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# AhamadShaik/SegFormer_RESIZE_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: 0.0520
- Train Dice Coef: 0.8435
- Train Iou: 0.7409
- Validation Loss: 0.0454
- Validation Dice Coef: 0.8836
- Validation Iou: 0.7926
- 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 |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.3496     | 0.3697          | 0.2435    | 0.2697          | 0.1141               | 0.0635         | 1e-04    | 0     |
| 0.1591     | 0.5600          | 0.4126    | 0.1768          | 0.3601               | 0.2345         | 1e-04    | 1     |
| 0.1295     | 0.6470          | 0.5014    | 0.1637          | 0.4628               | 0.3163         | 1e-04    | 2     |
| 0.1109     | 0.6903          | 0.5511    | 0.1319          | 0.5634               | 0.4072         | 1e-04    | 3     |
| 0.1018     | 0.7226          | 0.5858    | 0.0932          | 0.7480               | 0.6051         | 1e-04    | 4     |
| 0.0930     | 0.7373          | 0.6042    | 0.1618          | 0.5048               | 0.3614         | 1e-04    | 5     |
| 0.0878     | 0.7534          | 0.6255    | 0.1023          | 0.7076               | 0.5637         | 1e-04    | 6     |
| 0.0842     | 0.7585          | 0.6310    | 0.0878          | 0.7726               | 0.6384         | 1e-04    | 7     |
| 0.0798     | 0.7733          | 0.6475    | 0.0966          | 0.7434               | 0.5996         | 1e-04    | 8     |
| 0.0765     | 0.7716          | 0.6487    | 0.1073          | 0.7157               | 0.5657         | 1e-04    | 9     |
| 0.0701     | 0.7974          | 0.6794    | 0.1049          | 0.7190               | 0.5811         | 1e-04    | 10    |
| 0.0675     | 0.8020          | 0.6854    | 0.1319          | 0.6935               | 0.5427         | 1e-04    | 11    |
| 0.0662     | 0.8108          | 0.6957    | 0.1593          | 0.6269               | 0.4826         | 1e-04    | 12    |
| 0.0679     | 0.7980          | 0.6817    | 0.0483          | 0.8809               | 0.7881         | 5e-06    | 13    |
| 0.0613     | 0.8173          | 0.7069    | 0.0467          | 0.8827               | 0.7910         | 5e-06    | 14    |
| 0.0595     | 0.8160          | 0.7064    | 0.0475          | 0.8810               | 0.7883         | 5e-06    | 15    |
| 0.0589     | 0.8197          | 0.7115    | 0.0460          | 0.8835               | 0.7922         | 5e-06    | 16    |
| 0.0576     | 0.8214          | 0.7134    | 0.0459          | 0.8838               | 0.7927         | 5e-06    | 17    |
| 0.0577     | 0.8161          | 0.7082    | 0.0458          | 0.8838               | 0.7927         | 5e-06    | 18    |
| 0.0564     | 0.8310          | 0.7242    | 0.0461          | 0.8830               | 0.7915         | 5e-06    | 19    |
| 0.0567     | 0.8330          | 0.7271    | 0.0455          | 0.8828               | 0.7910         | 5e-06    | 20    |
| 0.0562     | 0.8252          | 0.7182    | 0.0455          | 0.8850               | 0.7947         | 5e-06    | 21    |
| 0.0560     | 0.8245          | 0.7188    | 0.0461          | 0.8830               | 0.7916         | 5e-06    | 22    |
| 0.0554     | 0.8259          | 0.7208    | 0.0463          | 0.8811               | 0.7885         | 5e-06    | 23    |
| 0.0548     | 0.8254          | 0.7212    | 0.0459          | 0.8832               | 0.7919         | 5e-06    | 24    |
| 0.0552     | 0.8331          | 0.7281    | 0.0452          | 0.8833               | 0.7920         | 5e-06    | 25    |
| 0.0534     | 0.8391          | 0.7355    | 0.0438          | 0.8872               | 0.7982         | 5e-06    | 26    |
| 0.0538     | 0.8350          | 0.7310    | 0.0447          | 0.8846               | 0.7941         | 5e-06    | 27    |
| 0.0543     | 0.8443          | 0.7406    | 0.0468          | 0.8803               | 0.7877         | 5e-06    | 28    |
| 0.0535     | 0.8350          | 0.7324    | 0.0459          | 0.8833               | 0.7919         | 5e-06    | 29    |
| 0.0529     | 0.8404          | 0.7376    | 0.0460          | 0.8820               | 0.7900         | 5e-06    | 30    |
| 0.0525     | 0.8396          | 0.7379    | 0.0444          | 0.8855               | 0.7954         | 5e-06    | 31    |
