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mit-b0-finetuned-sidewalks

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sayakpaul HF staff commited on
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932be1b
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1 Parent(s): 2e483fe

Training in progress epoch 16

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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: 0.2937
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- - Validation Loss: 0.5953
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- - Validation Mean Iou: 0.3305
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- - Validation Mean Accuracy: 0.4030
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- - Validation Overall Accuracy: 0.8449
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- - Validation Per Category Iou: [0. 0.69638318 0.85825162 0.52324422 0.5433419 0.3469765
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- nan 0.40975989 0.49170959 0.00172109 0.81852012 0.
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- 0. nan 0. 0.54972175 0. 0.
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- 0.72653579 0.12940395 0.49040852 0.36779631 0. nan
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- 0. 0.34952524 0.12859991 0. 0.84955571 0.82163775
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- 0.91348544 0.01357343 0.20901572 0.33696003 0. ]
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- - Validation Per Category Accuracy: [0. 0.77149692 0.93467369 0.62786344 0.75698164 0.52205905
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- nan 0.57849458 0.72399102 0.0020987 0.92177615 0.
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- 0. nan 0. 0.66111028 0. 0.
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- 0.89225829 0.13292311 0.62957849 0.44703839 0. nan
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- 0. 0.41933079 0.14719493 0. 0.94294399 0.92316157
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- 0.95997955 0.02286889 0.43984316 0.43884091 0. ]
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- - Epoch: 15
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  ## Model description
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@@ -251,6 +251,17 @@ The following hyperparameters were used during training:
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  0.89225829 0.13292311 0.62957849 0.44703839 0. nan
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  0. 0.41933079 0.14719493 0. 0.94294399 0.92316157
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  0.95997955 0.02286889 0.43984316 0.43884091 0. ] | 15 |
 
 
 
 
 
 
 
 
 
 
 
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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: 0.2985
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+ - Validation Loss: 0.6752
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+ - Validation Mean Iou: 0.3106
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+ - Validation Mean Accuracy: 0.3773
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+ - Validation Overall Accuracy: 0.8330
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+ - Validation Per Category Iou: [0. 0.70132108 0.84614259 0.46333382 0.56218727 0.24626879
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+ nan 0.40366902 0.47740097 0.0250144 0.80566818 0.
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+ 0. nan 0. 0.53413689 0. 0.
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+ 0.65726308 0.08036975 0.36642674 0.26539948 0. nan
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+ 0. 0.35749813 0.13576539 0. 0.84624546 0.80479301
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+ 0.90656978 0.00419358 0.21944385 0.22915907 0. ]
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+ - Validation Per Category Accuracy: [0. 0.75132765 0.94954979 0.66569367 0.76524644 0.28265659
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+ nan 0.5278041 0.66034967 0.0295519 0.90881823 0.
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+ 0. nan 0. 0.62968235 0. 0.
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+ 0.91281232 0.08130926 0.483296 0.29302424 0. nan
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+ 0. 0.44332482 0.15759113 0. 0.93912247 0.92471332
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+ 0.96940512 0.0103905 0.42560363 0.26159798 0. ]
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+ - Epoch: 16
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  ## Model description
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  0.89225829 0.13292311 0.62957849 0.44703839 0. nan
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  0. 0.41933079 0.14719493 0. 0.94294399 0.92316157
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  0.95997955 0.02286889 0.43984316 0.43884091 0. ] | 15 |
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+ | 0.2985 | 0.6752 | 0.3106 | 0.3773 | 0.8330 | [0. 0.70132108 0.84614259 0.46333382 0.56218727 0.24626879
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+ nan 0.40366902 0.47740097 0.0250144 0.80566818 0.
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+ 0. nan 0. 0.53413689 0. 0.
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+ 0.65726308 0.08036975 0.36642674 0.26539948 0. nan
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+ 0. 0.35749813 0.13576539 0. 0.84624546 0.80479301
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+ 0.90656978 0.00419358 0.21944385 0.22915907 0. ] | [0. 0.75132765 0.94954979 0.66569367 0.76524644 0.28265659
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+ nan 0.5278041 0.66034967 0.0295519 0.90881823 0.
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+ 0. nan 0. 0.62968235 0. 0.
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+ 0.91281232 0.08130926 0.483296 0.29302424 0. nan
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+ 0. 0.44332482 0.15759113 0. 0.93912247 0.92471332
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+ 0.96940512 0.0103905 0.42560363 0.26159798 0. ] | 16 |
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  ### Framework versions
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