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# AhamadShaik/SegFormer_PADDING_x.6

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: 0.0159
- Train Dice Coef: 0.8034
- Train Iou: 0.6803
- Validation Loss: 0.0223
- Validation Dice Coef: 0.8606
- Validation Iou: 0.7573
- 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 |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.2116     | 0.3018          | 0.1931    | 0.0863          | 0.6813               | 0.5211         | 1e-04    | 0     |
| 0.0722     | 0.4966          | 0.3490    | 0.0565          | 0.7560               | 0.6108         | 1e-04    | 1     |
| 0.0544     | 0.5768          | 0.4227    | 0.0465          | 0.7728               | 0.6368         | 1e-04    | 2     |
| 0.0446     | 0.6305          | 0.4771    | 0.0379          | 0.8130               | 0.6869         | 1e-04    | 3     |
| 0.0422     | 0.6479          | 0.4950    | 0.0366          | 0.8005               | 0.6719         | 1e-04    | 4     |
| 0.0375     | 0.6776          | 0.5273    | 0.0315          | 0.8327               | 0.7155         | 1e-04    | 5     |
| 0.0351     | 0.6926          | 0.5428    | 0.0311          | 0.8340               | 0.7177         | 1e-04    | 6     |
| 0.0341     | 0.6967          | 0.5485    | 0.0295          | 0.8377               | 0.7228         | 1e-04    | 7     |
| 0.0307     | 0.7246          | 0.5794    | 0.0278          | 0.8444               | 0.7328         | 1e-04    | 8     |
| 0.0318     | 0.7119          | 0.5664    | 0.0278          | 0.8423               | 0.7297         | 1e-04    | 9     |
| 0.0284     | 0.7362          | 0.5940    | 0.0280          | 0.8435               | 0.7314         | 1e-04    | 10    |
| 0.0278     | 0.7382          | 0.5979    | 0.0284          | 0.8371               | 0.7232         | 1e-04    | 11    |
| 0.0268     | 0.7429          | 0.6030    | 0.0261          | 0.8504               | 0.7419         | 1e-04    | 12    |
| 0.0262     | 0.7464          | 0.6072    | 0.0285          | 0.8408               | 0.7280         | 1e-04    | 13    |
| 0.0247     | 0.7560          | 0.6189    | 0.0255          | 0.8505               | 0.7419         | 1e-04    | 14    |
| 0.0244     | 0.7580          | 0.6209    | 0.0249          | 0.8524               | 0.7450         | 1e-04    | 15    |
| 0.0221     | 0.7719          | 0.6385    | 0.0246          | 0.8503               | 0.7422         | 1e-04    | 16    |
| 0.0234     | 0.7623          | 0.6261    | 0.0233          | 0.8567               | 0.7516         | 1e-04    | 17    |
| 0.0253     | 0.7527          | 0.6147    | 0.0258          | 0.8481               | 0.7401         | 1e-04    | 18    |
| 0.0241     | 0.7597          | 0.6236    | 0.0258          | 0.8430               | 0.7331         | 1e-04    | 19    |
| 0.0230     | 0.7657          | 0.6310    | 0.0224          | 0.8571               | 0.7522         | 1e-04    | 20    |
| 0.0210     | 0.7755          | 0.6431    | 0.0220          | 0.8609               | 0.7577         | 1e-04    | 21    |
| 0.0195     | 0.7867          | 0.6572    | 0.0231          | 0.8578               | 0.7531         | 1e-04    | 22    |
| 0.0192     | 0.7880          | 0.6592    | 0.0226          | 0.8602               | 0.7568         | 1e-04    | 23    |
| 0.0185     | 0.7909          | 0.6630    | 0.0231          | 0.8591               | 0.7549         | 1e-04    | 24    |
| 0.0186     | 0.7906          | 0.6626    | 0.0221          | 0.8590               | 0.7551         | 1e-04    | 25    |
| 0.0196     | 0.7836          | 0.6531    | 0.0239          | 0.8550               | 0.7491         | 1e-04    | 26    |
| 0.0177     | 0.7975          | 0.6717    | 0.0223          | 0.8589               | 0.7549         | 5e-06    | 27    |
| 0.0173     | 0.7979          | 0.6727    | 0.0228          | 0.8585               | 0.7542         | 5e-06    | 28    |
| 0.0170     | 0.7980          | 0.6731    | 0.0215          | 0.8594               | 0.7556         | 5e-06    | 29    |
| 0.0168     | 0.8003          | 0.6755    | 0.0213          | 0.8616               | 0.7590         | 5e-06    | 30    |
| 0.0167     | 0.8016          | 0.6774    | 0.0211          | 0.8614               | 0.7587         | 5e-06    | 31    |
| 0.0167     | 0.8044          | 0.6807    | 0.0217          | 0.8598               | 0.7562         | 5e-06    | 32    |