| 0.0525     | 0.8347          | 0.7322    | 0.0458          | 0.8833               | 0.7921         | 2.5e-07  | 32    |
| 0.0524     | 0.8414          | 0.7376    | 0.0453          | 0.8840               | 0.7930         | 2.5e-07  | 33    |
| 0.0524     | 0.8406          | 0.7372    | 0.0446          | 0.8842               | 0.7935         | 2.5e-07  | 34    |
| 0.0522     | 0.8408          | 0.7385    | 0.0456          | 0.8838               | 0.7927         | 2.5e-07  | 35    |
| 0.0521     | 0.8484          | 0.7454    | 0.0453          | 0.8839               | 0.7929         | 2.5e-07  | 36    |
| 0.0521     | 0.8503          | 0.7481    | 0.0459          | 0.8832               | 0.7919         | 1.25e-08 | 37    |
| 0.0521     | 0.8370          | 0.7344    | 0.0451          | 0.8845               | 0.7939         | 1.25e-08 | 38    |
| 0.0524     | 0.8484          | 0.7452    | 0.0456          | 0.8837               | 0.7927         | 1.25e-08 | 39    |
| 0.0529     | 0.8410          | 0.7388    | 0.0448          | 0.8848               | 0.7944         | 1.25e-08 | 40    |
| 0.0519     | 0.8402          | 0.7391    | 0.0444          | 0.8852               | 0.7951         | 1.25e-08 | 41    |
| 0.0518     | 0.8349          | 0.7331    | 0.0448          | 0.8850               | 0.7948         | 6.25e-10 | 42    |
| 0.0523     | 0.8406          | 0.7381    | 0.0452          | 0.8835               | 0.7922         | 6.25e-10 | 43    |
| 0.0519     | 0.8427          | 0.7402    | 0.0449          | 0.8854               | 0.7952         | 6.25e-10 | 44    |
| 0.0523     | 0.8445          | 0.7413    | 0.0453          | 0.8839               | 0.7930         | 6.25e-10 | 45    |
| 0.0519     | 0.8445          | 0.7434    | 0.0446          | 0.8858               | 0.7959         | 6.25e-10 | 46    |
| 0.0519     | 0.8388          | 0.7368    | 0.0447          | 0.8839               | 0.7929         | 1e-10    | 47    |
| 0.0518     | 0.8456          | 0.7438    | 0.0448          | 0.8847               | 0.7943         | 1e-10    | 48    |
| 0.0521     | 0.8447          | 0.7433    | 0.0442          | 0.8859               | 0.7961         | 1e-10    | 49    |
| 0.0520     | 0.8382          | 0.7359    | 0.0453          | 0.8838               | 0.7929         | 1e-10    | 50    |
| 0.0523     | 0.8469          | 0.7463    | 0.0448          | 0.8852               | 0.7951         | 1e-10    | 51    |
| 0.0515     | 0.8375          | 0.7362    | 0.0459          | 0.8825               | 0.7909         | 1e-10    | 52    |
| 0.0520     | 0.8447          | 0.7432    | 0.0443          | 0.8854               | 0.7954         | 1e-10    | 53    |
| 0.0523     | 0.8359          | 0.7337    | 0.0442          | 0.8860               | 0.7962         | 1e-10    | 54    |
| 0.0523     | 0.8352          | 0.7333    | 0.0440          | 0.8867               | 0.7974         | 1e-10    | 55    |
| 0.0523     | 0.8376          | 0.7347    | 0.0456          | 0.8846               | 0.7940         | 1e-10    | 56    |
| 0.0520     | 0.8466          | 0.7441    | 0.0448          | 0.8856               | 0.7956         | 1e-10    | 57    |
| 0.0524     | 0.8382          | 0.7357    | 0.0433          | 0.8875               | 0.7987         | 1e-10    | 58    |
| 0.0521     | 0.8431          | 0.7403    | 0.0450          | 0.8853               | 0.7951         | 1e-10    | 59    |
| 0.0524     | 0.8415          | 0.7389    | 0.0453          | 0.8846               | 0.7940         | 1e-10    | 60    |
| 0.0517     | 0.8436          | 0.7423    | 0.0444          | 0.8853               | 0.7951         | 1e-10    | 61    |
| 0.0523     | 0.8467          | 0.7443    | 0.0455          | 0.8840               | 0.7932         | 1e-10    | 62    |
| 0.0522     | 0.8470          | 0.7434    | 0.0445          | 0.8859               | 0.7961         | 1e-10    | 63    |
| 0.0520     | 0.8375          | 0.7356    | 0.0446          | 0.8857               | 0.7958         | 1e-10    | 64    |
| 0.0515     | 0.8416          | 0.7396    | 0.0440          | 0.8862               | 0.7966         | 1e-10    | 65    |
| 0.0526     | 0.8364          | 0.7346    | 0.0449          | 0.8848               | 0.7944         | 1e-10    | 66    |
