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# AhamadShaik/SegFormer_PADDING_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: 0.0161
- Train Dice Coef: 0.8042
- Train Iou: 0.6813
- Validation Loss: 0.0228
- Validation Dice Coef: 0.8559
- Validation Iou: 0.7502
- 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 |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.2695     | 0.2707          | 0.1687    | 0.1167          | 0.5713               | 0.4080         | 1e-04    | 0     |
| 0.0805     | 0.4647          | 0.3202    | 0.0690          | 0.6569               | 0.4990         | 1e-04    | 1     |
| 0.0582     | 0.5595          | 0.4069    | 0.0509          | 0.7745               | 0.6369         | 1e-04    | 2     |
| 0.0507     | 0.5971          | 0.4430    | 0.0459          | 0.7780               | 0.6420         | 1e-04    | 3     |
| 0.0443     | 0.6379          | 0.4837    | 0.0393          | 0.8008               | 0.6709         | 1e-04    | 4     |
| 0.0417     | 0.6535          | 0.5011    | 0.0363          | 0.8131               | 0.6882         | 1e-04    | 5     |
| 0.0384     | 0.6728          | 0.5223    | 0.0355          | 0.8240               | 0.7031         | 1e-04    | 6     |
| 0.0340     | 0.7019          | 0.5531    | 0.0327          | 0.8297               | 0.7113         | 1e-04    | 7     |
| 0.0339     | 0.7002          | 0.5530    | 0.0319          | 0.8344               | 0.7182         | 1e-04    | 8     |
| 0.0315     | 0.7137          | 0.5680    | 0.0307          | 0.8333               | 0.7169         | 1e-04    | 9     |
| 0.0292     | 0.7341          | 0.5908    | 0.0296          | 0.8400               | 0.7266         | 1e-04    | 10    |
| 0.0285     | 0.7321          | 0.5905    | 0.0294          | 0.8412               | 0.7279         | 1e-04    | 11    |
| 0.0287     | 0.7323          | 0.5901    | 0.0310          | 0.8338               | 0.7185         | 1e-04    | 12    |
| 0.0283     | 0.7361          | 0.5943    | 0.0285          | 0.8457               | 0.7351         | 1e-04    | 13    |
| 0.0254     | 0.7503          | 0.6120    | 0.0265          | 0.8474               | 0.7376         | 1e-04    | 14    |
| 0.0239     | 0.7623          | 0.6271    | 0.0262          | 0.8483               | 0.7393         | 1e-04    | 15    |
| 0.0237     | 0.7589          | 0.6231    | 0.0286          | 0.8425               | 0.7311         | 1e-04    | 16    |
| 0.0227     | 0.7682          | 0.6339    | 0.0264          | 0.8467               | 0.7368         | 1e-04    | 17    |
| 0.0236     | 0.7639          | 0.6281    | 0.0273          | 0.8450               | 0.7356         | 1e-04    | 18    |
| 0.0221     | 0.7701          | 0.6359    | 0.0251          | 0.8491               | 0.7411         | 1e-04    | 19    |
| 0.0235     | 0.7619          | 0.6262    | 0.0264          | 0.8482               | 0.7394         | 1e-04    | 20    |
| 0.0216     | 0.7718          | 0.6383    | 0.0257          | 0.8506               | 0.7425         | 1e-04    | 21    |
| 0.0222     | 0.7715          | 0.6379    | 0.0242          | 0.8543               | 0.7479         | 1e-04    | 22    |
| 0.0203     | 0.7810          | 0.6500    | 0.0239          | 0.8556               | 0.7499         | 1e-04    | 23    |
| 0.0196     | 0.7853          | 0.6556    | 0.0241          | 0.8552               | 0.7494         | 1e-04    | 24    |
| 0.0197     | 0.7813          | 0.6512    | 0.0236          | 0.8529               | 0.7464         | 1e-04    | 25    |
| 0.0191     | 0.7890          | 0.6608    | 0.0239          | 0.8574               | 0.7525         | 1e-04    | 26    |
| 0.0180     | 0.7930          | 0.6662    | 0.0236          | 0.8574               | 0.7524         | 1e-04    | 27    |
| 0.0179     | 0.7973          | 0.6709    | 0.0247          | 0.8559               | 0.7504         | 1e-04    | 28    |
| 0.0176     | 0.7959          | 0.6702    | 0.0245          | 0.8567               | 0.7515         | 1e-04    | 29    |
| 0.0225     | 0.7712          | 0.6380    | 0.0245          | 0.8496               | 0.7413         | 1e-04    | 30    |
| 0.0190     | 0.7867          | 0.6579    | 0.0232          | 0.8546               | 0.7486         | 5e-06    | 31    |
