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# AhamadShaik/SegFormer_PADDING_LM

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: 0.0186
- Train Dice Coef: 0.7789
- Train Iou: 0.6508
- Validation Loss: 0.0233
- Validation Dice Coef: 0.8506
- Validation Iou: 0.7439
- 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', 'learning_rate': 1e-10, 'decay': 0.0, '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 |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.1460     | 0.3657          | 0.2410    | 0.0908          | 0.4603               | 0.3168         | 1e-04    | 0     |
| 0.0610     | 0.5251          | 0.3760    | 0.1773          | 0.1542               | 0.0892         | 1e-04    | 1     |
| 0.0500     | 0.5831          | 0.4322    | 0.0806          | 0.5067               | 0.3659         | 1e-04    | 2     |
| 0.0432     | 0.6204          | 0.4699    | 0.1085          | 0.3757               | 0.2479         | 1e-04    | 3     |
| 0.0413     | 0.6306          | 0.4831    | 0.0771          | 0.5239               | 0.3646         | 1e-04    | 4     |
| 0.0374     | 0.6569          | 0.5086    | 0.0719          | 0.5267               | 0.3854         | 1e-04    | 5     |
| 0.0336     | 0.6770          | 0.5307    | 0.0540          | 0.6264               | 0.4881         | 1e-04    | 6     |
| 0.0302     | 0.7029          | 0.5592    | 0.0518          | 0.6516               | 0.5234         | 1e-04    | 7     |
| 0.0306     | 0.7010          | 0.5582    | 0.0704          | 0.5946               | 0.4483         | 1e-04    | 8     |
| 0.0285     | 0.7160          | 0.5744    | 0.0504          | 0.6951               | 0.5568         | 1e-04    | 9     |
| 0.0287     | 0.7245          | 0.5830    | 0.0357          | 0.7899               | 0.6630         | 1e-04    | 10    |
| 0.0273     | 0.7228          | 0.5825    | 0.0659          | 0.6279               | 0.4914         | 1e-04    | 11    |
| 0.0259     | 0.7344          | 0.5961    | 0.0357          | 0.7986               | 0.6716         | 1e-04    | 12    |
| 0.0257     | 0.7405          | 0.6010    | 0.0385          | 0.7970               | 0.6702         | 1e-04    | 13    |
| 0.0237     | 0.7434          | 0.6076    | 0.0364          | 0.8060               | 0.6841         | 1e-04    | 14    |
| 0.0227     | 0.7532          | 0.6192    | 0.0556          | 0.6927               | 0.5449         | 1e-04    | 15    |
| 0.0225     | 0.7546          | 0.6202    | 0.0242          | 0.8446               | 0.7356         | 5e-06    | 16    |
| 0.0207     | 0.7614          | 0.6312    | 0.0235          | 0.8482               | 0.7406         | 5e-06    | 17    |
| 0.0205     | 0.7676          | 0.6365    | 0.0235          | 0.8489               | 0.7414         | 5e-06    | 18    |
| 0.0200     | 0.7689          | 0.6389    | 0.0238          | 0.8497               | 0.7424         | 5e-06    | 19    |
| 0.0201     | 0.7693          | 0.6384    | 0.0237          | 0.8492               | 0.7418         | 5e-06    | 20    |
| 0.0195     | 0.7738          | 0.6438    | 0.0231          | 0.8504               | 0.7440         | 5e-06    | 21    |
| 0.0196     | 0.7749          | 0.6458    | 0.0234          | 0.8504               | 0.7436         | 5e-06    | 22    |
| 0.0192     | 0.7756          | 0.6464    | 0.0236          | 0.8482               | 0.7407         | 5e-06    | 23    |
| 0.0191     | 0.7741          | 0.6447    | 0.0231          | 0.8503               | 0.7435         | 5e-06    | 24    |
| 0.0191     | 0.7761          | 0.6466    | 0.0238          | 0.8493               | 0.7419         | 5e-06    | 25    |
| 0.0188     | 0.7781          | 0.6503    | 0.0237          | 0.8481               | 0.7405         | 5e-06    | 26    |
| 0.0192     | 0.7729          | 0.6440    | 0.0234          | 0.8483               | 0.7414         | 2.5e-07  | 27    |
| 0.0187     | 0.7849          | 0.6572    | 0.0241          | 0.8478               | 0.7398         | 2.5e-07  | 28    |
| 0.0188     | 0.7786          | 0.6501    | 0.0241          | 0.8484               | 0.7406         | 2.5e-07  | 29    |
| 0.0189     | 0.7815          | 0.6520    | 0.0232          | 0.8507               | 0.7439         | 2.5e-07  | 30    |
| 0.0185     | 0.7715          | 0.6440    | 0.0232          | 0.8505               | 0.7437         | 2.5e-07  | 31    |
| 0.0186     | 0.7764          | 0.6488    | 0.0233          | 0.8487               | 0.7416         | 1.25e-08 | 32    |
