readme: ---
  license: other
  tags:
  - generated_from_keras_callback
  model-index:
  - name: nateraw/mit-b0-finetuned-sidewalks
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # nateraw/mit-b0-finetuned-sidewalks
  
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Train Loss: 0.5197
  - Validation Loss: 0.6268
  - Validation Mean Iou: 0.2719
  - Validation Mean Accuracy: 0.3442
  - Validation Overall Accuracy: 0.8180
  - Validation Per Category Iou: [0.         0.62230678 0.81645513 0.18616589 0.66669478 0.30574734
          nan 0.36681201 0.31128062 0.         0.76635363 0.
   0.                nan 0.         0.37874505 0.         0.
   0.68193241 0.         0.48867838 0.25809644 0.                nan
   0.         0.25765818 0.         0.         0.81965205 0.71604385
   0.9214592  0.         0.00636635 0.12957446 0.        ]
  - Validation Per Category Accuracy: [0.         0.89469845 0.88320521 0.45231002 0.72104833 0.3386303
          nan 0.53522723 0.72026843 0.         0.93197124 0.
   0.                nan 0.         0.45525816 0.         0.
   0.87276184 0.         0.60762821 0.29654901 0.                nan
   0.         0.32162193 0.         0.         0.90797988 0.89199119
   0.96388697 0.         0.00646084 0.21171965 0.        ]
  - Epoch: 5
  
  ## 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:
  - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  - training_precision: float32
  
  ### Training results
  
  | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | Validation Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | Epoch |
  |:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----:|
  | 1.3430     | 0.8858          | 0.1724              | 0.2253                   | 0.7508                      | [0.00000000e+00 5.02535817e-01 7.94050536e-01 1.37476079e-01
   5.28949130e-01 1.76391302e-01            nan 1.19967229e-01
   0.00000000e+00 0.00000000e+00 6.61310784e-01 0.00000000e+00
   0.00000000e+00            nan 0.00000000e+00 0.00000000e+00
   0.00000000e+00 0.00000000e+00 5.06634036e-01 0.00000000e+00
   7.22567226e-02 5.35294630e-03 0.00000000e+00 0.00000000e+00
   0.00000000e+00 1.53949868e-02 0.00000000e+00 0.00000000e+00
   7.37842004e-01 5.78989440e-01 8.52258994e-01 0.00000000e+00
   0.00000000e+00 6.16858377e-05 0.00000000e+00] 