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# AhamadShaik/SegFormer_RESIZE_x.5

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0497
- Train Dice Coef: 0.8670
- Train Iou: 0.7679
- Validation Loss: 0.0477
- Validation Dice Coef: 0.8831
- Validation Iou: 0.7923
- Train Lr: 1e-10
- Epoch: 99

## 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': 1e-10, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Dice Coef | Train Iou | Validation Loss | Validation Dice Coef | Validation Iou | Train Lr | Epoch |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.2269     | 0.5814          | 0.4248    | 0.1165          | 0.7019               | 0.5504         | 1e-04    | 0     |
| 0.1305     | 0.6934          | 0.5423    | 0.0877          | 0.7790               | 0.6433         | 1e-04    | 1     |
| 0.1116     | 0.7311          | 0.5867    | 0.0729          | 0.8299               | 0.7120         | 1e-04    | 2     |
| 0.0985     | 0.7624          | 0.6241    | 0.0648          | 0.8555               | 0.7491         | 1e-04    | 3     |
| 0.0918     | 0.7766          | 0.6431    | 0.0711          | 0.8271               | 0.7098         | 1e-04    | 4     |
| 0.0869     | 0.7877          | 0.6566    | 0.0607          | 0.8552               | 0.7492         | 1e-04    | 5     |
| 0.0818     | 0.7993          | 0.6722    | 0.0555          | 0.8665               | 0.7662         | 1e-04    | 6     |
| 0.0753     | 0.8136          | 0.6906    | 0.0544          | 0.8701               | 0.7719         | 1e-04    | 7     |
| 0.0719     | 0.8216          | 0.7016    | 0.0530          | 0.8725               | 0.7754         | 1e-04    | 8     |
| 0.0715     | 0.8221          | 0.7027    | 0.0588          | 0.8610               | 0.7579         | 1e-04    | 9     |
| 0.0673     | 0.8304          | 0.7139    | 0.0502          | 0.8766               | 0.7820         | 1e-04    | 10    |
| 0.0634     | 0.8388          | 0.7260    | 0.0520          | 0.8757               | 0.7806         | 1e-04    | 11    |
| 0.0617     | 0.8435          | 0.7328    | 0.0513          | 0.8776               | 0.7831         | 1e-04    | 12    |
| 0.0731     | 0.8230          | 0.7046    | 0.0540          | 0.8722               | 0.7752         | 1e-04    | 13    |
| 0.0612     | 0.8439          | 0.7335    | 0.0523          | 0.8749               | 0.7793         | 1e-04    | 14    |
| 0.0568     | 0.8534          | 0.7473    | 0.0537          | 0.8779               | 0.7842         | 1e-04    | 15    |
| 0.0549     | 0.8569          | 0.7529    | 0.0486          | 0.8817               | 0.7903         | 5e-06    | 16    |
| 0.0526     | 0.8607          | 0.7584    | 0.0470          | 0.8849               | 0.7953         | 5e-06    | 17    |
| 0.0516     | 0.8641          | 0.7633    | 0.0478          | 0.8844               | 0.7946         | 5e-06    | 18    |
| 0.0523     | 0.8625          | 0.7610    | 0.0483          | 0.8817               | 0.7901         | 5e-06    | 19    |
| 0.0507     | 0.8662          | 0.7661    | 0.0475          | 0.8842               | 0.7941         | 5e-06    | 20    |
| 0.0504     | 0.8664          | 0.7665    | 0.0477          | 0.8832               | 0.7924         | 5e-06    | 21    |
| 0.0504     | 0.8674          | 0.7682    | 0.0474          | 0.8833               | 0.7926         | 5e-06    | 22    |
| 0.0501     | 0.8655          | 0.7657    | 0.0475          | 0.8833               | 0.7926         | 2.5e-07  | 23    |
| 0.0498     | 0.8677          | 0.7687    | 0.0471          | 0.8845               | 0.7944         | 2.5e-07  | 24    |
| 0.0504     | 0.8665          | 0.7672    | 0.0470          | 0.8846               | 0.7946         | 2.5e-07  | 25    |
| 0.0502     | 0.8677          | 0.7686    | 0.0472          | 0.8844               | 0.7943         | 2.5e-07  | 26    |
| 0.0502     | 0.8662          | 0.7667    | 0.0477          | 0.8833               | 0.7925         | 2.5e-07  | 27    |
| 0.0507     | 0.8667          | 0.7670    | 0.0462          | 0.8853               | 0.7957         | 1.25e-08 | 28    |
| 0.0495     | 0.8685          | 0.7701    | 0.0475          | 0.8841               | 0.7937         | 1.25e-08 | 29    |
| 0.0503     | 0.8669          | 0.7676    | 0.0472          | 0.8840               | 0.7936         | 1.25e-08 | 30    |
| 0.0495     | 0.8689          | 0.7704    | 0.0471          | 0.8854               | 0.7959         | 1.25e-08 | 31    |
| 0.0496     | 0.8681          | 0.7693    | 0.0474          | 0.8844               | 0.7942         | 1.25e-08 | 32    |
| 0.0502     | 0.8665          | 0.7667    | 0.0480          | 0.8823               | 0.7912         | 1.25e-08 | 33    |
