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# dousey/scene_segmentation

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: nan
- Validation Loss: nan
- Validation Mean Iou: 0.0217
- Validation Mean Accuracy: 0.5
- Validation Overall Accuracy: 0.2545
- Validation Accuracy Background: 1.0
- Validation Accuracy Bleuet: 0.0
- Validation Accuracy Comptonie: nan
- Validation Accuracy Kalmia: nan
- Validation Iou Background: 0.0433
- Validation Iou Bleuet: 0.0
- Validation Iou Comptonie: nan
- Validation Iou Kalmia: nan
- Epoch: 1

## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 76500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Background | Validation Accuracy Bleuet | Validation Accuracy Comptonie | Validation Accuracy Kalmia | Validation Iou Background | Validation Iou Bleuet | Validation Iou Comptonie | Validation Iou Kalmia | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:-----------------------------:|:--------------------------:|:-------------------------:|:---------------------:|:------------------------:|:---------------------:|:-----:|
| nan        | nan             | 0.0217              | 0.5                      | 0.2545                      | 1.0                            | 0.0                        | nan                           | nan                        | 0.0433                    | 0.0                   | nan                      | nan                   | 0     |
| nan        | nan             | 0.0217              | 0.5                      | 0.2545                      | 1.0                            | 0.0                        | nan                           | nan                        | 0.0433                    | 0.0                   | nan                      | nan                   | 1     |


### Framework versions

- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dousey/scene_segmentation", "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 

dousey/scene\_segmentation
==========================


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: nan
* Validation Loss: nan
* Validation Mean Iou: 0.0217
* Validation Mean Accuracy: 0.5
* Validation Overall Accuracy: 0.2545
* Validation Accuracy Background: 1.0
* Validation Accuracy Bleuet: 0.0
* Validation Accuracy Comptonie: nan
* Validation Accuracy Kalmia: nan
* Validation Iou Background: 0.0433
* Validation Iou Bleuet: 0.0
* Validation Iou Comptonie: nan
* Validation Iou Kalmia: nan
* Epoch: 1


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': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 6e-05, 'decay\_steps': 76500, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 6e-05, 'decay\_steps': 76500, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* 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': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 6e-05, 'decay\_steps': 76500, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* 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': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 6e-05, 'decay\_steps': 76500, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2