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mit-b0-finetuned-sidewalks

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sayakpaul HF staff commited on
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1 Parent(s): 094b0ff

Training in progress epoch 32

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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: 0.1948
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- - Validation Loss: 0.6286
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- - Validation Mean Iou: 0.3352
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- - Validation Mean Accuracy: 0.4155
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- - Validation Overall Accuracy: 0.8554
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- - Validation Per Category Iou: [0. 0.74480318 0.86761128 0.5176209 0.5190301 0.40152005
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- nan 0.4527552 0.53233406 0.15708484 0.82879754 0.00217143
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- 0. 0. 0. 0.58339119 0. 0.
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- 0.72814199 0.14335127 0.46532525 0.37317693 0. nan
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- 0.00161007 0.3834689 0.24461318 0. 0.86028821 0.82354793
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- 0.92365312 0.00621865 0.20450293 0.29498818 0. ]
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- - Validation Per Category Accuracy: [0. 0.85793814 0.94828919 0.60320043 0.61968345 0.53021019
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- nan 0.631553 0.67209681 0.19744753 0.9241889 0.00298109
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- 0. nan 0. 0.72392868 0. 0.
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- 0.89955641 0.1682199 0.58754204 0.44020516 0. nan
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- 0.00308382 0.49677594 0.31448494 0. 0.94186424 0.93023771
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- 0.96854534 0.01029263 0.46237188 0.36048331 0. ]
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- - Epoch: 31
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  ## Model description
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@@ -463,6 +463,17 @@ The following hyperparameters were used during training:
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  0.89955641 0.1682199 0.58754204 0.44020516 0. nan
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  0.00308382 0.49677594 0.31448494 0. 0.94186424 0.93023771
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  0.96854534 0.01029263 0.46237188 0.36048331 0. ] | 31 |
 
 
 
 
 
 
 
 
 
 
 
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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: 0.1898
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+ - Validation Loss: 0.6369
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+ - Validation Mean Iou: 0.3434
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+ - Validation Mean Accuracy: 0.4111
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+ - Validation Overall Accuracy: 0.8572
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+ - Validation Per Category Iou: [0. 0.77005646 0.8691602 0.51421807 0.54505788 0.37509283
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+ nan 0.47473811 0.5177013 0.10361726 0.82314535 0.
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+ 0. nan 0. 0.55573429 0. 0.
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+ 0.72789094 0.16284579 0.46446138 0.32311816 0. nan
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+ 0. 0.38446267 0.21404751 0. 0.86325521 0.8362079
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+ 0.92635894 0.01265831 0.22255217 0.30201657 0. ]
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+ - Validation Per Category Accuracy: [0. 0.84783576 0.94994793 0.63546724 0.67852851 0.47478672
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+ nan 0.65145622 0.67587993 0.11633579 0.93587159 0.
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+ 0. nan 0. 0.6686395 0. 0.
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+ 0.90790966 0.17633784 0.62937796 0.34917531 0. nan
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+ 0. 0.4742821 0.2644691 0. 0.94883563 0.91963887
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+ 0.96496596 0.03255799 0.46656807 0.38725811 0. ]
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+ - Epoch: 32
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  ## Model description
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  0.89955641 0.1682199 0.58754204 0.44020516 0. nan
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  0.00308382 0.49677594 0.31448494 0. 0.94186424 0.93023771
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  0.96854534 0.01029263 0.46237188 0.36048331 0. ] | 31 |
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+ | 0.1898 | 0.6369 | 0.3434 | 0.4111 | 0.8572 | [0. 0.77005646 0.8691602 0.51421807 0.54505788 0.37509283
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+ nan 0.47473811 0.5177013 0.10361726 0.82314535 0.
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+ 0. nan 0. 0.55573429 0. 0.
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+ 0.72789094 0.16284579 0.46446138 0.32311816 0. nan
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+ 0. 0.38446267 0.21404751 0. 0.86325521 0.8362079
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+ 0.92635894 0.01265831 0.22255217 0.30201657 0. ] | [0. 0.84783576 0.94994793 0.63546724 0.67852851 0.47478672
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+ nan 0.65145622 0.67587993 0.11633579 0.93587159 0.
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+ 0. nan 0. 0.6686395 0. 0.
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+ 0.90790966 0.17633784 0.62937796 0.34917531 0. nan
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+ 0. 0.4742821 0.2644691 0. 0.94883563 0.91963887
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+ 0.96496596 0.03255799 0.46656807 0.38725811 0. ] | 32 |
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  ### Framework versions
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