| 0.0178     | 0.7963          | 0.6701    | 0.0239          | 0.8520               | 0.7450         | 5e-06    | 32    |
| 0.0175     | 0.7964          | 0.6703    | 0.0232          | 0.8547               | 0.7489         | 5e-06    | 33    |
| 0.0173     | 0.7972          | 0.6719    | 0.0225          | 0.8556               | 0.7499         | 5e-06    | 34    |
| 0.0168     | 0.8006          | 0.6759    | 0.0232          | 0.8561               | 0.7507         | 5e-06    | 35    |
| 0.0167     | 0.7984          | 0.6739    | 0.0232          | 0.8543               | 0.7480         | 5e-06    | 36    |
| 0.0167     | 0.8025          | 0.6786    | 0.0226          | 0.8561               | 0.7507         | 5e-06    | 37    |
| 0.0163     | 0.8027          | 0.6791    | 0.0231          | 0.8560               | 0.7505         | 5e-06    | 38    |
| 0.0164     | 0.7995          | 0.6753    | 0.0225          | 0.8568               | 0.7516         | 5e-06    | 39    |
| 0.0160     | 0.8077          | 0.6853    | 0.0229          | 0.8559               | 0.7503         | 2.5e-07  | 40    |
| 0.0160     | 0.8070          | 0.6843    | 0.0227          | 0.8567               | 0.7514         | 2.5e-07  | 41    |
| 0.0160     | 0.8072          | 0.6844    | 0.0232          | 0.8564               | 0.7509         | 2.5e-07  | 42    |
| 0.0164     | 0.8037          | 0.6801    | 0.0219          | 0.8573               | 0.7525         | 2.5e-07  | 43    |
| 0.0161     | 0.8022          | 0.6786    | 0.0233          | 0.8548               | 0.7487         | 2.5e-07  | 44    |
| 0.0160     | 0.8041          | 0.6809    | 0.0226          | 0.8571               | 0.7522         | 2.5e-07  | 45    |
| 0.0161     | 0.8043          | 0.6810    | 0.0228          | 0.8558               | 0.7501         | 2.5e-07  | 46    |
| 0.0167     | 0.8035          | 0.6807    | 0.0226          | 0.8564               | 0.7510         | 2.5e-07  | 47    |
| 0.0161     | 0.8047          | 0.6818    | 0.0224          | 0.8565               | 0.7512         | 2.5e-07  | 48    |
| 0.0161     | 0.8041          | 0.6811    | 0.0225          | 0.8576               | 0.7528         | 1.25e-08 | 49    |
| 0.0162     | 0.8060          | 0.6832    | 0.0233          | 0.8555               | 0.7498         | 1.25e-08 | 50    |
| 0.0161     | 0.8029          | 0.6802    | 0.0227          | 0.8570               | 0.7519         | 1.25e-08 | 51    |
| 0.0160     | 0.8069          | 0.6843    | 0.0231          | 0.8556               | 0.7498         | 1.25e-08 | 52    |
| 0.0158     | 0.8074          | 0.6853    | 0.0230          | 0.8567               | 0.7514         | 1.25e-08 | 53    |
| 0.0160     | 0.8050          | 0.6821    | 0.0229          | 0.8563               | 0.7509         | 6.25e-10 | 54    |
| 0.0159     | 0.8057          | 0.6834    | 0.0230          | 0.8558               | 0.7501         | 6.25e-10 | 55    |
| 0.0160     | 0.8045          | 0.6811    | 0.0226          | 0.8574               | 0.7525         | 6.25e-10 | 56    |
| 0.0161     | 0.8061          | 0.6828    | 0.0226          | 0.8560               | 0.7506         | 6.25e-10 | 57    |
| 0.0160     | 0.8062          | 0.6833    | 0.0231          | 0.8560               | 0.7505         | 6.25e-10 | 58    |
| 0.0159     | 0.8037          | 0.6810    | 0.0227          | 0.8557               | 0.7500         | 1e-10    | 59    |
| 0.0159     | 0.8085          | 0.6859    | 0.0223          | 0.8572               | 0.7522         | 1e-10    | 60    |
| 0.0159     | 0.8046          | 0.6815    | 0.0223          | 0.8578               | 0.7533         | 1e-10    | 61    |
| 0.0161     | 0.8057          | 0.6830    | 0.0229          | 0.8561               | 0.7507         | 1e-10    | 62    |
| 0.0160     | 0.8046          | 0.6817    | 0.0230          | 0.8566               | 0.7514         | 1e-10    | 63    |
| 0.0160     | 0.8038          | 0.6812    | 0.0225          | 0.8567               | 0.7514         | 1e-10    | 64    |
| 0.0159     | 0.8084          | 0.6865    | 0.0225          | 0.8561               | 0.7508         | 1e-10    | 65    |
| 0.0161     | 0.8030          | 0.6795    | 0.0226          | 0.8570               | 0.7520         | 1e-10    | 66    |
| 0.0163     | 0.8046          | 0.6813    | 0.0223          | 0.8579               | 0.7534         | 1e-10    | 67    |
