.0066
- Mean Accuracy: 0.0133
- Overall Accuracy: 0.0133
- Accuracy Bkg: nan
- Accuracy Wht: 0.0133
- Iou Bkg: 0.0
- Iou Wht: 0.0133

## 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:
- learning_rate: 6e-05
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bkg | Accuracy Wht | Iou Bkg | Iou Wht |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:-------:|:-------:|
| 0.1707        | 1.0   | 180  | 0.0800          | 0.0      | 0.0           | 0.0              | nan          | 0.0          | 0.0     | 0.0     |
| 0.0919        | 2.0   | 360  | 0.0798          | 0.0      | 0.0           | 0.0              | nan          | 0.0          | 0.0     | 0.0     |
| 0.0874        | 3.0   | 540  | 0.0771          | 0.0      | 0.0           | 0.0              | nan          | 0.0          | 0.0     | 0.0     |
| 0.0843        | 4.0   | 720  | 0.0786          | 0.0      | 0.0           | 0.0              | nan          | 0.0          | 0.0     | 0.0     |
| 0.0808        | 5.0   | 900  | 0.0805          | 0.0014   | 0.0029        | 0.0029           | nan          | 0.0029       | 0.0     | 0.0029  |
| 0.0775        | 6.0   | 1080 | 0.0796          | 0.0      | 0.0           | 0.0              | nan          | 0.0          | 0.0     | 0.0     |
| 0.0712        | 7.0   | 1260 | 0.0906          | 0.0381   | 0.0762        | 0.0762           | nan          | 0.0762       | 0.0     | 0.0762  |
| 0.0629        | 8.0   | 1440 | 0.0830          | 0.0055   | 0.0110        | 0.0110           | nan          | 0.0110       | 0.0     | 0.0110  |
| 0.0547        | 9.0   | 1620 | 0.0911          | 0.0141   | 0.0281        | 0.0281           | nan          | 0.0281       | 0.0     | 0.0281  |
| 0.0483        | 10.0  | 1800 | 0.1032          | 0.0097   | 0.0194        | 0.0194           | nan          | 0.0194       | 0.0     | 0.0194  |
| 0.0443        | 11.0  | 1980 | 0.0924          | 0.0146   | 0.0292        | 0.0292           | nan          | 0.0292       | 0.0     | 0.0292  |
| 0.0406        | 12.0  | 2160 | 0.0979          | 0.0069   | 0.0137        | 0.0137           | nan          | 0.0137       | 0.0     | 0.0137  |
| 0.0369        | 13.0  | 2340 | 0.1030          | 0.0103   | 0.0206        | 0.0206           | nan          | 0.0206       | 0.0     | 0.0206  |
| 0.0352        | 14.0  | 2520 | 0.0993          | 0.0065   | 0.0129        | 0.0129           | nan          | 0.0129       | 0.0     | 0.0129  |
| 0.0341        | 15.0  | 2700 | 0.0978          | 0.0062   | 0.0125        | 0.0125           | nan          | 0.0125       | 0.0     | 0.0125  |
| 0.0324        | 16.0  | 2880 | 0.1044          | 0.0151   | 0.0302        | 0.0302           | nan          | 0.0302       | 0.0     | 0.0302  |
| 0.0307        | 17.0  | 3060 | 0.1014          | 0.0164   | 0.0328        | 0.0328           | nan          | 0.0328       | 0.0     | 0.0328  |
| 0.03          | 18.0  | 3240 | 0.1043          | 0.0128   | 0.0257        | 0.0257           | nan          | 0.0257       | 0.0     | 0.0257  |
| 0.0295        | 19.0  | 3420 | 0.1093          | 0.0083   | 0.0165        | 0.0165           | nan          | 0.0165       | 0.0     | 0.0165  |
| 0.0273        | 20.0  | 3600 | 0.1136          | 0.0100   | 0.0201        | 0.0201           | nan          | 0.0201       | 0.0     | 0.0201  |
| 0.0264        | 21.0  | 3780 | 0.1086          | 0.0154   | 0.0309        | 0.0309           | nan          | 0.0309       | 0.0     | 0.0309  |
| 0.0261        | 22.0  | 3960 | 0.1107          | 0.0165   | 0.0330        | 0.0330           | nan          | 0.0330       | 0.0     | 0.0330  |
| 0.0257        | 23.0  | 4140 | 0.1119          | 0.0137   | 0.0274        | 0.0274           | nan          | 0.0274       | 0.0     | 0.0274  |
| 0.0248        | 24.0  | 4320 | 0.1140          | 0.0101   | 0.0201        | 0.0201           | nan          | 0.0201       | 0.0     | 0.0201  |
| 0.0242        | 25.0  | 4500 | 0.1056          | 0.0168   | 0.0336        | 0.0336           | nan          | 0.0336       | 0.0     | 0.0336  |
