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# segformer-b0-finetuned-fish-almogm

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: 0.0068
- eval_mean_iou: 0.4831
- eval_mean_accuracy: 1.0000
- eval_overall_accuracy: 1.0000
- eval_accuracy_background: 1.0000
- eval_accuracy_fish: nan
- eval_iou_background: 0.9662
- eval_iou_fish: 0.0
- eval_runtime: 62.449
- eval_samples_per_second: 0.801
- eval_steps_per_second: 0.4
- epoch: 6.46
- step: 640

## 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: 2
- eval_batch_size: 2
- 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 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
{"license": "other", "tags": ["image-segmentation"], "model-index": [{"name": "segformer-b0-finetuned-fish-almogm", "results": []}]}
transformers pytorch tensorboard segformer image-segmentation license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #license-other #endpoints_compatible #region-us 




# segformer-b0-finetuned-fish-almogm

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: 0.0068
- eval_mean_iou: 0.4831
- eval_mean_accuracy: 1.0000
- eval_overall_accuracy: 1.0000
- eval_accuracy_background: 1.0000
- eval_accuracy_fish: nan
- eval_iou_background: 0.9662
- eval_iou_fish: 0.0
- eval_runtime: 62.449
- eval_samples_per_second: 0.801
- eval_steps_per_second: 0.4
- epoch: 6.46
- step: 640

## 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: 2
- eval_batch_size: 2
- 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 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
# segformer-b0-finetuned-fish-almogm

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: 0.0068
- eval_mean_iou: 0.4831
- eval_mean_accuracy: 1.0000
- eval_overall_accuracy: 1.0000
- eval_accuracy_background: 1.0000
- eval_accuracy_fish: nan
- eval_iou_background: 0.9662
- eval_iou_fish: 0.0
- eval_runtime: 62.449
- eval_samples_per_second: 0.801
- eval_steps_per_second: 0.4
- epoch: 6.46
- step: 640 ## 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: 2
- eval_batch_size: 2
- 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 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #license-other #endpoints_compatible #region-us 
 # segformer-b0-finetuned-fish-almogm

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: 0.0068
- eval_mean_iou: 0.4831
- eval_mean_accuracy: 1.0000
- eval_overall_accuracy: 1.0000
- eval_accuracy_background: 1.0000
- eval_accuracy_fish: nan
- eval_iou_background: 0.9662
- eval_iou_fish: 0.0
- eval_runtime: 62.449
- eval_samples_per_second: 0.801
- eval_steps_per_second: 0.4
- epoch: 6.46
- step: 640 ## 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: 2
- eval_batch_size: 2
- 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 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2