| 0.0189     | 0.7725          | 0.6438    | 0.0235          | 0.8492               | 0.7418         | 1.25e-08 | 33    |
| 0.0186     | 0.7767          | 0.6484    | 0.0237          | 0.8491               | 0.7414         | 1.25e-08 | 34    |
| 0.0186     | 0.7800          | 0.6517    | 0.0229          | 0.8503               | 0.7436         | 1.25e-08 | 35    |
| 0.0187     | 0.7758          | 0.6463    | 0.0232          | 0.8501               | 0.7433         | 1.25e-08 | 36    |
| 0.0187     | 0.7774          | 0.6497    | 0.0232          | 0.8496               | 0.7423         | 1.25e-08 | 37    |
| 0.0187     | 0.7791          | 0.6502    | 0.0234          | 0.8496               | 0.7424         | 1.25e-08 | 38    |
| 0.0189     | 0.7743          | 0.6446    | 0.0237          | 0.8501               | 0.7429         | 1.25e-08 | 39    |
| 0.0189     | 0.7770          | 0.6491    | 0.0234          | 0.8479               | 0.7402         | 1.25e-08 | 40    |
| 0.0187     | 0.7793          | 0.6507    | 0.0233          | 0.8507               | 0.7441         | 6.25e-10 | 41    |
| 0.0186     | 0.7788          | 0.6505    | 0.0231          | 0.8502               | 0.7434         | 6.25e-10 | 42    |
| 0.0188     | 0.7773          | 0.6491    | 0.0232          | 0.8510               | 0.7443         | 6.25e-10 | 43    |
| 0.0185     | 0.7775          | 0.6493    | 0.0229          | 0.8518               | 0.7456         | 6.25e-10 | 44    |
| 0.0187     | 0.7765          | 0.6487    | 0.0233          | 0.8491               | 0.7416         | 6.25e-10 | 45    |
| 0.0186     | 0.7804          | 0.6521    | 0.0234          | 0.8499               | 0.7430         | 1e-10    | 46    |
| 0.0187     | 0.7765          | 0.6482    | 0.0235          | 0.8486               | 0.7410         | 1e-10    | 47    |
| 0.0187     | 0.7777          | 0.6497    | 0.0233          | 0.8493               | 0.7419         | 1e-10    | 48    |
| 0.0187     | 0.7785          | 0.6498    | 0.0230          | 0.8502               | 0.7432         | 1e-10    | 49    |
| 0.0188     | 0.7813          | 0.6529    | 0.0235          | 0.8491               | 0.7418         | 1e-10    | 50    |
| 0.0186     | 0.7770          | 0.6498    | 0.0229          | 0.8504               | 0.7435         | 1e-10    | 51    |
| 0.0190     | 0.7764          | 0.6483    | 0.0232          | 0.8503               | 0.7437         | 1e-10    | 52    |
| 0.0189     | 0.7764          | 0.6480    | 0.0233          | 0.8500               | 0.7430         | 1e-10    | 53    |
| 0.0189     | 0.7744          | 0.6461    | 0.0231          | 0.8516               | 0.7449         | 1e-10    | 54    |
| 0.0188     | 0.7767          | 0.6485    | 0.0233          | 0.8499               | 0.7429         | 1e-10    | 55    |
| 0.0189     | 0.7729          | 0.6441    | 0.0234          | 0.8488               | 0.7413         | 1e-10    | 56    |
| 0.0186     | 0.7814          | 0.6531    | 0.0235          | 0.8486               | 0.7408         | 1e-10    | 57    |
| 0.0189     | 0.7772          | 0.6480    | 0.0237          | 0.8482               | 0.7405         | 1e-10    | 58    |
| 0.0187     | 0.7756          | 0.6477    | 0.0231          | 0.8511               | 0.7443         | 1e-10    | 59    |
| 0.0188     | 0.7783          | 0.6500    | 0.0234          | 0.8489               | 0.7415         | 1e-10    | 60    |
| 0.0186     | 0.7771          | 0.6484    | 0.0238          | 0.8482               | 0.7402         | 1e-10    | 61    |
| 0.0186     | 0.7776          | 0.6502    | 0.0231          | 0.8499               | 0.7429         | 1e-10    | 62    |
| 0.0185     | 0.7784          | 0.6504    | 0.0232          | 0.8496               | 0.7422         | 1e-10    | 63    |
| 0.0188     | 0.7797          | 0.6519    | 0.0234          | 0.8484               | 0.7406         | 1e-10    | 64    |
| 0.0189     | 0.7851          | 0.6566    | 0.0230          | 0.8518               | 0.7455         | 1e-10    | 65    |
| 0.0187     | 0.7795          | 0.6515    | 0.0237          | 0.8494               | 0.7420         | 1e-10    | 66    |
| 0.0188     | 0.7779          | 0.6489    | 0.0237          | 0.8470               | 0.7395         | 1e-10    | 67    |
| 0.0190     | 0.7751          | 0.6455    | 0.0243          | 0.8472               | 0.7391         | 1e-10    | 68    |
| 0.0188     | 0.7767          | 0.6486    | 0.0233          | 0.8502               | 0.7433         | 1e-10    | 69    |
| 0.0189     | 0.7819          | 0.6535    | 0.0231          | 0.8504               | 0.7436         | 1e-10    | 70    |
