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# deprem_satellite_semantic_xview2_large_2

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9892
- eval_mean_iou: 0.3093
- eval_mean_accuracy: 0.3576
- eval_overall_accuracy: 0.9646
- eval_accuracy_background: 0.9860
- eval_accuracy_nodamage: 0.8022
- eval_accuracy_minordamaged: 0.0
- eval_accuracy_majordamaged: 0.0
- eval_accuracy_destroyed: 0.0
- eval_iou_background: 0.9677
- eval_iou_nodamage: 0.5789
- eval_iou_minordamaged: 0.0
- eval_iou_majordamaged: 0.0
- eval_iou_destroyed: 0.0
- eval_runtime: 200.8095
- eval_samples_per_second: 4.646
- eval_steps_per_second: 4.646
- epoch: 19.3
- step: 27000

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Framework versions

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

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "deprem_satellite_semantic_xview2_large_2", "results": []}]}
transformers pytorch tensorboard segformer generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 




# deprem_satellite_semantic_xview2_large_2

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9892
- eval_mean_iou: 0.3093
- eval_mean_accuracy: 0.3576
- eval_overall_accuracy: 0.9646
- eval_accuracy_background: 0.9860
- eval_accuracy_nodamage: 0.8022
- eval_accuracy_minordamaged: 0.0
- eval_accuracy_majordamaged: 0.0
- eval_accuracy_destroyed: 0.0
- eval_iou_background: 0.9677
- eval_iou_nodamage: 0.5789
- eval_iou_minordamaged: 0.0
- eval_iou_majordamaged: 0.0
- eval_iou_destroyed: 0.0
- eval_runtime: 200.8095
- eval_samples_per_second: 4.646
- eval_steps_per_second: 4.646
- epoch: 19.3
- step: 27000

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Framework versions

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

# deprem_satellite_semantic_xview2_large_2

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9892
- eval_mean_iou: 0.3093
- eval_mean_accuracy: 0.3576
- eval_overall_accuracy: 0.9646
- eval_accuracy_background: 0.9860
- eval_accuracy_nodamage: 0.8022
- eval_accuracy_minordamaged: 0.0
- eval_accuracy_majordamaged: 0.0
- eval_accuracy_destroyed: 0.0
- eval_iou_background: 0.9677
- eval_iou_nodamage: 0.5789
- eval_iou_minordamaged: 0.0
- eval_iou_majordamaged: 0.0
- eval_iou_destroyed: 0.0
- eval_runtime: 200.8095
- eval_samples_per_second: 4.646
- eval_steps_per_second: 4.646
- epoch: 19.3
- step: 27000 ## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25 ### 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 #license-other #endpoints_compatible #region-us 
 # deprem_satellite_semantic_xview2_large_2

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9892
- eval_mean_iou: 0.3093
- eval_mean_accuracy: 0.3576
- eval_overall_accuracy: 0.9646
- eval_accuracy_background: 0.9860
- eval_accuracy_nodamage: 0.8022
- eval_accuracy_minordamaged: 0.0
- eval_accuracy_majordamaged: 0.0
- eval_accuracy_destroyed: 0.0
- eval_iou_background: 0.9677
- eval_iou_nodamage: 0.5789
- eval_iou_minordamaged: 0.0
- eval_iou_majordamaged: 0.0
- eval_iou_destroyed: 0.0
- eval_runtime: 200.8095
- eval_samples_per_second: 4.646
- eval_steps_per_second: 4.646
- epoch: 19.3
- step: 27000 ## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25 ### 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 #license-other #endpoints_compatible #region-us 
# deprem_satellite_semantic_xview2_large_2

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9892
- eval_mean_iou: 0.3093
- eval_mean_accuracy: 0.3576
- eval_overall_accuracy: 0.9646
- eval_accuracy_background: 0.9860
- eval_accuracy_nodamage: 0.8022
- eval_accuracy_minordamaged: 0.0
- eval_accuracy_majordamaged: 0.0
- eval_accuracy_destroyed: 0.0
- eval_iou_background: 0.9677
- eval_iou_nodamage: 0.5789
- eval_iou_minordamaged: 0.0
- eval_iou_majordamaged: 0.0
- eval_iou_destroyed: 0.0
- eval_runtime: 200.8095
- eval_samples_per_second: 4.646
- eval_steps_per_second: 4.646
- epoch: 19.3
- step: 27000## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25### Framework versions

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