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# AhamadShaik/SegFormer_RESIZE_NLM

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: 0.0424
- Train Dice Coef: 0.8817
- Train Iou: 0.7903
- Validation Loss: 0.0436
- Validation Dice Coef: 0.8897
- Validation Iou: 0.8024
- 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 |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.2282     | 0.5657          | 0.4102    | 0.1322          | 0.6524               | 0.4967         | 1e-04    | 0     |
| 0.1354     | 0.6853          | 0.5329    | 0.0855          | 0.7853               | 0.6544         | 1e-04    | 1     |
| 0.1105     | 0.7364          | 0.5924    | 0.0737          | 0.8147               | 0.6916         | 1e-04    | 2     |
| 0.0985     | 0.7610          | 0.6226    | 0.0632          | 0.8518               | 0.7440         | 1e-04    | 3     |
| 0.0933     | 0.7745          | 0.6399    | 0.0627          | 0.8455               | 0.7351         | 1e-04    | 4     |
| 0.0886     | 0.7856          | 0.6535    | 0.0584          | 0.8603               | 0.7566         | 1e-04    | 5     |
| 0.0831     | 0.7971          | 0.6695    | 0.0559          | 0.8621               | 0.7596         | 1e-04    | 6     |
| 0.0770     | 0.8107          | 0.6867    | 0.0530          | 0.8726               | 0.7756         | 1e-04    | 7     |
| 0.0741     | 0.8160          | 0.6942    | 0.0512          | 0.8775               | 0.7832         | 1e-04    | 8     |
| 0.0750     | 0.8163          | 0.6945    | 0.0581          | 0.8627               | 0.7606         | 1e-04    | 9     |
| 0.0678     | 0.8306          | 0.7138    | 0.0531          | 0.8719               | 0.7745         | 1e-04    | 10    |
| 0.0659     | 0.8341          | 0.7196    | 0.0519          | 0.8738               | 0.7781         | 1e-04    | 11    |
| 0.0626     | 0.8412          | 0.7294    | 0.0496          | 0.8789               | 0.7853         | 1e-04    | 12    |
| 0.0637     | 0.8383          | 0.7257    | 0.0515          | 0.8772               | 0.7828         | 1e-04    | 13    |
| 0.0601     | 0.8462          | 0.7367    | 0.0498          | 0.8765               | 0.7814         | 1e-04    | 14    |
| 0.0573     | 0.8525          | 0.7458    | 0.0474          | 0.8817               | 0.7897         | 1e-04    | 15    |
| 0.0565     | 0.8520          | 0.7456    | 0.0459          | 0.8850               | 0.7948         | 1e-04    | 16    |
| 0.0633     | 0.8381          | 0.7262    | 0.0487          | 0.8797               | 0.7868         | 1e-04    | 17    |
| 0.0558     | 0.8544          | 0.7489    | 0.0476          | 0.8828               | 0.7917         | 1e-04    | 18    |
| 0.0523     | 0.8617          | 0.7595    | 0.0454          | 0.8872               | 0.7983         | 1e-04    | 19    |
| 0.0516     | 0.8632          | 0.7617    | 0.0465          | 0.8838               | 0.7934         | 1e-04    | 20    |
| 0.0515     | 0.8636          | 0.7625    | 0.0494          | 0.8816               | 0.7894         | 1e-04    | 21    |
| 0.0518     | 0.8630          | 0.7615    | 0.0487          | 0.8836               | 0.7930         | 1e-04    | 22    |
| 0.0521     | 0.8616          | 0.7595    | 0.0483          | 0.8822               | 0.7908         | 1e-04    | 23    |
| 0.0510     | 0.8634          | 0.7624    | 0.0501          | 0.8814               | 0.7899         | 1e-04    | 24    |
| 0.0485     | 0.8703          | 0.7728    | 0.0439          | 0.8892               | 0.8018         | 5e-06    | 25    |
| 0.0464     | 0.8755          | 0.7807    | 0.0433          | 0.8890               | 0.8015         | 5e-06    | 26    |
| 0.0456     | 0.8760          | 0.7817    | 0.0439          | 0.8884               | 0.8004         | 5e-06    | 27    |
| 0.0446     | 0.8790          | 0.7860    | 0.0428          | 0.8896               | 0.8024         | 5e-06    | 28    |
| 0.0443     | 0.8786          | 0.7855    | 0.0426          | 0.8905               | 0.8038         | 5e-06    | 29    |
| 0.0439     | 0.8795          | 0.7867    | 0.0439          | 0.8881               | 0.7999         | 5e-06    | 30    |
| 0.0436     | 0.8800          | 0.7876    | 0.0429          | 0.8902               | 0.8032         | 5e-06    | 31    |