| 0.0499     | 0.8663          | 0.7668    | 0.0467          | 0.8852               | 0.7955         | 6.25e-10 | 34    |
| 0.0498     | 0.8668          | 0.7676    | 0.0471          | 0.8844               | 0.7943         | 6.25e-10 | 35    |
| 0.0505     | 0.8653          | 0.7653    | 0.0480          | 0.8821               | 0.7908         | 6.25e-10 | 36    |
| 0.0497     | 0.8687          | 0.7702    | 0.0471          | 0.8847               | 0.7947         | 6.25e-10 | 37    |
| 0.0506     | 0.8660          | 0.7662    | 0.0476          | 0.8838               | 0.7935         | 6.25e-10 | 38    |
| 0.0499     | 0.8678          | 0.7688    | 0.0473          | 0.8849               | 0.7951         | 1e-10    | 39    |
| 0.0499     | 0.8668          | 0.7676    | 0.0476          | 0.8839               | 0.7935         | 1e-10    | 40    |
| 0.0500     | 0.8672          | 0.7679    | 0.0478          | 0.8829               | 0.7921         | 1e-10    | 41    |
| 0.0500     | 0.8670          | 0.7677    | 0.0468          | 0.8845               | 0.7944         | 1e-10    | 42    |
| 0.0502     | 0.8668          | 0.7673    | 0.0474          | 0.8837               | 0.7932         | 1e-10    | 43    |
| 0.0500     | 0.8666          | 0.7671    | 0.0476          | 0.8832               | 0.7926         | 1e-10    | 44    |
| 0.0495     | 0.8682          | 0.7695    | 0.0474          | 0.8839               | 0.7935         | 1e-10    | 45    |
| 0.0495     | 0.8680          | 0.7690    | 0.0474          | 0.8842               | 0.7938         | 1e-10    | 46    |
| 0.0502     | 0.8666          | 0.7671    | 0.0474          | 0.8840               | 0.7937         | 1e-10    | 47    |
| 0.0501     | 0.8668          | 0.7673    | 0.0473          | 0.8840               | 0.7936         | 1e-10    | 48    |
| 0.0498     | 0.8676          | 0.7686    | 0.0470          | 0.8842               | 0.7939         | 1e-10    | 49    |
| 0.0495     | 0.8677          | 0.7690    | 0.0477          | 0.8831               | 0.7924         | 1e-10    | 50    |
| 0.0496     | 0.8694          | 0.7713    | 0.0471          | 0.8846               | 0.7945         | 1e-10    | 51    |
| 0.0496     | 0.8686          | 0.7699    | 0.0467          | 0.8851               | 0.7953         | 1e-10    | 52    |
| 0.0495     | 0.8688          | 0.7701    | 0.0469          | 0.8848               | 0.7949         | 1e-10    | 53    |
| 0.0497     | 0.8677          | 0.7686    | 0.0468          | 0.8848               | 0.7950         | 1e-10    | 54    |
| 0.0492     | 0.8689          | 0.7704    | 0.0473          | 0.8845               | 0.7944         | 1e-10    | 55    |
| 0.0498     | 0.8678          | 0.7687    | 0.0473          | 0.8837               | 0.7932         | 1e-10    | 56    |
| 0.0502     | 0.8668          | 0.7672    | 0.0471          | 0.8838               | 0.7934         | 1e-10    | 57    |
| 0.0497     | 0.8670          | 0.7676    | 0.0469          | 0.8840               | 0.7936         | 1e-10    | 58    |
| 0.0500     | 0.8680          | 0.7690    | 0.0473          | 0.8837               | 0.7933         | 1e-10    | 59    |
| 0.0497     | 0.8681          | 0.7692    | 0.0467          | 0.8840               | 0.7937         | 1e-10    | 60    |
| 0.0496     | 0.8685          | 0.7694    | 0.0474          | 0.8844               | 0.7944         | 1e-10    | 61    |
| 0.0506     | 0.8659          | 0.7660    | 0.0474          | 0.8838               | 0.7933         | 1e-10    | 62    |
| 0.0496     | 0.8677          | 0.7689    | 0.0472          | 0.8850               | 0.7953         | 1e-10    | 63    |
| 0.0498     | 0.8669          | 0.7675    | 0.0468          | 0.8836               | 0.7930         | 1e-10    | 64    |
| 0.0498     | 0.8675          | 0.7684    | 0.0471          | 0.8843               | 0.7942         | 1e-10    | 65    |
| 0.0499     | 0.8680          | 0.7691    | 0.0472          | 0.8842               | 0.7941         | 1e-10    | 66    |
| 0.0499     | 0.8677          | 0.7688    | 0.0474          | 0.8835               | 0.7928         | 1e-10    | 67    |
| 0.0501     | 0.8655          | 0.7656    | 0.0466          | 0.8855               | 0.7960         | 1e-10    | 68    |
| 0.0499     | 0.8673          | 0.7682    | 0.0480          | 0.8825               | 0.7913         | 1e-10    | 69    |
| 0.0494     | 0.8682          | 0.7698    | 0.0470          | 0.8851               | 0.7955         | 1e-10    | 70    |
| 0.0499     | 0.8676          | 0.7685    | 0.0475          | 0.8837               | 0.7932         | 1e-10    | 71    |
| 0.0500     | 0.8672          | 0.7681    | 0.0467          | 0.8855               | 0.7960         | 1e-10    | 72    |
