readme: ---
  license: other
  tags:
  - vision
  - image-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b5-finetuned-segments-trash-3
    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-segments-trash-3
  
  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/excluding_ebo_town dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0709
  - Mean Iou: 0.1946
  - Mean Accuracy: 0.2606
  - Overall Accuracy: 0.7329
  - Accuracy Unlabeled: nan
  - Accuracy Trash: 0.7819
  - Accuracy Litter: 0.0
  - Accuracy Building: nan
  - Accuracy Vegetation: nan
  - Accuracy Burnpit: 0.0
  - Accuracy Construction: nan
  - Iou Unlabeled: 0.0
  - Iou Trash: 0.7785
  - Iou Litter: 0.0
  - Iou Building: nan
  - Iou Vegetation: nan
  - Iou Burnpit: 0.0
  - Iou Construction: nan
  
  ## 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Accuracy Litter | Accuracy Building | Accuracy Vegetation | Accuracy Burnpit | Accuracy Construction | Iou Unlabeled | Iou Trash | Iou Litter | Iou Building | Iou Vegetation | Iou Burnpit | Iou Construction |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:---------------:|:-----------------:|:-------------------:|:----------------:|:---------------------:|:-------------:|:---------:|:----------:|:------------:|:--------------:|:-----------:|:----------------:|
  | 1.1204        | 0.25  | 20   | 0.8933          | 0.1434   | 0.2938        | 0.8162           | nan                | 0.8702         | 0.0             | nan               | nan                 | 0.0111           | nan                   | 0.0           | 0.8492    | 0.0        | 0.0          | 0.0            | 0.0111      | nan              |
  | 0.5544        | 0.5   | 40   | 0.5804          | 0.2058   | 0.2808        | 0.7897           | nan                | 0.8425         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.8230    | 0.0        | nan          | nan            | 0.0         | nan              |
  | 0.3754        | 0.75  | 60   | 0.2758          | 0.0696   | 0.1161        | 0.3264           | nan                | 0.3482         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.3482    | 0.0        | 0.0          | nan            | 0.0         | nan              |
  | 0.1975        | 1.0   | 80   | 0.1908          | 0.1728   | 0.2316        | 0.6511           | nan                | 0.6947         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.6913    | 0.0        | nan          | nan            | 0.0         | nan              |
  | 0.1308        | 1.25  | 100  | 0.1413          | 0.1771   | 0.2367        | 0.6655           | nan                | 0.7100         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.7084    | 0.0        | nan          | nan            | 0.0         | nan              |
  | 0.1846        | 1.5   | 120  | 0.1205          | 0.1940   | 0.2598        | 0.7306           | nan                | 0.7795         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.7759    | 0.0        | nan          | nan            | 0.0         | nan              |
  | 0.0534        | 1.75  | 140  | 0.0885          | 0.1597   | 0.2130        | 0.5990           | nan                | 0.6390         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.6388    | 0.0        | nan          | nan            | 0.0         | nan              |
  | 0.0486        | 2.0   | 160  | 0.0899          | 0.1926   | 0.2577        | 0.7247           | nan                | 0.7731         | 0.0             | nan               | nan                 | 0.0              | nan                   | 0.0           | 0.7702    | 0.0        | nan          | nan            | 0.0         | nan              |
  | 0.0342        | 2.25  | 180  | 0.0866          | 0.1978   | 0.2649        | 0.7448        