| 0.0430     | 0.8809          | 0.7890    | 0.0439          | 0.8876               | 0.7992         | 5e-06    | 32    |
| 0.0427     | 0.8812          | 0.7894    | 0.0432          | 0.8892               | 0.8016         | 5e-06    | 33    |
| 0.0431     | 0.8798          | 0.7875    | 0.0433          | 0.8895               | 0.8022         | 5e-06    | 34    |
| 0.0425     | 0.8816          | 0.7903    | 0.0435          | 0.8892               | 0.8016         | 2.5e-07  | 35    |
| 0.0420     | 0.8826          | 0.7917    | 0.0433          | 0.8894               | 0.8021         | 2.5e-07  | 36    |
| 0.0423     | 0.8833          | 0.7926    | 0.0429          | 0.8893               | 0.8018         | 2.5e-07  | 37    |
| 0.0420     | 0.8833          | 0.7929    | 0.0430          | 0.8895               | 0.8023         | 2.5e-07  | 38    |
| 0.0424     | 0.8832          | 0.7924    | 0.0437          | 0.8890               | 0.8013         | 2.5e-07  | 39    |
| 0.0422     | 0.8824          | 0.7914    | 0.0427          | 0.8897               | 0.8024         | 1.25e-08 | 40    |
| 0.0426     | 0.8824          | 0.7913    | 0.0431          | 0.8900               | 0.8030         | 1.25e-08 | 41    |
| 0.0424     | 0.8832          | 0.7926    | 0.0433          | 0.8893               | 0.8019         | 1.25e-08 | 42    |
| 0.0424     | 0.8830          | 0.7922    | 0.0436          | 0.8886               | 0.8008         | 1.25e-08 | 43    |
| 0.0427     | 0.8806          | 0.7888    | 0.0434          | 0.8893               | 0.8020         | 1.25e-08 | 44    |
| 0.0421     | 0.8829          | 0.7921    | 0.0431          | 0.8899               | 0.8028         | 6.25e-10 | 45    |
| 0.0427     | 0.8817          | 0.7901    | 0.0431          | 0.8896               | 0.8023         | 6.25e-10 | 46    |
| 0.0422     | 0.8825          | 0.7916    | 0.0433          | 0.8895               | 0.8022         | 6.25e-10 | 47    |
| 0.0423     | 0.8823          | 0.7912    | 0.0431          | 0.8897               | 0.8024         | 6.25e-10 | 48    |
| 0.0423     | 0.8826          | 0.7916    | 0.0433          | 0.8895               | 0.8021         | 6.25e-10 | 49    |
| 0.0425     | 0.8827          | 0.7918    | 0.0433          | 0.8896               | 0.8023         | 1e-10    | 50    |
| 0.0421     | 0.8838          | 0.7937    | 0.0431          | 0.8891               | 0.8014         | 1e-10    | 51    |
| 0.0424     | 0.8820          | 0.7907    | 0.0436          | 0.8884               | 0.8003         | 1e-10    | 52    |
| 0.0424     | 0.8824          | 0.7915    | 0.0426          | 0.8899               | 0.8029         | 1e-10    | 53    |
| 0.0423     | 0.8828          | 0.7920    | 0.0433          | 0.8894               | 0.8020         | 1e-10    | 54    |
| 0.0424     | 0.8818          | 0.7905    | 0.0431          | 0.8901               | 0.8031         | 1e-10    | 55    |
| 0.0421     | 0.8823          | 0.7911    | 0.0438          | 0.8887               | 0.8008         | 1e-10    | 56    |
| 0.0421     | 0.8821          | 0.7909    | 0.0426          | 0.8896               | 0.8023         | 1e-10    | 57    |
| 0.0420     | 0.8818          | 0.7906    | 0.0428          | 0.8903               | 0.8035         | 1e-10    | 58    |
| 0.0416     | 0.8845          | 0.7945    | 0.0434          | 0.8889               | 0.8012         | 1e-10    | 59    |
| 0.0421     | 0.8830          | 0.7921    | 0.0429          | 0.8900               | 0.8029         | 1e-10    | 60    |
| 0.0420     | 0.8834          | 0.7927    | 0.0433          | 0.8888               | 0.8010         | 1e-10    | 61    |
| 0.0425     | 0.8820          | 0.7909    | 0.0429          | 0.8896               | 0.8023         | 1e-10    | 62    |
| 0.0421     | 0.8827          | 0.7919    | 0.0431          | 0.8906               | 0.8039         | 1e-10    | 63    |
| 0.0422     | 0.8815          | 0.7901    | 0.0429          | 0.8901               | 0.8031         | 1e-10    | 64    |
| 0.0420     | 0.8833          | 0.7927    | 0.0430          | 0.8899               | 0.8029         | 1e-10    | 65    |
| 0.0426     | 0.8822          | 0.7911    | 0.0431          | 0.8891               | 0.8015         | 1e-10    | 66    |
| 0.0422     | 0.8829          | 0.7923    | 0.0428          | 0.8902               | 0.8033         | 1e-10    | 67    |