| 0.0502     | 0.8662          | 0.7664    | 0.0473          | 0.8829               | 0.7919         | 1e-10    | 73    |
| 0.0498     | 0.8670          | 0.7679    | 0.0474          | 0.8846               | 0.7947         | 1e-10    | 74    |
| 0.0501     | 0.8665          | 0.7671    | 0.0480          | 0.8827               | 0.7916         | 1e-10    | 75    |
| 0.0493     | 0.8677          | 0.7689    | 0.0473          | 0.8836               | 0.7930         | 1e-10    | 76    |
| 0.0496     | 0.8678          | 0.7687    | 0.0474          | 0.8843               | 0.7942         | 1e-10    | 77    |
| 0.0495     | 0.8679          | 0.7689    | 0.0472          | 0.8844               | 0.7943         | 1e-10    | 78    |
| 0.0496     | 0.8679          | 0.7690    | 0.0470          | 0.8846               | 0.7945         | 1e-10    | 79    |
| 0.0501     | 0.8673          | 0.7683    | 0.0473          | 0.8836               | 0.7931         | 1e-10    | 80    |
| 0.0497     | 0.8679          | 0.7691    | 0.0471          | 0.8839               | 0.7936         | 1e-10    | 81    |
| 0.0496     | 0.8681          | 0.7693    | 0.0475          | 0.8836               | 0.7931         | 1e-10    | 82    |
| 0.0495     | 0.8689          | 0.7703    | 0.0474          | 0.8836               | 0.7930         | 1e-10    | 83    |
| 0.0496     | 0.8685          | 0.7697    | 0.0470          | 0.8845               | 0.7945         | 1e-10    | 84    |
| 0.0504     | 0.8665          | 0.7669    | 0.0477          | 0.8833               | 0.7926         | 1e-10    | 85    |
| 0.0496     | 0.8677          | 0.7690    | 0.0478          | 0.8830               | 0.7921         | 1e-10    | 86    |
| 0.0493     | 0.8682          | 0.7694    | 0.0470          | 0.8837               | 0.7931         | 1e-10    | 87    |
| 0.0495     | 0.8677          | 0.7688    | 0.0475          | 0.8835               | 0.7929         | 1e-10    | 88    |
| 0.0499     | 0.8668          | 0.7673    | 0.0471          | 0.8844               | 0.7942         | 1e-10    | 89    |
| 0.0495     | 0.8682          | 0.7694    | 0.0476          | 0.8836               | 0.7930         | 1e-10    | 90    |
| 0.0499     | 0.8672          | 0.7679    | 0.0475          | 0.8835               | 0.7929         | 1e-10    | 91    |
| 0.0496     | 0.8676          | 0.7685    | 0.0478          | 0.8831               | 0.7923         | 1e-10    | 92    |
| 0.0500     | 0.8677          | 0.7686    | 0.0475          | 0.8838               | 0.7934         | 1e-10    | 93    |
| 0.0495     | 0.8677          | 0.7686    | 0.0471          | 0.8837               | 0.7931         | 1e-10    | 94    |
| 0.0494     | 0.8680          | 0.7691    | 0.0473          | 0.8835               | 0.7930         | 1e-10    | 95    |
| 0.0500     | 0.8656          | 0.7659    | 0.0465          | 0.8848               | 0.7950         | 1e-10    | 96    |
| 0.0494     | 0.8678          | 0.7690    | 0.0477          | 0.8821               | 0.7907         | 1e-10    | 97    |
| 0.0498     | 0.8681          | 0.7691    | 0.0475          | 0.8843               | 0.7942         | 1e-10    | 98    |
| 0.0497     | 0.8670          | 0.7679    | 0.0477          | 0.8831               | 0.7923         | 1e-10    | 99    |


### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AhamadShaik/SegFormer_RESIZE_x.5", "results": []}]}
transformers tf segformer generated_from_keras_callback license:other endpoints_compatible region:us
TAGS
#transformers #tf #segformer #generated_from_keras_callback #license-other #endpoints_compatible #region-us 

AhamadShaik/SegFormer\_RESIZE\_x.5
==================================


This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.0497
* Train Dice Coef: 0.8670
* Train Iou: 0.7679
* Validation Loss: 0.0477
* Validation Dice Coef: 0.8831
* Validation Iou: 0.7923
* Train Lr: 1e-10
* Epoch: 99


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': 1e-10, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.1
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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': 1e-10, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.1
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #tf #segformer #generated_from_keras_callback #license-other #endpoints_compatible #region-us 
 ### 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': 1e-10, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.1
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #tf #segformer #generated_from_keras_callback #license-other #endpoints_compatible #region-us 
### 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': 1e-10, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.1
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2