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mit-b0-finetuned-sidewalks

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sayakpaul HF staff commited on
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1 Parent(s): 3f0cb44

Training in progress epoch 20

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  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: 0.2706
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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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@@ -301,6 +301,17 @@ The following hyperparameters were used during training:
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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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  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: 0.2593
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+ - Validation Loss: 0.6052
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+ - Validation Mean Iou: 0.3341
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+ - Validation Mean Accuracy: 0.4031
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+ - Validation Overall Accuracy: 0.8496
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+ - Validation Per Category Iou: [0. 0.73848967 0.86979676 0.50888802 0.54822563 0.35498187
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+ nan 0.41457852 0.49852188 0.05160441 0.81578019 0.
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+ 0. nan 0. 0.55649339 0. 0.
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+ 0.71513888 0.10932455 0.45060669 0.40457293 0. nan
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+ 0. 0.36272233 0.17307211 0. 0.84243775 0.79946792
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+ 0.91817544 0.012773 0.20910169 0.33510921 0. ]
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+ - Validation Per Category Accuracy: [0. 0.84018083 0.95039376 0.60283148 0.66137445 0.46000795
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+ nan 0.56556385 0.67075327 0.06449234 0.92243169 0.
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+ 0. nan 0. 0.66928878 0. 0.
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+ 0.87170292 0.11050136 0.60695508 0.46164118 0. nan
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+ 0. 0.4538352 0.21895404 0. 0.93350917 0.93032015
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+ 0.9603643 0.04076273 0.46684323 0.43611836 0. ]
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+ - Epoch: 20
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  ## Model description
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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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+ | 0.2593 | 0.6052 | 0.3341 | 0.4031 | 0.8496 | [0. 0.73848967 0.86979676 0.50888802 0.54822563 0.35498187
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+ nan 0.41457852 0.49852188 0.05160441 0.81578019 0.
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+ 0. nan 0. 0.55649339 0. 0.
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+ 0.71513888 0.10932455 0.45060669 0.40457293 0. nan
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+ 0. 0.36272233 0.17307211 0. 0.84243775 0.79946792
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+ 0.91817544 0.012773 0.20910169 0.33510921 0. ] | [0. 0.84018083 0.95039376 0.60283148 0.66137445 0.46000795
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+ nan 0.56556385 0.67075327 0.06449234 0.92243169 0.
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+ 0. nan 0. 0.66928878 0. 0.
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+ 0.87170292 0.11050136 0.60695508 0.46164118 0. nan
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+ 0. 0.4538352 0.21895404 0. 0.93350917 0.93032015
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+ 0.9603643 0.04076273 0.46684323 0.43611836 0. ] | 20 |
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  ### Framework versions
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