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# segment_50ep

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0867
- eval_mean_iou: 0.8941
- eval_mean_accuracy: 0.9459
- eval_overall_accuracy: 0.9728
- eval_per_category_iou: [0.8914159628180123, 0.9397057910334902, 0.784713695838044, 0.9606094621573129]
- eval_per_category_accuracy: [0.9685998627316403, 0.9696767617484154, 0.8661740631737143, 0.9789942690602516]
- eval_runtime: 40.9902
- eval_samples_per_second: 0.976
- eval_steps_per_second: 0.244
- epoch: 36.82
- step: 3240

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segment_50ep", "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 




# segment_50ep

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0867
- eval_mean_iou: 0.8941
- eval_mean_accuracy: 0.9459
- eval_overall_accuracy: 0.9728
- eval_per_category_iou: [0.8914159628180123, 0.9397057910334902, 0.784713695838044, 0.9606094621573129]
- eval_per_category_accuracy: [0.9685998627316403, 0.9696767617484154, 0.8661740631737143, 0.9789942690602516]
- eval_runtime: 40.9902
- eval_samples_per_second: 0.976
- eval_steps_per_second: 0.244
- epoch: 36.82
- step: 3240

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2

# segment_50ep

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0867
- eval_mean_iou: 0.8941
- eval_mean_accuracy: 0.9459
- eval_overall_accuracy: 0.9728
- eval_per_category_iou: [0.8914159628180123, 0.9397057910334902, 0.784713695838044, 0.9606094621573129]
- eval_per_category_accuracy: [0.9685998627316403, 0.9696767617484154, 0.8661740631737143, 0.9789942690602516]
- eval_runtime: 40.9902
- eval_samples_per_second: 0.976
- eval_steps_per_second: 0.244
- epoch: 36.82
- step: 3240 ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 
 # segment_50ep

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0867
- eval_mean_iou: 0.8941
- eval_mean_accuracy: 0.9459
- eval_overall_accuracy: 0.9728
- eval_per_category_iou: [0.8914159628180123, 0.9397057910334902, 0.784713695838044, 0.9606094621573129]
- eval_per_category_accuracy: [0.9685998627316403, 0.9696767617484154, 0.8661740631737143, 0.9789942690602516]
- eval_runtime: 40.9902
- eval_samples_per_second: 0.976
- eval_steps_per_second: 0.244
- epoch: 36.82
- step: 3240 ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 
# segment_50ep

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0867
- eval_mean_iou: 0.8941
- eval_mean_accuracy: 0.9459
- eval_overall_accuracy: 0.9728
- eval_per_category_iou: [0.8914159628180123, 0.9397057910334902, 0.784713695838044, 0.9606094621573129]
- eval_per_category_accuracy: [0.9685998627316403, 0.9696767617484154, 0.8661740631737143, 0.9789942690602516]
- eval_runtime: 40.9902
- eval_samples_per_second: 0.976
- eval_steps_per_second: 0.244
- epoch: 36.82
- step: 3240## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2