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---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_keras_callback
model-index:
- name: Segformer-MRIseg_model
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. -->
# Segformer-MRIseg_model
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0049
- Validation Loss: 0.0133
- Epoch: 59
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2537 | 0.0685 | 0 |
| 0.0849 | 0.0639 | 1 |
| 0.0664 | 0.0532 | 2 |
| 0.0580 | 0.0503 | 3 |
| 0.0536 | 0.0497 | 4 |
| 0.0476 | 0.0396 | 5 |
| 0.0437 | 0.0477 | 6 |
| 0.0359 | 0.0397 | 7 |
| 0.0312 | 0.0289 | 8 |
| 0.0256 | 0.0322 | 9 |
| 0.0241 | 0.0279 | 10 |
| 0.0220 | 0.0229 | 11 |
| 0.0180 | 0.0226 | 12 |
| 0.0160 | 0.0192 | 13 |
| 0.0165 | 0.0227 | 14 |
| 0.0151 | 0.0194 | 15 |
| 0.0146 | 0.0184 | 16 |
| 0.0132 | 0.0177 | 17 |
| 0.0121 | 0.0211 | 18 |
| 0.0111 | 0.0197 | 19 |
| 0.0107 | 0.0175 | 20 |
| 0.0116 | 0.0131 | 21 |
| 0.0115 | 0.0181 | 22 |
| 0.0094 | 0.0153 | 23 |
| 0.0099 | 0.0140 | 24 |
| 0.0098 | 0.0151 | 25 |
| 0.0084 | 0.0126 | 26 |
| 0.0080 | 0.0140 | 27 |
| 0.0071 | 0.0128 | 28 |
| 0.0067 | 0.0169 | 29 |
| 0.0061 | 0.0131 | 30 |
| 0.0063 | 0.0207 | 31 |
| 0.0067 | 0.0129 | 32 |
| 0.0062 | 0.0152 | 33 |
| 0.0056 | 0.0148 | 34 |
| 0.0056 | 0.0171 | 35 |
| 0.0051 | 0.0154 | 36 |
| 0.0049 | 0.0172 | 37 |
| 0.0049 | 0.0180 | 38 |
| 0.0056 | 0.0168 | 39 |
| 0.0050 | 0.0142 | 40 |
| 0.0048 | 0.0165 | 41 |
| 0.0051 | 0.0195 | 42 |
| 0.0048 | 0.0232 | 43 |
| 0.0042 | 0.0208 | 44 |
| 0.0041 | 0.0249 | 45 |
| 0.0044 | 0.0220 | 46 |
| 0.0041 | 0.0234 | 47 |
| 0.0042 | 0.0198 | 48 |
| 0.0040 | 0.0282 | 49 |
| 0.0039 | 0.0251 | 50 |
| 0.0039 | 0.0302 | 51 |
| 0.0041 | 0.0219 | 52 |
| 0.0040 | 0.0187 | 53 |
| 0.0039 | 0.0203 | 54 |
| 0.0043 | 0.0180 | 55 |
| 0.0051 | 0.0150 | 56 |
| 0.0079 | 0.0205 | 57 |
| 0.0052 | 0.0152 | 58 |
| 0.0049 | 0.0133 | 59 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
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