| 0.0161     | 0.8047          | 0.6812    | 0.0226          | 0.8566               | 0.7513         | 1e-10    | 68    |
| 0.0161     | 0.8047          | 0.6813    | 0.0230          | 0.8564               | 0.7510         | 1e-10    | 69    |
| 0.0164     | 0.8049          | 0.6820    | 0.0231          | 0.8553               | 0.7493         | 1e-10    | 70    |
| 0.0160     | 0.8041          | 0.6812    | 0.0227          | 0.8574               | 0.7525         | 1e-10    | 71    |
| 0.0161     | 0.8074          | 0.6852    | 0.0222          | 0.8575               | 0.7527         | 1e-10    | 72    |
| 0.0161     | 0.7989          | 0.6748    | 0.0228          | 0.8563               | 0.7509         | 1e-10    | 73    |
| 0.0161     | 0.8009          | 0.6772    | 0.0224          | 0.8569               | 0.7518         | 1e-10    | 74    |
| 0.0160     | 0.8039          | 0.6803    | 0.0228          | 0.8561               | 0.7506         | 1e-10    | 75    |
| 0.0160     | 0.8049          | 0.6823    | 0.0222          | 0.8578               | 0.7532         | 1e-10    | 76    |
| 0.0159     | 0.8053          | 0.6830    | 0.0224          | 0.8565               | 0.7513         | 1e-10    | 77    |
| 0.0161     | 0.8060          | 0.6825    | 0.0225          | 0.8574               | 0.7525         | 1e-10    | 78    |
| 0.0159     | 0.8051          | 0.6825    | 0.0227          | 0.8561               | 0.7506         | 1e-10    | 79    |
| 0.0161     | 0.8034          | 0.6798    | 0.0227          | 0.8559               | 0.7504         | 1e-10    | 80    |
| 0.0163     | 0.8076          | 0.6850    | 0.0230          | 0.8569               | 0.7517         | 1e-10    | 81    |
| 0.0159     | 0.8068          | 0.6840    | 0.0234          | 0.8562               | 0.7506         | 1e-10    | 82    |
| 0.0161     | 0.8047          | 0.6818    | 0.0228          | 0.8562               | 0.7508         | 1e-10    | 83    |
| 0.0162     | 0.8036          | 0.6802    | 0.0224          | 0.8563               | 0.7509         | 1e-10    | 84    |
| 0.0160     | 0.8045          | 0.6816    | 0.0230          | 0.8566               | 0.7514         | 1e-10    | 85    |
| 0.0161     | 0.8026          | 0.6794    | 0.0233          | 0.8562               | 0.7508         | 1e-10    | 86    |
| 0.0161     | 0.8036          | 0.6806    | 0.0228          | 0.8562               | 0.7507         | 1e-10    | 87    |
| 0.0160     | 0.8026          | 0.6791    | 0.0230          | 0.8556               | 0.7497         | 1e-10    | 88    |
| 0.0164     | 0.8041          | 0.6809    | 0.0227          | 0.8564               | 0.7509         | 1e-10    | 89    |
| 0.0163     | 0.8029          | 0.6797    | 0.0232          | 0.8552               | 0.7493         | 1e-10    | 90    |
| 0.0161     | 0.8061          | 0.6833    | 0.0231          | 0.8553               | 0.7495         | 1e-10    | 91    |
| 0.0160     | 0.8043          | 0.6814    | 0.0222          | 0.8577               | 0.7530         | 1e-10    | 92    |
| 0.0159     | 0.8058          | 0.6833    | 0.0223          | 0.8560               | 0.7505         | 1e-10    | 93    |
| 0.0161     | 0.8032          | 0.6796    | 0.0223          | 0.8566               | 0.7514         | 1e-10    | 94    |
| 0.0162     | 0.8037          | 0.6804    | 0.0228          | 0.8558               | 0.7502         | 1e-10    | 95    |
| 0.0160     | 0.8037          | 0.6805    | 0.0226          | 0.8568               | 0.7517         | 1e-10    | 96    |
| 0.0160     | 0.8059          | 0.6835    | 0.0223          | 0.8579               | 0.7533         | 1e-10    | 97    |
| 0.0160     | 0.8055          | 0.6822    | 0.0229          | 0.8552               | 0.7492         | 1e-10    | 98    |
| 0.0161     | 0.8042          | 0.6813    | 0.0228          | 0.8559               | 0.7502         | 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_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\_PADDING\_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: 0.0161
* Train Dice Coef: 0.8042
* Train Iou: 0.6813
* Validation Loss: 0.0228
* Validation Dice Coef: 0.8559
* Validation Iou: 0.7502
* 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