| 0.0188     | 0.7734          | 0.6452    | 0.0230          | 0.8508               | 0.7442         | 1e-10    | 71    |
| 0.0186     | 0.7784          | 0.6516    | 0.0234          | 0.8484               | 0.7414         | 1e-10    | 72    |
| 0.0187     | 0.7706          | 0.6424    | 0.0236          | 0.8483               | 0.7407         | 1e-10    | 73    |
| 0.0189     | 0.7720          | 0.6430    | 0.0237          | 0.8481               | 0.7401         | 1e-10    | 74    |
| 0.0189     | 0.7753          | 0.6464    | 0.0232          | 0.8505               | 0.7439         | 1e-10    | 75    |
| 0.0188     | 0.7759          | 0.6481    | 0.0232          | 0.8500               | 0.7427         | 1e-10    | 76    |
| 0.0188     | 0.7760          | 0.6479    | 0.0235          | 0.8494               | 0.7418         | 1e-10    | 77    |
| 0.0187     | 0.7828          | 0.6538    | 0.0231          | 0.8518               | 0.7456         | 1e-10    | 78    |
| 0.0188     | 0.7771          | 0.6489    | 0.0235          | 0.8488               | 0.7414         | 1e-10    | 79    |
| 0.0188     | 0.7766          | 0.6480    | 0.0235          | 0.8487               | 0.7411         | 1e-10    | 80    |
| 0.0187     | 0.7764          | 0.6492    | 0.0236          | 0.8497               | 0.7421         | 1e-10    | 81    |
| 0.0188     | 0.7769          | 0.6489    | 0.0232          | 0.8504               | 0.7434         | 1e-10    | 82    |
| 0.0190     | 0.7805          | 0.6507    | 0.0237          | 0.8494               | 0.7418         | 1e-10    | 83    |
| 0.0187     | 0.7752          | 0.6473    | 0.0231          | 0.8502               | 0.7431         | 1e-10    | 84    |
| 0.0189     | 0.7758          | 0.6472    | 0.0234          | 0.8484               | 0.7414         | 1e-10    | 85    |
| 0.0185     | 0.7735          | 0.6460    | 0.0234          | 0.8492               | 0.7417         | 1e-10    | 86    |
| 0.0185     | 0.7814          | 0.6534    | 0.0235          | 0.8490               | 0.7414         | 1e-10    | 87    |
| 0.0186     | 0.7762          | 0.6472    | 0.0234          | 0.8490               | 0.7415         | 1e-10    | 88    |
| 0.0189     | 0.7769          | 0.6481    | 0.0230          | 0.8514               | 0.7452         | 1e-10    | 89    |
| 0.0186     | 0.7776          | 0.6495    | 0.0238          | 0.8496               | 0.7422         | 1e-10    | 90    |
| 0.0188     | 0.7772          | 0.6486    | 0.0233          | 0.8496               | 0.7423         | 1e-10    | 91    |
| 0.0186     | 0.7743          | 0.6467    | 0.0231          | 0.8505               | 0.7436         | 1e-10    | 92    |
| 0.0188     | 0.7794          | 0.6505    | 0.0233          | 0.8503               | 0.7431         | 1e-10    | 93    |
| 0.0186     | 0.7739          | 0.6455    | 0.0237          | 0.8476               | 0.7395         | 1e-10    | 94    |
| 0.0188     | 0.7769          | 0.6477    | 0.0234          | 0.8492               | 0.7419         | 1e-10    | 95    |
| 0.0188     | 0.7689          | 0.6415    | 0.0236          | 0.8487               | 0.7409         | 1e-10    | 96    |
| 0.0194     | 0.7756          | 0.6476    | 0.0236          | 0.8504               | 0.7433         | 1e-10    | 97    |
| 0.0187     | 0.7792          | 0.6504    | 0.0231          | 0.8502               | 0.7436         | 1e-10    | 98    |
| 0.0186     | 0.7789          | 0.6508    | 0.0233          | 0.8506               | 0.7439         | 1e-10    | 99    |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.10.1
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AhamadShaik/SegFormer_PADDING_LM", "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\_PADDING\_LM
==================================


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: 0.0186
* Train Dice Coef: 0.7789
* Train Iou: 0.6508
* Validation Loss: 0.0233
* Validation Dice Coef: 0.8506
* Validation Iou: 0.7439
* 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', 'learning\_rate': 1e-10, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.4
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-10, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3
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', 'learning\_rate': 1e-10, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3
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', 'learning\_rate': 1e-10, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.4
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3