| 0.0524     | 0.8461          | 0.7438    | 0.0452          | 0.8838               | 0.7928         | 1e-10    | 67    |
| 0.0523     | 0.8374          | 0.7361    | 0.0453          | 0.8849               | 0.7947         | 1e-10    | 68    |
| 0.0520     | 0.8370          | 0.7355    | 0.0446          | 0.8852               | 0.7950         | 1e-10    | 69    |
| 0.0522     | 0.8487          | 0.7473    | 0.0455          | 0.8835               | 0.7923         | 1e-10    | 70    |
| 0.0520     | 0.8446          | 0.7429    | 0.0463          | 0.8828               | 0.7911         | 1e-10    | 71    |
| 0.0520     | 0.8364          | 0.7345    | 0.0454          | 0.8841               | 0.7933         | 1e-10    | 72    |
| 0.0528     | 0.8468          | 0.7431    | 0.0452          | 0.8846               | 0.7939         | 1e-10    | 73    |
| 0.0518     | 0.8455          | 0.7441    | 0.0449          | 0.8846               | 0.7940         | 1e-10    | 74    |
| 0.0519     | 0.8351          | 0.7330    | 0.0445          | 0.8852               | 0.7948         | 1e-10    | 75    |
| 0.0521     | 0.8423          | 0.7406    | 0.0453          | 0.8849               | 0.7945         | 1e-10    | 76    |
| 0.0525     | 0.8467          | 0.7449    | 0.0456          | 0.8836               | 0.7925         | 1e-10    | 77    |
| 0.0522     | 0.8436          | 0.7397    | 0.0445          | 0.8847               | 0.7942         | 1e-10    | 78    |
| 0.0521     | 0.8423          | 0.7393    | 0.0443          | 0.8855               | 0.7954         | 1e-10    | 79    |
| 0.0513     | 0.8439          | 0.7415    | 0.0454          | 0.8837               | 0.7926         | 1e-10    | 80    |
| 0.0520     | 0.8433          | 0.7422    | 0.0445          | 0.8843               | 0.7937         | 1e-10    | 81    |
| 0.0522     | 0.8417          | 0.7396    | 0.0451          | 0.8844               | 0.7939         | 1e-10    | 82    |
| 0.0520     | 0.8492          | 0.7471    | 0.0449          | 0.8847               | 0.7943         | 1e-10    | 83    |
| 0.0526     | 0.8384          | 0.7360    | 0.0445          | 0.8862               | 0.7968         | 1e-10    | 84    |
| 0.0520     | 0.8477          | 0.7457    | 0.0447          | 0.8844               | 0.7938         | 1e-10    | 85    |
| 0.0518     | 0.8410          | 0.7387    | 0.0452          | 0.8848               | 0.7944         | 1e-10    | 86    |
| 0.0523     | 0.8443          | 0.7421    | 0.0443          | 0.8865               | 0.7971         | 1e-10    | 87    |
| 0.0519     | 0.8429          | 0.7402    | 0.0465          | 0.8813               | 0.7891         | 1e-10    | 88    |
| 0.0526     | 0.8328          | 0.7291    | 0.0446          | 0.8853               | 0.7952         | 1e-10    | 89    |
| 0.0528     | 0.8435          | 0.7408    | 0.0449          | 0.8855               | 0.7954         | 1e-10    | 90    |
| 0.0521     | 0.8417          | 0.7399    | 0.0441          | 0.8859               | 0.7961         | 1e-10    | 91    |
| 0.0516     | 0.8430          | 0.7422    | 0.0455          | 0.8830               | 0.7915         | 1e-10    | 92    |
| 0.0523     | 0.8499          | 0.7491    | 0.0457          | 0.8832               | 0.7919         | 1e-10    | 93    |
| 0.0525     | 0.8399          | 0.7371    | 0.0452          | 0.8846               | 0.7940         | 1e-10    | 94    |
| 0.0517     | 0.8433          | 0.7401    | 0.0455          | 0.8835               | 0.7924         | 1e-10    | 95    |
| 0.0524     | 0.8417          | 0.7398    | 0.0453          | 0.8831               | 0.7917         | 1e-10    | 96    |
| 0.0517     | 0.8410          | 0.7390    | 0.0440          | 0.8870               | 0.7979         | 1e-10    | 97    |
| 0.0525     | 0.8394          | 0.7373    | 0.0451          | 0.8846               | 0.7941         | 1e-10    | 98    |
| 0.0520     | 0.8435          | 0.7409    | 0.0454          | 0.8836               | 0.7926         | 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_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\_RESIZE\_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: 0.0520
* Train Dice Coef: 0.8435
* Train Iou: 0.7409
* Validation Loss: 0.0454
* Validation Dice Coef: 0.8836
* Validation Iou: 0.7926
* 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