| 0.024         | 26.0  | 4680 | 0.1143          | 0.0100   | 0.0200        | 0.0200           | nan          | 0.0200       | 0.0     | 0.0200  |
| 0.0234        | 27.0  | 4860 | 0.1155          | 0.0091   | 0.0181        | 0.0181           | nan          | 0.0181       | 0.0     | 0.0181  |
| 0.0228        | 28.0  | 5040 | 0.1201          | 0.0073   | 0.0146        | 0.0146           | nan          | 0.0146       | 0.0     | 0.0146  |
| 0.0226        | 29.0  | 5220 | 0.1192          | 0.0094   | 0.0188        | 0.0188           | nan          | 0.0188       | 0.0     | 0.0188  |
| 0.0224        | 30.0  | 5400 | 0.1187          | 0.0118   | 0.0237        | 0.0237           | nan          | 0.0237       | 0.0     | 0.0237  |
| 0.0218        | 31.0  | 5580 | 0.1227          | 0.0105   | 0.0209        | 0.0209           | nan          | 0.0209       | 0.0     | 0.0209  |
| 0.0211        | 32.0  | 5760 | 0.1159          | 0.0155   | 0.0310        | 0.0310           | nan          | 0.0310       | 0.0     | 0.0310  |
| 0.0208        | 33.0  | 5940 | 0.1224          | 0.0108   | 0.0215        | 0.0215           | nan          | 0.0215       | 0.0     | 0.0215  |
| 0.0203        | 34.0  | 6120 | 0.1239          | 0.0123   | 0.0246        | 0.0246           | nan          | 0.0246       | 0.0     | 0.0246  |
| 0.0197        | 35.0  | 6300 | 0.1285          | 0.0065   | 0.0130        | 0.0130           | nan          | 0.0130       | 0.0     | 0.0130  |
| 0.02          | 36.0  | 6480 | 0.1293          | 0.0037   | 0.0075        | 0.0075           | nan          | 0.0075       | 0.0     | 0.0075  |
| 0.0192        | 37.0  | 6660 | 0.1258          | 0.0059   | 0.0119        | 0.0119           | nan          | 0.0119       | 0.0     | 0.0119  |
| 0.0193        | 38.0  | 6840 | 0.1234          | 0.0105   | 0.0210        | 0.0210           | nan          | 0.0210       | 0.0     | 0.0210  |
| 0.0189        | 39.0  | 7020 | 0.1267          | 0.0080   | 0.0159        | 0.0159           | nan          | 0.0159       | 0.0     | 0.0159  |
| 0.0181        | 40.0  | 7200 | 0.1308          | 0.0060   | 0.0120        | 0.0120           | nan          | 0.0120       | 0.0     | 0.0120  |
| 0.0183        | 41.0  | 7380 | 0.1337          | 0.0056   | 0.0112        | 0.0112           | nan          | 0.0112       | 0.0     | 0.0112  |
| 0.018         | 42.0  | 7560 | 0.1349          | 0.0071   | 0.0142        | 0.0142           | nan          | 0.0142       | 0.0     | 0.0142  |
| 0.0178        | 43.0  | 7740 | 0.1332          | 0.0069   | 0.0139        | 0.0139           | nan          | 0.0139       | 0.0     | 0.0139  |
| 0.0171        | 44.0  | 7920 | 0.1363          | 0.0066   | 0.0132        | 0.0132           | nan          | 0.0132       | 0.0     | 0.0132  |
| 0.0176        | 45.0  | 8100 | 0.1352          | 0.0065   | 0.0131        | 0.0131           | nan          | 0.0131       | 0.0     | 0.0131  |
| 0.0181        | 46.0  | 8280 | 0.1384          | 0.0064   | 0.0127        | 0.0127           | nan          | 0.0127       | 0.0     | 0.0127  |
| 0.0173        | 47.0  | 8460 | 0.1419          | 0.0065   | 0.0129        | 0.0129           | nan          | 0.0129       | 0.0     | 0.0129  |
| 0.0176        | 48.0  | 8640 | 0.1374          | 0.0081   | 0.0161        | 0.0161           | nan          | 0.0161       | 0.0     | 0.0161  |
| 0.0173        | 49.0  | 8820 | 0.1383          | 0.0065   | 0.0130        | 0.0130           | nan          | 0.0130       | 0.0     | 0.0130  |
| 0.0173        | 50.0  | 9000 | 0.1400          | 0.0066   | 0.0133        | 0.0133           | nan          | 0.0133       | 0.0     | 0.0133  |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "other", "tags": ["generated_from_trainer"], "base_model": "nvidia/mit-b2", "model-index": [{"name": "testing_100_epoches", "results": []}]}
transformers pytorch segformer generated_from_trainer base_model:nvidia/mit-b2 license:other endpoints_compatible region:us