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# deprem_satellite_semantic_whu

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0692
- eval_mean_iou: 0.8739
- eval_mean_accuracy: 0.9277
- eval_overall_accuracy: 0.9786
- eval_accuracy_background: 0.9888
- eval_accuracy_building: 0.8665
- eval_iou_background: 0.9770
- eval_iou_building: 0.7708
- eval_runtime: 124.6705
- eval_samples_per_second: 4.011
- eval_steps_per_second: 4.011
- step: 0

## 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:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "deprem_satellite_semantic_whu", "results": []}]}
transformers pytorch tensorboard segformer generated_from_trainer endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #endpoints_compatible #has_space #region-us 




# deprem_satellite_semantic_whu

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0692
- eval_mean_iou: 0.8739
- eval_mean_accuracy: 0.9277
- eval_overall_accuracy: 0.9786
- eval_accuracy_background: 0.9888
- eval_accuracy_building: 0.8665
- eval_iou_background: 0.9770
- eval_iou_building: 0.7708
- eval_runtime: 124.6705
- eval_samples_per_second: 4.011
- eval_steps_per_second: 4.011
- step: 0

## 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:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# deprem_satellite_semantic_whu

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0692
- eval_mean_iou: 0.8739
- eval_mean_accuracy: 0.9277
- eval_overall_accuracy: 0.9786
- eval_accuracy_background: 0.9888
- eval_accuracy_building: 0.8665
- eval_iou_background: 0.9770
- eval_iou_building: 0.7708
- eval_runtime: 124.6705
- eval_samples_per_second: 4.011
- eval_steps_per_second: 4.011
- step: 0 ## 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:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #endpoints_compatible #has_space #region-us 
 # deprem_satellite_semantic_whu

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0692
- eval_mean_iou: 0.8739
- eval_mean_accuracy: 0.9277
- eval_overall_accuracy: 0.9786
- eval_accuracy_background: 0.9888
- eval_accuracy_building: 0.8665
- eval_iou_background: 0.9770
- eval_iou_building: 0.7708
- eval_runtime: 124.6705
- eval_samples_per_second: 4.011
- eval_steps_per_second: 4.011
- step: 0 ## 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:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #endpoints_compatible #has_space #region-us 
# deprem_satellite_semantic_whu

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0692
- eval_mean_iou: 0.8739
- eval_mean_accuracy: 0.9277
- eval_overall_accuracy: 0.9786
- eval_accuracy_background: 0.9888
- eval_accuracy_building: 0.8665
- eval_iou_background: 0.9770
- eval_iou_building: 0.7708
- eval_runtime: 124.6705
- eval_samples_per_second: 4.011
- eval_steps_per_second: 4.011
- step: 0## 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:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2