readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b0-pavement
    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-pavement
  
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the reannayang/FL_pavement dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4165
  - Mean Iou: 0.6318
  - Mean Accuracy: 0.9700
  - Overall Accuracy: 0.9738
  - Per Category Iou: [0.0, 0.964166382973358, 0.9809231860559384, 0.0, 0.9295139919583345, 0.9164463823409184]
  - Per Category Accuracy: [nan, 0.9643001261034048, 0.9983497924348297, nan, 0.995031342981772, 0.9223532638507954]
  
  ## 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                                                                      |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|
  | 1.0651        | 10.0  | 20   | 1.3005          | 0.5967   | 0.9512        | 0.9534           | [0.0, 0.9462421185372005, 0.9681701711239586, 0.0, 0.7994398965962947, 0.8662896799897185] | [nan, 0.9462421185372005, 0.9693809143181291, nan, 0.9648149753011526, 0.9243828853538124] |
  | 0.5732        | 20.0  | 40   | 0.6626          | 0.6287   | 0.9702        | 0.9760           | [0.0, 0.975246652572234, 0.985446932366533, 0.0, 0.9010974339804011, 0.9103918683964157]   | [nan, 0.9772635561160151, 0.9952040842637238, nan, 0.9748678395008233, 0.9334887547997806] |
  | 0.6987        | 30.0  | 60   | 0.4319          | 0.6317   | 0.9705        | 0.9758           | [0.0, 0.9709705045212967, 0.9798115236227942, 0.0, 0.9255918522130127, 0.9139245313729214] | [nan, 0.9722194199243379, 0.9986205296134905, nan, 0.9871161568015715, 0.924026330224904]  |
  | 0.6915        | 40.0  | 80   | 0.4382          | 0.6237   | 0.9634        | 0.9692           | [0.0, 0.9611727616645649, 0.9725125142706595, 0.0, 0.9147983251179308, 0.8937433316006894] | [nan, 0.9611727616645649, 0.9993811721630611, nan, 0.9971690210012422, 0.896023038946791]  |
  | 0.4373        | 50.0  | 100  | 0.4165          | 0.6318   | 0.9700        | 0.9738           | [0.0, 0.964166382973358, 0.9809231860559384, 0.0, 0.9295139919583345, 0.9164463823409184]  | [nan, 0.9643001261034048, 0.9983497924348297, nan, 0.995031342981772, 0.9223532638507954]  |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.7.1
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  