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nateraw
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mit-b0-finetuned-sidewalks-v2

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segformer
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1 Parent(s): 1d97c76

Training in progress epoch 12

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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: 0.3487
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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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@@ -207,6 +207,17 @@ The following hyperparameters were used during training:
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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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  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: 0.3460
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+ - Validation Loss: 0.4342
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+ - Validation Mean Iou: 0.3234
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+ - Validation Mean Accuracy: 0.3852
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+ - Validation Overall Accuracy: 0.8669
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+ - Validation Per Category Iou: [0. 0.76828673 0.86958873 0.66044471 0.84588115 0.46323947
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+ nan 0.41208499 0.54202812 0. 0.82543751 0.
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+ 0. 0. 0. 0.50071248 0. 0.
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+ 0.72333932 0.0173886 0.36535728 0.5284402 0. nan
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+ 0. 0.24239821 0.13456635 0. 0.86084123 0.73217705
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+ 0.92386442 0.09545854 0.04193608 0.11945951 0. ]
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+ - Validation Per Category Accuracy: [0. 0.92666259 0.91906703 0.74134089 0.92518489 0.60022437
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+ nan 0.56316038 0.77045814 0. 0.93600314 0.
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+ 0. 0. 0. 0.61358664 0. 0.
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+ 0.87835072 0.01757469 0.57608316 0.64108174 0. nan
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+ 0. 0.30432247 0.13750695 0. 0.93332326 0.85806371
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+ 0.96442783 0.10753599 0.15152274 0.14552189 0. ]
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+ - Epoch: 12
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  ## Model description
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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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+ | 0.3460 | 0.4342 | 0.3234 | 0.3852 | 0.8669 | [0. 0.76828673 0.86958873 0.66044471 0.84588115 0.46323947
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+ nan 0.41208499 0.54202812 0. 0.82543751 0.
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+ 0. 0. 0. 0.50071248 0. 0.
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+ 0.72333932 0.0173886 0.36535728 0.5284402 0. nan
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+ 0. 0.24239821 0.13456635 0. 0.86084123 0.73217705
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+ 0.92386442 0.09545854 0.04193608 0.11945951 0. ] | [0. 0.92666259 0.91906703 0.74134089 0.92518489 0.60022437
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+ nan 0.56316038 0.77045814 0. 0.93600314 0.
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+ 0. 0. 0. 0.61358664 0. 0.
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+ 0.87835072 0.01757469 0.57608316 0.64108174 0. nan
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+ 0. 0.30432247 0.13750695 0. 0.93332326 0.85806371
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+ 0.96442783 0.10753599 0.15152274 0.14552189 0. ] | 12 |
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  ### Framework versions
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