readme: ---
  license: other
  tags:
  - vision
  - image-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b0-finetuned-busigt2
    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-b0-finetuned-busigt2
  
  This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the kasumi222/busigt5 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2904
  - Mean Iou: 0.4458
  - Mean Accuracy: 0.6980
  - Overall Accuracy: 0.6969
  - Per Category Iou: [0.0, 0.6551336334577343, 0.6821319425157643]
  - Per Category Accuracy: [nan, 0.6913100552356098, 0.70464740289276]
  
  ## 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: 0.00013
  - train_batch_size: 20
  - eval_batch_size: 20
  - 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                         |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------:|:---------------------------------------------:|
  | 0.1095        | 0.77  | 20   | 0.2086          | 0.4674   | 0.7410        | 0.7419           | [0.0, 0.6978460673452154, 0.704309291034096]  | [nan, 0.7461995349612959, 0.7357650020760118] |
  | 0.1156        | 1.54  | 40   | 0.1980          | 0.4186   | 0.6721        | 0.6783           | [0.0, 0.6446507442278364, 0.6112330250576428] | [nan, 0.7089917293749448, 0.635300900559587]  |
  | 0.1039        | 2.31  | 60   | 0.1987          | 0.3706   | 0.5810        | 0.5757           | [0.0, 0.5345322994102119, 0.5773860979625277] | [nan, 0.5495831330265778, 0.6123860258526792] |
  | 0.0672        | 3.08  | 80   | 0.1960          | 0.4099   | 0.6407        | 0.6439           | [0.0, 0.6194380206711395, 0.6103561290824698] | [nan, 0.6596136450596995, 0.6218662960315686] |
  | 0.0992        | 3.85  | 100  | 0.1969          | 0.4201   | 0.6684        | 0.6695           | [0.0, 0.6251984513525223, 0.6351366565306488] | [nan, 0.675036447653713, 0.661700391303438]   |
  | 0.085         | 4.62  | 120  | 0.2075          | 0.4383   | 0.6997        | 0.6964           | [0.0, 0.6407576836532538, 0.6742246105299582] | [nan, 0.6804532655724195, 0.718889834811138]  |
  | 0.0561        | 5.38  | 140  | 0.2037          | 0.4401   | 0.7033        | 0.7071           | [0.0, 0.6545188689920507, 0.665783897448558]  | [nan, 0.7263735810923504, 0.6801427547189345] |
  | 0.0841        | 6.15  | 160  | 0.2119          | 0.3651   | 0.5891        | 0.5934           | [0.0, 0.5494216923933923, 0.5458843877102458] | [nan, 0.6146571565924632, 0.5634664881039569] |
  | 0.1034        | 6.92  | 180  | 0.2371          | 0.3684   | 0.6193        | 0.6367           | [0.0, 0.6047004430113216, 0.5003660220404046] | [nan, 0.7229919452156935, 0.5156554415186935] |
  | 0.0691        | 7.69  | 200  | 0.2266          | 0.4285   | 0.6991        | 0.7117           | [0.0, 0.6730686627556878, 0.6124621276402561] | [nan, 0.7742042834577688, 0.6240342690621383] |
  | 0.0601        | 8.46  | 220  | 0.2106          | 0.4198   | 0.6674        | 0.6704           | [0.0, 0.6308213023617786, 0.6287108585057931] | [nan, 0.6851880267250091, 0.6497046776895365] |
  | 0.0647        | 9.23  | 240  | 0.2234          | 0.4229   | 0.6746        | 0.6777           | [0.0, 0.6338885508159525, 0.6349404984513296] | [nan, 0.6928998204597407, 0.6563077167064432] |
  | 0.0626        | 10.0  | 260  | 0.2322          | 0.3991   | 0.6540        | 0.6655           | [0.0, 0.6267222060572648, 0.570544858752452]  | [nan, 0.7227113522422911, 0.5852409330048426] |
  | 0.0604        | 10.77 | 280  | 0.2021          | 0.4660   | 0.7283        | 0.7288           | [0.0, 0.6990308020264264, 0.6989818924111941] | [nan, 0.7310753774760368, 0.7255727204344536] |
  | 0.0573        | 11.54 | 300  | 0.2227          | 0.4513   | 0.7014        | 0.6951           | [0.0, 0.6488805486358904, 0.7049138389320693] | [nan, 0.6638350976679388, 0.7389417956785915] |
  | 0.0474        | 12.31 | 320  | 0.2108          | 0.4781   | 0.7468        | 0.7371           | [0.0, 0.6761855871787447, 0.7580093480444655] | [nan, 0.6890590324447889, 0.8044529075728725] |
  | 0.0805        | 13.08 | 340  | 0.2257          | 0.4325   | 0.6902        | 0.6940           | [0.0, 0.6550347525850334, 0.6423545682885212] | [nan, 0.7128733309133007, 0.6675247882412931] |
  | 0.0545        | 13.85 | 360  | 0.2155          | 0.4609   | 0.7230        