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mit-b0-finetuned-sidewalks

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sayakpaul HF staff commited on
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92e0fe7
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1 Parent(s): 9204b21

Training in progress epoch 25

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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: 0.2424
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- - Validation Loss: 0.6093
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- - Validation Mean Iou: 0.3307
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- - Validation Mean Accuracy: 0.4014
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- - Validation Overall Accuracy: 0.8467
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- - Validation Per Category Iou: [0. 0.73958869 0.86025659 0.4755376 0.53185114 0.36403392
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- nan 0.431977 0.51665889 0.05667583 0.82699054 0.
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- 0. nan 0. 0.59522006 0. 0.
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- 0.70336309 0.14633348 0.35893874 0.36236139 0. nan
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- 0. 0.38549316 0.20212979 0. 0.85264169 0.81347649
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- 0.91720649 0.00336927 0.18366165 0.25489549 0. ]
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- - Validation Per Category Accuracy: [0. 0.83988126 0.94645528 0.56990742 0.68333357 0.47459971
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- nan 0.56392528 0.71480722 0.06313103 0.921812 0.
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- 0. nan 0. 0.79483818 0. 0.
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- 0.87930289 0.15972854 0.44215142 0.48526807 0. nan
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- 0. 0.49199521 0.2562916 0. 0.94459632 0.92598071
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- 0.96917316 0.00629628 0.40341886 0.30884273 0. ]
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- - Epoch: 24
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  ## Model description
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@@ -362,6 +362,17 @@ The following hyperparameters were used during training:
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  0.87930289 0.15972854 0.44215142 0.48526807 0. nan
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  0. 0.49199521 0.2562916 0. 0.94459632 0.92598071
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  0.96917316 0.00629628 0.40341886 0.30884273 0. ] | 24 |
 
 
 
 
 
 
 
 
 
 
 
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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: 0.2225
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+ - Validation Loss: 0.6468
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+ - Validation Mean Iou: 0.3391
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+ - Validation Mean Accuracy: 0.4135
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+ - Validation Overall Accuracy: 0.8494
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+ - Validation Per Category Iou: [0. 0.70993004 0.86487011 0.52191637 0.57664557 0.3444165
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+ nan 0.43372414 0.51182778 0.08290104 0.83020271 0.
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+ 0. nan 0. 0.57442713 0. 0.
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+ 0.73826685 0.15668916 0.47129259 0.34339061 0. nan
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+ 0. 0.37774687 0.19761191 0. 0.85313493 0.81669651
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+ 0.92318268 0.00461872 0.16799491 0.34913299 0. ]
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+ - Validation Per Category Accuracy: [0. 0.76188591 0.94953714 0.62857903 0.76788451 0.49450254
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+ nan 0.60497833 0.66535259 0.09517867 0.92364467 0.
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+ 0. nan 0. 0.74902607 0. 0.
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+ 0.88590133 0.172701 0.64600271 0.4212137 0. nan
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+ 0. 0.45525406 0.24234548 0. 0.93503283 0.93763733
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+ 0.96721471 0.0103905 0.45387632 0.46408109 0. ]
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+ - Epoch: 25
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  ## Model description
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  0.87930289 0.15972854 0.44215142 0.48526807 0. nan
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  0. 0.49199521 0.2562916 0. 0.94459632 0.92598071
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  0.96917316 0.00629628 0.40341886 0.30884273 0. ] | 24 |
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+ | 0.2225 | 0.6468 | 0.3391 | 0.4135 | 0.8494 | [0. 0.70993004 0.86487011 0.52191637 0.57664557 0.3444165
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+ nan 0.43372414 0.51182778 0.08290104 0.83020271 0.
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+ 0. nan 0. 0.57442713 0. 0.
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+ 0.73826685 0.15668916 0.47129259 0.34339061 0. nan
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+ 0. 0.37774687 0.19761191 0. 0.85313493 0.81669651
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+ 0.92318268 0.00461872 0.16799491 0.34913299 0. ] | [0. 0.76188591 0.94953714 0.62857903 0.76788451 0.49450254
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+ nan 0.60497833 0.66535259 0.09517867 0.92364467 0.
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+ 0. nan 0. 0.74902607 0. 0.
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+ 0.88590133 0.172701 0.64600271 0.4212137 0. nan
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+ 0. 0.45525406 0.24234548 0. 0.93503283 0.93763733
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+ 0.96721471 0.0103905 0.45387632 0.46408109 0. ] | 25 |
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  ### Framework versions
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