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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_Mar24
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_Mar24
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: 0.0037
- Validation Loss: 0.0081
- 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 |
|:----------:|:---------------:|:-----:|
| 0.5861 | 0.0929 | 0 |
| 0.0953 | 0.0606 | 1 |
| 0.0509 | 0.0350 | 2 |
| 0.0308 | 0.0231 | 3 |
| 0.0253 | 0.0219 | 4 |
| 0.0202 | 0.0179 | 5 |
| 0.0162 | 0.0184 | 6 |
| 0.0152 | 0.0188 | 7 |
| 0.0135 | 0.0157 | 8 |
| 0.0119 | 0.0170 | 9 |
| 0.0110 | 0.0150 | 10 |
| 0.0102 | 0.0157 | 11 |
| 0.0097 | 0.0137 | 12 |
| 0.0095 | 0.0141 | 13 |
| 0.0087 | 0.0118 | 14 |
| 0.0079 | 0.0116 | 15 |
| 0.0075 | 0.0119 | 16 |
| 0.0072 | 0.0109 | 17 |
| 0.0069 | 0.0118 | 18 |
| 0.0068 | 0.0104 | 19 |
| 0.0065 | 0.0108 | 20 |
| 0.0064 | 0.0124 | 21 |
| 0.0062 | 0.0095 | 22 |
| 0.0058 | 0.0111 | 23 |
| 0.0058 | 0.0094 | 24 |
| 0.0056 | 0.0111 | 25 |
| 0.0055 | 0.0125 | 26 |
| 0.0057 | 0.0104 | 27 |
| 0.0053 | 0.0096 | 28 |
| 0.0051 | 0.0105 | 29 |
| 0.0050 | 0.0103 | 30 |
| 0.0048 | 0.0091 | 31 |
| 0.0047 | 0.0097 | 32 |
| 0.0044 | 0.0094 | 33 |
| 0.0045 | 0.0092 | 34 |
| 0.0045 | 0.0093 | 35 |
| 0.0047 | 0.0088 | 36 |
| 0.0048 | 0.0089 | 37 |
| 0.0045 | 0.0108 | 38 |
| 0.0043 | 0.0088 | 39 |
| 0.0043 | 0.0090 | 40 |
| 0.0044 | 0.0106 | 41 |
| 0.0053 | 0.0100 | 42 |
| 0.0051 | 0.0102 | 43 |
| 0.0044 | 0.0097 | 44 |
| 0.0039 | 0.0088 | 45 |
| 0.0040 | 0.0097 | 46 |
| 0.0040 | 0.0089 | 47 |
| 0.0037 | 0.0095 | 48 |
| 0.0034 | 0.0085 | 49 |
| 0.0041 | 0.0082 | 50 |
| 0.0054 | 0.0098 | 51 |
| 0.0053 | 0.0085 | 52 |
| 0.0044 | 0.0086 | 53 |
| 0.0040 | 0.0082 | 54 |
| 0.0038 | 0.0082 | 55 |
| 0.0035 | 0.0092 | 56 |
| 0.0034 | 0.0090 | 57 |
| 0.0035 | 0.0079 | 58 |
| 0.0037 | 0.0081 | 59 |
### Framework versions
- Transformers 4.38.1
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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