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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_Sep
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_Sep
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: 0.0026
- Validation Loss: 0.0116
- 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 |
|:----------:|:---------------:|:-----:|
| 0.1563 | 0.0558 | 0 |
| 0.0488 | 0.0706 | 1 |
| 0.0290 | 0.0230 | 2 |
| 0.0207 | 0.0160 | 3 |
| 0.0164 | 0.0145 | 4 |
| 0.0136 | 0.0133 | 5 |
| 0.0117 | 0.0136 | 6 |
| 0.0106 | 0.0144 | 7 |
| 0.0104 | 0.0147 | 8 |
| 0.0091 | 0.0109 | 9 |
| 0.0078 | 0.0104 | 10 |
| 0.0071 | 0.0108 | 11 |
| 0.0067 | 0.0101 | 12 |
| 0.0064 | 0.0110 | 13 |
| 0.0058 | 0.0096 | 14 |
| 0.0057 | 0.0101 | 15 |
| 0.0055 | 0.0115 | 16 |
| 0.0055 | 0.0109 | 17 |
| 0.0051 | 0.0098 | 18 |
| 0.0051 | 0.0108 | 19 |
| 0.0048 | 0.0097 | 20 |
| 0.0050 | 0.0097 | 21 |
| 0.0047 | 0.0088 | 22 |
| 0.0045 | 0.0110 | 23 |
| 0.0043 | 0.0098 | 24 |
| 0.0041 | 0.0098 | 25 |
| 0.0041 | 0.0126 | 26 |
| 0.0040 | 0.0125 | 27 |
| 0.0042 | 0.0098 | 28 |
| 0.0039 | 0.0109 | 29 |
| 0.0037 | 0.0096 | 30 |
| 0.0038 | 0.0104 | 31 |
| 0.0036 | 0.0103 | 32 |
| 0.0034 | 0.0100 | 33 |
| 0.0033 | 0.0099 | 34 |
| 0.0038 | 0.0100 | 35 |
| 0.0036 | 0.0111 | 36 |
| 0.0036 | 0.0158 | 37 |
| 0.0034 | 0.0104 | 38 |
| 0.0032 | 0.0100 | 39 |
| 0.0030 | 0.0100 | 40 |
| 0.0030 | 0.0099 | 41 |
| 0.0029 | 0.0098 | 42 |
| 0.0028 | 0.0112 | 43 |
| 0.0030 | 0.0105 | 44 |
| 0.0029 | 0.0104 | 45 |
| 0.0029 | 0.0100 | 46 |
| 0.0027 | 0.0119 | 47 |
| 0.0030 | 0.0119 | 48 |
| 0.0028 | 0.0109 | 49 |
| 0.0029 | 0.0106 | 50 |
| 0.0027 | 0.0111 | 51 |
| 0.0027 | 0.0111 | 52 |
| 0.0027 | 0.0132 | 53 |
| 0.0029 | 0.0105 | 54 |
| 0.0026 | 0.0113 | 55 |
| 0.0027 | 0.0108 | 56 |
| 0.0025 | 0.0112 | 57 |
| 0.0025 | 0.0105 | 58 |
| 0.0026 | 0.0116 | 59 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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