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nateraw
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mit-b0-finetuned-sidewalks-v2

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segformer
generated_from_keras_callback
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1d97c76
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1 Parent(s): d9166b8

Training in progress epoch 11

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Files changed (2) hide show
  1. README.md +29 -18
  2. tf_model.h5 +1 -1
README.md CHANGED
@@ -14,24 +14,24 @@ probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Train Loss: 0.3736
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- - Validation Loss: 0.4515
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- - Validation Mean Iou: 0.3180
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- - Validation Mean Accuracy: 0.3859
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- - Validation Overall Accuracy: 0.8600
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- - Validation Per Category Iou: [0. 0.77296038 0.8679117 0.60122746 0.84573808 0.42877201
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- nan 0.40372521 0.5356554 0. 0.82057963 0.
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- 0. 0. 0. 0.48309209 0. 0.
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- 0.70156487 0.07165346 0.31172072 0.45383525 0. nan
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- 0. 0.26337213 0.07457255 0. 0.85227381 0.7079085
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- 0.92271657 0.20363628 0.03853875 0.13249146 0. ]
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- - Validation Per Category Accuracy: [0. 0.90081404 0.93156248 0.71723323 0.91251575 0.57187527
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- nan 0.53665381 0.74547838 0. 0.93718616 0.
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- 0. 0. 0. 0.6410839 0. 0.
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- 0.80529967 0.07249561 0.6074764 0.5775282 0. nan
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- 0. 0.34898163 0.07545859 0. 0.95221746 0.80297775
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- 0.96768443 0.26155608 0.19382562 0.17354842 0. ]
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- - Epoch: 10
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  ## Model description
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@@ -196,6 +196,17 @@ The following hyperparameters were used during training:
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  0.80529967 0.07249561 0.6074764 0.5775282 0. nan
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  0. 0.34898163 0.07545859 0. 0.95221746 0.80297775
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  0.96768443 0.26155608 0.19382562 0.17354842 0. ] | 10 |
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Train Loss: 0.3487
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+ - Validation Loss: 0.4486
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+ - Validation Mean Iou: 0.3181
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+ - Validation Mean Accuracy: 0.3898
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+ - Validation Overall Accuracy: 0.8637
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+ - Validation Per Category Iou: [0. 0.79416982 0.87767891 0.70942695 0.81634288 0.46749785
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+ nan 0.42873013 0.48671464 0. 0.82752704 0.
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+ 0. 0. 0. 0.50844774 0. 0.
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+ 0.68070149 0.03976498 0.29304387 0.46322705 0. nan
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+ 0. 0.24856882 0.12795031 0. 0.84646906 0.71781094
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+ 0.92550642 0.04810685 0.04610752 0.14423047 0. ]
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+ - Validation Per Category Accuracy: [0. 0.86951324 0.95247608 0.82408892 0.90393017 0.59760857
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+ nan 0.5760741 0.83602638 0. 0.93420702 0.
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+ 0. 0. 0. 0.63502483 0. 0.
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+ 0.76902695 0.04024918 0.57179186 0.75842139 0. nan
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+ 0. 0.30837498 0.13239994 0. 0.95283514 0.78607095
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+ 0.96594744 0.05354669 0.18906967 0.2060098 0. ]
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+ - Epoch: 11
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  ## Model description
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  0.80529967 0.07249561 0.6074764 0.5775282 0. nan
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  0. 0.34898163 0.07545859 0. 0.95221746 0.80297775
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  0.96768443 0.26155608 0.19382562 0.17354842 0. ] | 10 |
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+ | 0.3487 | 0.4486 | 0.3181 | 0.3898 | 0.8637 | [0. 0.79416982 0.87767891 0.70942695 0.81634288 0.46749785
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+ nan 0.42873013 0.48671464 0. 0.82752704 0.
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+ 0. 0. 0. 0.50844774 0. 0.
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+ 0.68070149 0.03976498 0.29304387 0.46322705 0. nan
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+ 0. 0.24856882 0.12795031 0. 0.84646906 0.71781094
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+ 0.92550642 0.04810685 0.04610752 0.14423047 0. ] | [0. 0.86951324 0.95247608 0.82408892 0.90393017 0.59760857
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+ nan 0.5760741 0.83602638 0. 0.93420702 0.
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+ 0. 0. 0. 0.63502483 0. 0.
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+ 0.76902695 0.04024918 0.57179186 0.75842139 0. nan
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+ 0. 0.30837498 0.13239994 0. 0.95283514 0.78607095
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+ 0.96594744 0.05354669 0.18906967 0.2060098 0. ] | 11 |
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  ### Framework versions
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