readme: ---
  license: other
  tags:
  - vision
  - image-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b5-finetuned-segments-instryde-foot-test
    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-instryde-foot-test
  
  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0496
  - Mean Iou: 0.4672
  - Mean Accuracy: 0.9344
  - Overall Accuracy: 0.9344
  - Per Category Iou: [0.0, 0.9343870058298716]
  - Per Category Accuracy: [nan, 0.9343870058298716]
  
  ## 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
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou          | Per Category Accuracy     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
  | 0.1392        | 0.23  | 20   | 0.2371          | 0.4064   | 0.8128        | 0.8128           | [0.0, 0.8127920708469037] | [nan, 0.8127920708469037] |
  | 0.2273        | 0.45  | 40   | 0.0993          | 0.4449   | 0.8898        | 0.8898           | [0.0, 0.889800913515142]  | [nan, 0.889800913515142]  |
  | 0.0287        | 0.68  | 60   | 0.0607          | 0.4190   | 0.8379        | 0.8379           | [0.0, 0.8379005425233161] | [nan, 0.8379005425233161] |
  | 0.03          | 0.91  | 80   | 0.0572          | 0.4072   | 0.8144        | 0.8144           | [0.0, 0.8144304164916533] | [nan, 0.8144304164916533] |
  | 0.0239        | 1.14  | 100  | 0.0577          | 0.3973   | 0.7946        | 0.7946           | [0.0, 0.7946284254068925] | [nan, 0.7946284254068925] |
  | 0.0196        | 1.36  | 120  | 0.0425          | 0.4227   | 0.8455        | 0.8455           | [0.0, 0.8454754171184029] | [nan, 0.8454754171184029] |
  | 0.0295        | 1.59  | 140  | 0.0368          | 0.4479   | 0.8958        | 0.8958           | [0.0, 0.895802316554768]  | [nan, 0.895802316554768]  |
  | 0.0297        | 1.82  | 160  | 0.0441          | 0.4561   | 0.9121        | 0.9121           | [0.0, 0.9121241975954804] | [nan, 0.9121241975954804] |
  | 0.0276        | 2.05  | 180  | 0.0332          | 0.4629   | 0.9258        | 0.9258           | [0.0, 0.925774145806165]  | [nan, 0.925774145806165]  |
  | 0.0148        | 2.27  | 200  | 0.0395          | 0.4310   | 0.8621        | 0.8621           | [0.0, 0.8620666905637888] | [nan, 0.8620666905637888] |
  | 0.012         | 2.5   | 220  | 0.0372          | 0.4381   | 0.8761        | 0.8761           | [0.0, 0.8761025846276997] | [nan, 0.8761025846276997] |
  | 0.0117        | 2.73  | 240  | 0.0339          | 0.4471   | 0.8941        | 0.8941           | [0.0, 0.8941320836457919] | [nan, 0.8941320836457919] |
  | 0.0198        | 2.95  | 260  | 0.0297          | 0.4485   | 0.8969        | 0.8969           | [0.0, 0.8969491585060927] | [nan, 0.8969491585060927] |
  | 0.0247        | 3.18  | 280  | 0.0303          | 0.4565   | 0.9130        | 0.9130           | [0.0, 0.9130423308930413] | [nan, 0.9130423308930413] |
  | 0.0115        | 3.41  | 300  | 0.0307          | 0.4533   | 0.9066        | 0.9066           | [0.0, 0.9065626188900153] | [nan, 0.9065626188900153] |
  | 0.0164        | 3.64  | 320  | 0.0330          | 0.4549   | 0.9097        | 0.9097           | [0.0, 0.9097436483868343] | [nan, 0.9097436483868343] |
  | 0.0114        | 3.86  | 340  | 0.0362          | 0.4425   | 0.8850        | 0.8850           | [0.0, 0.8849727418868903] | [nan, 0.8849727418868903] |
  | 0.012         | 4.09  | 360  | 0.0321          | 0.4582   | 0.9164        | 0.9164           | [0.0, 0.9164498699219532] | [nan, 0.9164498699219532] |
  | 0.0153        | 4.32  | 380  | 0.0321          | 0.4572   | 0.9144        | 0.9144           | [0.0, 0.9144310762281544] | [nan, 0.9144310762281544] |
  | 0.0115        | 4.55  | 400  | 0.0307          | 0.4573   | 0.9145        | 0.9145           | [0.0, 0.9145300367033407] | [nan, 0.9145300367033407] |
  | 0.0139        | 4.77  | 420  | 0.0330          | 0.4678   | 0.9357        | 0.9357           | [0.0, 0.935664695520609]  | [nan, 0.935664695520609]  |
  | 0.014         | 5.0   | 440  | 0.0317          | 0.4635   | 0.9271        | 0.9271           | [0.0, 0.9270562337402442] | [nan, 0.9270562337402442] |
  | 0.0197        | 5.23  | 460  | 0.0320          | 0.4678