| 0.0424     | 0.8813          | 0.7898    | 0.0435          | 0.8893               | 0.8019         | 1e-10    | 68    |
| 0.0420     | 0.8826          | 0.7918    | 0.0430          | 0.8896               | 0.8024         | 1e-10    | 69    |
| 0.0428     | 0.8811          | 0.7895    | 0.0434          | 0.8900               | 0.8030         | 1e-10    | 70    |
| 0.0422     | 0.8832          | 0.7926    | 0.0431          | 0.8895               | 0.8021         | 1e-10    | 71    |
| 0.0427     | 0.8816          | 0.7902    | 0.0432          | 0.8898               | 0.8026         | 1e-10    | 72    |
| 0.0426     | 0.8817          | 0.7904    | 0.0434          | 0.8891               | 0.8015         | 1e-10    | 73    |
| 0.0424     | 0.8811          | 0.7897    | 0.0434          | 0.8899               | 0.8028         | 1e-10    | 74    |
| 0.0432     | 0.8807          | 0.7890    | 0.0430          | 0.8897               | 0.8025         | 1e-10    | 75    |
| 0.0423     | 0.8816          | 0.7904    | 0.0435          | 0.8894               | 0.8019         | 1e-10    | 76    |
| 0.0418     | 0.8838          | 0.7935    | 0.0431          | 0.8897               | 0.8025         | 1e-10    | 77    |
| 0.0425     | 0.8817          | 0.7901    | 0.0428          | 0.8898               | 0.8026         | 1e-10    | 78    |
| 0.0424     | 0.8818          | 0.7904    | 0.0434          | 0.8891               | 0.8015         | 1e-10    | 79    |
| 0.0419     | 0.8828          | 0.7920    | 0.0431          | 0.8901               | 0.8031         | 1e-10    | 80    |
| 0.0429     | 0.8812          | 0.7897    | 0.0425          | 0.8903               | 0.8034         | 1e-10    | 81    |
| 0.0419     | 0.8829          | 0.7922    | 0.0427          | 0.8905               | 0.8038         | 1e-10    | 82    |
| 0.0426     | 0.8820          | 0.7908    | 0.0431          | 0.8894               | 0.8019         | 1e-10    | 83    |
| 0.0424     | 0.8830          | 0.7921    | 0.0433          | 0.8893               | 0.8018         | 1e-10    | 84    |
| 0.0420     | 0.8832          | 0.7927    | 0.0432          | 0.8894               | 0.8019         | 1e-10    | 85    |
| 0.0421     | 0.8828          | 0.7921    | 0.0426          | 0.8907               | 0.8042         | 1e-10    | 86    |
| 0.0424     | 0.8817          | 0.7903    | 0.0430          | 0.8905               | 0.8038         | 1e-10    | 87    |
| 0.0423     | 0.8819          | 0.7908    | 0.0431          | 0.8901               | 0.8032         | 1e-10    | 88    |
| 0.0428     | 0.8809          | 0.7891    | 0.0429          | 0.8897               | 0.8025         | 1e-10    | 89    |
| 0.0424     | 0.8818          | 0.7903    | 0.0434          | 0.8897               | 0.8025         | 1e-10    | 90    |
| 0.0422     | 0.8827          | 0.7918    | 0.0428          | 0.8902               | 0.8033         | 1e-10    | 91    |
| 0.0426     | 0.8813          | 0.7897    | 0.0433          | 0.8891               | 0.8016         | 1e-10    | 92    |
| 0.0418     | 0.8839          | 0.7936    | 0.0427          | 0.8898               | 0.8026         | 1e-10    | 93    |
| 0.0418     | 0.8831          | 0.7924    | 0.0431          | 0.8900               | 0.8031         | 1e-10    | 94    |
| 0.0425     | 0.8822          | 0.7912    | 0.0429          | 0.8904               | 0.8037         | 1e-10    | 95    |
| 0.0424     | 0.8812          | 0.7895    | 0.0429          | 0.8896               | 0.8023         | 1e-10    | 96    |
| 0.0423     | 0.8818          | 0.7908    | 0.0428          | 0.8900               | 0.8028         | 1e-10    | 97    |
| 0.0417     | 0.8838          | 0.7934    | 0.0427          | 0.8906               | 0.8040         | 1e-10    | 98    |
| 0.0424     | 0.8817          | 0.7903    | 0.0436          | 0.8897               | 0.8024         | 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_RESIZE_NLM", "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\_NLM
==================================


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: 0.0424
* Train Dice Coef: 0.8817
* Train Iou: 0.7903
* Validation Loss: 0.0436
* Validation Dice Coef: 0.8897
* Validation Iou: 0.8024
* 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