| 0.0167     | 0.8048          | 0.6815    | 0.0211          | 0.8622               | 0.7599         | 5e-06    | 33    |
| 0.0164     | 0.8013          | 0.6773    | 0.0213          | 0.8621               | 0.7596         | 5e-06    | 34    |
| 0.0162     | 0.8025          | 0.6790    | 0.0216          | 0.8608               | 0.7578         | 5e-06    | 35    |
| 0.0163     | 0.8018          | 0.6784    | 0.0212          | 0.8615               | 0.7587         | 5e-06    | 36    |
| 0.0161     | 0.8043          | 0.6818    | 0.0211          | 0.8627               | 0.7605         | 2.5e-07  | 37    |
| 0.0161     | 0.8025          | 0.6793    | 0.0218          | 0.8604               | 0.7572         | 2.5e-07  | 38    |
| 0.0163     | 0.8039          | 0.6810    | 0.0211          | 0.8618               | 0.7592         | 2.5e-07  | 39    |
| 0.0159     | 0.8044          | 0.6816    | 0.0215          | 0.8622               | 0.7597         | 2.5e-07  | 40    |
| 0.0157     | 0.8068          | 0.6841    | 0.0213          | 0.8612               | 0.7584         | 2.5e-07  | 41    |
| 0.0159     | 0.8063          | 0.6837    | 0.0214          | 0.8615               | 0.7588         | 1.25e-08 | 42    |
| 0.0160     | 0.8040          | 0.6814    | 0.0217          | 0.8609               | 0.7578         | 1.25e-08 | 43    |
| 0.0159     | 0.8072          | 0.6852    | 0.0213          | 0.8616               | 0.7589         | 1.25e-08 | 44    |
| 0.0160     | 0.8062          | 0.6836    | 0.0215          | 0.8611               | 0.7581         | 1.25e-08 | 45    |
| 0.0159     | 0.8045          | 0.6820    | 0.0211          | 0.8623               | 0.7600         | 1.25e-08 | 46    |
| 0.0162     | 0.8027          | 0.6798    | 0.0210          | 0.8622               | 0.7599         | 6.25e-10 | 47    |
| 0.0160     | 0.8039          | 0.6807    | 0.0218          | 0.8606               | 0.7575         | 6.25e-10 | 48    |
| 0.0159     | 0.8093          | 0.6874    | 0.0220          | 0.8601               | 0.7566         | 6.25e-10 | 49    |
| 0.0159     | 0.8072          | 0.6841    | 0.0217          | 0.8622               | 0.7596         | 6.25e-10 | 50    |
| 0.0159     | 0.8045          | 0.6815    | 0.0213          | 0.8614               | 0.7586         | 6.25e-10 | 51    |
| 0.0159     | 0.8111          | 0.6894    | 0.0216          | 0.8615               | 0.7588         | 6.25e-10 | 52    |
| 0.0158     | 0.8066          | 0.6843    | 0.0213          | 0.8617               | 0.7592         | 1e-10    | 53    |
| 0.0161     | 0.8042          | 0.6813    | 0.0212          | 0.8618               | 0.7592         | 1e-10    | 54    |
| 0.0163     | 0.8058          | 0.6829    | 0.0221          | 0.8604               | 0.7570         | 1e-10    | 55    |
| 0.0164     | 0.8017          | 0.6785    | 0.0214          | 0.8612               | 0.7583         | 1e-10    | 56    |
| 0.0160     | 0.8059          | 0.6827    | 0.0210          | 0.8620               | 0.7595         | 1e-10    | 57    |
| 0.0162     | 0.8038          | 0.6805    | 0.0216          | 0.8616               | 0.7587         | 1e-10    | 58    |
| 0.0160     | 0.8022          | 0.6791    | 0.0222          | 0.8598               | 0.7562         | 1e-10    | 59    |
| 0.0161     | 0.8045          | 0.6812    | 0.0215          | 0.8614               | 0.7585         | 1e-10    | 60    |
| 0.0159     | 0.8026          | 0.6794    | 0.0213          | 0.8605               | 0.7572         | 1e-10    | 61    |
| 0.0161     | 0.8069          | 0.6846    | 0.0216          | 0.8608               | 0.7577         | 1e-10    | 62    |
| 0.0159     | 0.8088          | 0.6873    | 0.0209          | 0.8628               | 0.7607         | 1e-10    | 63    |
| 0.0161     | 0.8016          | 0.6783    | 0.0212          | 0.8616               | 0.7588         | 1e-10    | 64    |
| 0.0161     | 0.8031          | 0.6798    | 0.0213          | 0.8612               | 0.7583         | 1e-10    | 65    |
| 0.0161     | 0.8038          | 0.6811    | 0.0215          | 0.8601               | 0.7566         | 1e-10    | 66    |
| 0.0160     | 0.8052          | 0.6827    | 0.0216          | 0.8608               | 0.7576         | 1e-10    | 67    |
| 0.0161     | 0.8051          | 0.6825    | 0.0216          | 0.8610               | 0.7580         | 1e-10    | 68    |
| 0.0159     | 0.8055          | 0.6826    | 0.0218          | 0.8601               | 0.7568         | 1e-10    | 69    |
