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---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_keras_callback
model-index:
- name: akar49/MRI_segmentation-1
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. -->
# akar49/MRI_segmentation-1
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: 0.0034
- Validation Loss: 0.0098
- 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 |
|:----------:|:---------------:|:-----:|
| 0.3300 | 0.0731 | 0 |
| 0.0881 | 0.0651 | 1 |
| 0.0660 | 0.0632 | 2 |
| 0.0573 | 0.0463 | 3 |
| 0.0525 | 0.0495 | 4 |
| 0.0475 | 0.0431 | 5 |
| 0.0379 | 0.0367 | 6 |
| 0.0318 | 0.0319 | 7 |
| 0.0280 | 0.0260 | 8 |
| 0.0246 | 0.0253 | 9 |
| 0.0235 | 0.0267 | 10 |
| 0.0205 | 0.0205 | 11 |
| 0.0182 | 0.0227 | 12 |
| 0.0182 | 0.0234 | 13 |
| 0.0169 | 0.0197 | 14 |
| 0.0163 | 0.0200 | 15 |
| 0.0173 | 0.0192 | 16 |
| 0.0130 | 0.0179 | 17 |
| 0.0123 | 0.0123 | 18 |
| 0.0103 | 0.0137 | 19 |
| 0.0098 | 0.0121 | 20 |
| 0.0114 | 0.0115 | 21 |
| 0.0097 | 0.0116 | 22 |
| 0.0083 | 0.0108 | 23 |
| 0.0078 | 0.0116 | 24 |
| 0.0074 | 0.0102 | 25 |
| 0.0071 | 0.0121 | 26 |
| 0.0073 | 0.0104 | 27 |
| 0.0071 | 0.0128 | 28 |
| 0.0066 | 0.0110 | 29 |
| 0.0061 | 0.0103 | 30 |
| 0.0067 | 0.0115 | 31 |
| 0.0071 | 0.0246 | 32 |
| 0.0091 | 0.0106 | 33 |
| 0.0079 | 0.0107 | 34 |
| 0.0066 | 0.0109 | 35 |
| 0.0059 | 0.0104 | 36 |
| 0.0057 | 0.0092 | 37 |
| 0.0055 | 0.0096 | 38 |
| 0.0052 | 0.0099 | 39 |
| 0.0051 | 0.0102 | 40 |
| 0.0057 | 0.0108 | 41 |
| 0.0049 | 0.0097 | 42 |
| 0.0048 | 0.0093 | 43 |
| 0.0047 | 0.0095 | 44 |
| 0.0045 | 0.0115 | 45 |
| 0.0047 | 0.0114 | 46 |
| 0.0048 | 0.0105 | 47 |
| 0.0049 | 0.0093 | 48 |
| 0.0050 | 0.0093 | 49 |
| 0.0044 | 0.0097 | 50 |
| 0.0040 | 0.0101 | 51 |
| 0.0041 | 0.0098 | 52 |
| 0.0041 | 0.0099 | 53 |
| 0.0039 | 0.0109 | 54 |
| 0.0040 | 0.0099 | 55 |
| 0.0038 | 0.0112 | 56 |
| 0.0037 | 0.0096 | 57 |
| 0.0035 | 0.0106 | 58 |
| 0.0034 | 0.0098 | 59 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.2
- Tokenizers 0.13.3
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