readme: ---
  license: other
  tags:
  - vision
  - image-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b5-finetuned-all_pooled1
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # segformer-b5-finetuned-all_pooled1
  
  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locations_pooled dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0254
  - Mean Iou: 0.3898
  - Mean Accuracy: 0.7796
  - Overall Accuracy: 0.7796
  - Accuracy Unlabeled: nan
  - Accuracy Trash: 0.7796
  - Iou Unlabeled: 0.0
  - Iou Trash: 0.7796
  
  ## 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: 5e-05
  - train_batch_size: 3
  - eval_batch_size: 3
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Iou Unlabeled | Iou Trash |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
  | 0.0466        | 1.0   | 80   | 0.0565          | 0.3515   | 0.7031        | 0.7031           | nan                | 0.7031         | 0.0           | 0.7031    |
  | 0.0255        | 2.0   | 160  | 0.0366          | 0.3387   | 0.6773        | 0.6773           | nan                | 0.6773         | 0.0           | 0.6773    |
  | 0.0275        | 3.0   | 240  | 0.0306          | 0.3967   | 0.7935        | 0.7935           | nan                | 0.7935         | 0.0           | 0.7935    |
  | 0.0286        | 4.0   | 320  | 0.0281          | 0.3839   | 0.7678        | 0.7678           | nan                | 0.7678         | 0.0           | 0.7678    |
  | 0.0308        | 5.0   | 400  | 0.0277          | 0.4000   | 0.8000        | 0.8000           | nan                | 0.8000         | 0.0           | 0.8000    |
  | 0.0138        | 6.0   | 480  | 0.0259          | 0.3873   | 0.7745        | 0.7745           | nan                | 0.7745         | 0.0           | 0.7745    |
  | 0.0333        | 7.0   | 560  | 0.0269          | 0.4046   | 0.8093        | 0.8093           | nan                | 0.8093         | 0.0           | 0.8093    |
  | 0.0046        | 8.0   | 640  | 0.0261          | 0.3677   | 0.7354        | 0.7354           | nan                | 0.7354         | 0.0           | 0.7354    |
  | 0.0092        | 9.0   | 720  | 0.0258          | 0.3960   | 0.7919        | 0.7919           | nan                | 0.7919         | 0.0           | 0.7919    |
  | 0.0184        | 10.0  | 800  | 0.0254          | 0.3898   | 0.7796        | 0.7796           | nan                | 0.7796         | 0.0           | 0.7796    |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  