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mit-b0-finetuned-sidewalks

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sayakpaul HF staff commited on
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1 Parent(s): 2250a40

Training in progress epoch 19

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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: 0.2777
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- - Validation Loss: 0.5923
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- - Validation Mean Iou: 0.3311
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- - Validation Mean Accuracy: 0.4005
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- - Validation Overall Accuracy: 0.8496
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- - Validation Per Category Iou: [0. 0.74552742 0.85847838 0.50817039 0.54300979 0.31118625
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- nan 0.42604196 0.48432857 0. 0.80689375 0.
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- 0. nan 0. 0.53701465 0. 0.
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- 0.72095961 0.1181785 0.44646925 0.38422934 0. nan
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- 0. 0.36931298 0.14420905 0. 0.85509203 0.82052953
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- 0.91364053 0.00100348 0.23289685 0.36817268 0. ]
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- - Validation Per Category Accuracy: [0. 0.84028626 0.9434955 0.64877174 0.63691529 0.41306725
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- nan 0.60885149 0.7505436 0. 0.92003686 0.
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- 0. nan 0. 0.6933623 0. 0.
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- 0.87623974 0.12084362 0.60794245 0.40909699 0. nan
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- 0. 0.43594135 0.17001585 0. 0.95100912 0.93487828
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- 0.9639534 0.00228363 0.4196533 0.46863408 0. ]
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- - Epoch: 18
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  ## Model description
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@@ -290,6 +290,17 @@ The following hyperparameters were used during training:
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  0.87623974 0.12084362 0.60794245 0.40909699 0. nan
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  0. 0.43594135 0.17001585 0. 0.95100912 0.93487828
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  0.9639534 0.00228363 0.4196533 0.46863408 0. ] | 18 |
 
 
 
 
 
 
 
 
 
 
 
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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: 0.2706
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+ - Validation Loss: 0.6428
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+ - Validation Mean Iou: 0.3221
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+ - Validation Mean Accuracy: 0.3962
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+ - Validation Overall Accuracy: 0.8382
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+ - Validation Per Category Iou: [0. 0.68452221 0.8452384 0.47997747 0.51381266 0.28444074
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+ nan 0.37559387 0.49555801 0.03074893 0.81638827 0.
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+ 0. nan 0. 0.51609207 0. 0.
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+ 0.71837159 0.08793388 0.44408701 0.37771393 0. nan
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+ 0. 0.32731481 0.15540722 0. 0.85633997 0.83551836
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+ 0.91595508 0.00870258 0.18741004 0.35077478 0. ]
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+ - Validation Per Category Accuracy: [0. 0.85198925 0.92426408 0.60975057 0.67323702 0.32590697
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+ nan 0.51767974 0.69125992 0.03437323 0.90694783 0.
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+ 0. nan 0. 0.75364599 0. 0.
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+ 0.86882314 0.0906936 0.61902497 0.43503007 0. nan
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+ 0. 0.36935451 0.18301109 0. 0.9493751 0.91609312
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+ 0.96411175 0.02618015 0.47471968 0.49440352 0. ]
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+ - Epoch: 19
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  ## Model description
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  0.87623974 0.12084362 0.60794245 0.40909699 0. nan
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  0. 0.43594135 0.17001585 0. 0.95100912 0.93487828
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  0.9639534 0.00228363 0.4196533 0.46863408 0. ] | 18 |
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+ | 0.2706 | 0.6428 | 0.3221 | 0.3962 | 0.8382 | [0. 0.68452221 0.8452384 0.47997747 0.51381266 0.28444074
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+ nan 0.37559387 0.49555801 0.03074893 0.81638827 0.
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+ 0. nan 0. 0.51609207 0. 0.
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+ 0.71837159 0.08793388 0.44408701 0.37771393 0. nan
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+ 0. 0.32731481 0.15540722 0. 0.85633997 0.83551836
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+ 0.91595508 0.00870258 0.18741004 0.35077478 0. ] | [0. 0.85198925 0.92426408 0.60975057 0.67323702 0.32590697
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+ nan 0.51767974 0.69125992 0.03437323 0.90694783 0.
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+ 0. nan 0. 0.75364599 0. 0.
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+ 0.86882314 0.0906936 0.61902497 0.43503007 0. nan
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+ 0. 0.36935451 0.18301109 0. 0.9493751 0.91609312
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+ 0.96411175 0.02618015 0.47471968 0.49440352 0. ] | 19 |
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  ### Framework versions
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