readme: ---
  license: other
  tags:
  - generated_from_trainer
  model-index:
  - name: segformer-b0-finetuned-segments-rowbody-4cats
    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-segments-rowbody-4cats
  
  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:
  - Loss: 0.1486
  - Mean Iou: 0.6527
  - Mean Accuracy: 0.9381
  - Overall Accuracy: 0.9558
  - Accuracy Sleeve-right: nan
  - Accuracy Sleeve-left: 0.9259
  - Accuracy Neck: 0.9212
  - Accuracy Body: 0.9670
  - Iou Sleeve-right: 0.0
  - Iou Sleeve-left: 0.9012
  - Iou Neck: 0.7545
  - Iou Body: 0.9551
  
  ## 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: 1
  - eval_batch_size: 1
  - 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 | Accuracy Sleeve-right | Accuracy Sleeve-left | Accuracy Neck | Accuracy Body | Iou Sleeve-right | Iou Sleeve-left | Iou Neck | Iou Body |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------:|:--------------------:|:-------------:|:-------------:|:----------------:|:---------------:|:--------:|:--------:|
  | 0.9629        | 2.5   | 20   | 1.1228          | 0.2325   | 0.3900        | 0.7680           | nan                   | 0.1708               | 0.0           | 0.9991        | 0.0              | 0.1703          | 0.0      | 0.7598   |
  | 0.6667        | 5.0   | 40   | 0.6465          | 0.4513   | 0.6646        | 0.8681           | nan                   | 0.5731               | 0.4337        | 0.9870        | 0.0              | 0.5659          | 0.3911   | 0.8483   |
  | 0.414         | 7.5   | 60   | 0.4340          | 0.5935   | 0.8506        | 0.9377           | nan                   | 0.8496               | 0.7215        | 0.9806        | 0.0              | 0.8374          | 0.6136   | 0.9228   |
  | 0.3351        | 10.0  | 80   | 0.3371          | 0.6263   | 0.9237        | 0.9411           | nan                   | 0.9325               | 0.8909        | 0.9478        | 0.0              | 0.8568          | 0.7171   | 0.9314   |
  | 0.4849        | 12.5  | 100  | 0.3146          | 0.6428   | 0.9438        | 0.9526           | nan                   | 0.9708               | 0.9093        | 0.9513        | 0.0              | 0.8922          | 0.7375   | 0.9415   |
  | 0.2461        | 15.0  | 120  | 0.2716          | 0.6431   | 0.9554        | 0.9496           | nan                   | 0.9717               | 0.9511        | 0.9434        | 0.0              | 0.8938          | 0.7410   | 0.9373   |
  | 0.2123        | 17.5  | 140  | 0.2477          | 0.6715   | 0.9481        | 0.9679           | nan                   | 0.9523               | 0.9151        | 0.9768        | 0.0              | 0.9229          | 0.7999   | 0.9632   |
  | 0.1827        | 20.0  | 160  | 0.2413          | 0.6620   | 0.9048        | 0.9649           | nan                   | 0.9200               | 0.8032        | 0.9912        | 0.0              | 0.9147          | 0.7717   | 0.9617   |
  | 0.2828        | 22.5  | 180  | 0.2286          | 0.6484   | 0.9472        | 0.9532           | nan                   | 0.9622               | 0.9265        | 0.9530        | 0.0              | 0.8996          | 0.7495   | 0.9443   |
  | 0.4631        | 25.0  | 200  | 0.2137          | 0.6459   | 0.9452        | 0.9485           | nan                   | 0.9523               | 0.9345        | 0.9486        | 0.0              | 0.8886          | 0.7543   | 0.9408   |
  | 0.159         | 27.5  | 220  | 0.1854          | 0.6336   | 0.9374        | 0.9415           | nan                   | 0.9355               | 0.9328        | 0.9440        | 0.0              | 0.8832          | 0.7145   | 0.9368   |
  | 0.1361        | 30.0  | 240  | 0.1760          | 0.6563   | 0.9429        | 0.9576           | nan                   | 0.9492               | 0.9161        | 0.9635        | 0.0              | 0.9027          | 0.7687   | 0.9538   |
  | 0.1369        | 32.5  | 260  | 0.1634          | 0.6516   | 0.9451        | 0.9562           | nan                   | 0.9497               | 0.9249        | 0.9607        | 0.0              | 0.9130          | 0.7426   | 0.9507   |
  | 0.1356        | 35.0  | 280  | 0.1580          | 0.6488   | 0.9248        | 0.9527           | nan                   | 0.9154               | 0.8905        | 0.9684        