| 0.0159     | 0.8024          | 0.6793    | 0.0212          | 0.8617               | 0.7591         | 1e-10    | 70    |
| 0.0158     | 0.8043          | 0.6813    | 0.0214          | 0.8608               | 0.7578         | 1e-10    | 71    |
| 0.0161     | 0.8074          | 0.6850    | 0.0212          | 0.8610               | 0.7579         | 1e-10    | 72    |
| 0.0161     | 0.8066          | 0.6841    | 0.0216          | 0.8615               | 0.7586         | 1e-10    | 73    |
| 0.0159     | 0.8065          | 0.6841    | 0.0214          | 0.8611               | 0.7582         | 1e-10    | 74    |
| 0.0162     | 0.8039          | 0.6808    | 0.0212          | 0.8617               | 0.7591         | 1e-10    | 75    |
| 0.0160     | 0.8036          | 0.6801    | 0.0214          | 0.8616               | 0.7589         | 1e-10    | 76    |
| 0.0161     | 0.8100          | 0.6879    | 0.0211          | 0.8619               | 0.7595         | 1e-10    | 77    |
| 0.0161     | 0.8049          | 0.6816    | 0.0211          | 0.8616               | 0.7590         | 1e-10    | 78    |
| 0.0161     | 0.8037          | 0.6805    | 0.0221          | 0.8596               | 0.7558         | 1e-10    | 79    |
| 0.0159     | 0.8044          | 0.6816    | 0.0219          | 0.8615               | 0.7587         | 1e-10    | 80    |
| 0.0161     | 0.8031          | 0.6796    | 0.0214          | 0.8611               | 0.7581         | 1e-10    | 81    |
| 0.0160     | 0.8016          | 0.6782    | 0.0209          | 0.8622               | 0.7599         | 1e-10    | 82    |
| 0.0162     | 0.8040          | 0.6810    | 0.0211          | 0.8623               | 0.7601         | 1e-10    | 83    |
| 0.0159     | 0.8065          | 0.6844    | 0.0210          | 0.8624               | 0.7602         | 1e-10    | 84    |
| 0.0159     | 0.8064          | 0.6841    | 0.0216          | 0.8613               | 0.7585         | 1e-10    | 85    |
| 0.0159     | 0.8068          | 0.6851    | 0.0212          | 0.8626               | 0.7604         | 1e-10    | 86    |
| 0.0158     | 0.8049          | 0.6822    | 0.0222          | 0.8600               | 0.7564         | 1e-10    | 87    |
| 0.0161     | 0.8028          | 0.6797    | 0.0210          | 0.8621               | 0.7597         | 1e-10    | 88    |
| 0.0163     | 0.8050          | 0.6814    | 0.0218          | 0.8602               | 0.7567         | 1e-10    | 89    |
| 0.0159     | 0.8077          | 0.6858    | 0.0215          | 0.8611               | 0.7582         | 1e-10    | 90    |
| 0.0159     | 0.8067          | 0.6841    | 0.0213          | 0.8623               | 0.7599         | 1e-10    | 91    |
| 0.0160     | 0.8064          | 0.6837    | 0.0213          | 0.8615               | 0.7588         | 1e-10    | 92    |
| 0.0160     | 0.8073          | 0.6847    | 0.0209          | 0.8627               | 0.7606         | 1e-10    | 93    |
| 0.0159     | 0.8056          | 0.6833    | 0.0214          | 0.8612               | 0.7583         | 1e-10    | 94    |
| 0.0159     | 0.8073          | 0.6852    | 0.0213          | 0.8616               | 0.7590         | 1e-10    | 95    |
| 0.0158     | 0.8051          | 0.6832    | 0.0219          | 0.8615               | 0.7587         | 1e-10    | 96    |
| 0.0161     | 0.8053          | 0.6826    | 0.0220          | 0.8593               | 0.7555         | 1e-10    | 97    |
| 0.0161     | 0.8059          | 0.6832    | 0.0218          | 0.8608               | 0.7577         | 1e-10    | 98    |
| 0.0159     | 0.8034          | 0.6803    | 0.0223          | 0.8606               | 0.7573         | 1e-10    | 99    |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.11.0
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AhamadShaik/SegFormer_PADDING_x.6", "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\_x.6
===================================


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: 0.0159
* Train Dice Coef: 0.8034
* Train Iou: 0.6803
* Validation Loss: 0.0223
* Validation Dice Coef: 0.8606
* Validation Iou: 0.7573
* 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 4.27.4
* TensorFlow 2.11.0
* 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 4.27.4
* TensorFlow 2.11.0
* 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 4.27.4
* TensorFlow 2.11.0
* 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 4.27.4
* TensorFlow 2.11.0
* Tokenizers 0.13.2