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mit-b0-finetuned-sidewalks

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sayakpaul HF staff commited on
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1 Parent(s): d2f257b

Training in progress epoch 21

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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: 0.2593
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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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@@ -312,6 +312,17 @@ The following hyperparameters were used during training:
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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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  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: 0.2566
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+ - Validation Loss: 0.6314
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+ - Validation Mean Iou: 0.3271
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+ - Validation Mean Accuracy: 0.3947
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+ - Validation Overall Accuracy: 0.8466
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+ - Validation Per Category Iou: [0. 0.70998525 0.86678486 0.49194558 0.5537256 0.33629647
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+ nan 0.41985347 0.5135536 0.04026674 0.81694759 0.
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+ 0. nan 0. 0.57746551 0. 0.
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+ 0.69225859 0.14474648 0.39427342 0.33636012 0. nan
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+ 0. 0.35794269 0.17788878 0. 0.85568704 0.8282232
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+ 0.91236012 0.01923597 0.18998213 0.23102913 0. ]
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+ - Validation Per Category Accuracy: [0. 0.80619283 0.95245983 0.57379426 0.72315118 0.42797816
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+ nan 0.57649234 0.7130394 0.04452638 0.92405685 0.
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+ 0. nan 0. 0.70041704 0. 0.
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+ 0.89411357 0.17005455 0.48671835 0.37690491 0. nan
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+ 0. 0.43708457 0.22123613 0. 0.95864813 0.92771201
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+ 0.96946207 0.0313183 0.43519983 0.27845097 0. ]
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+ - Epoch: 21
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  ## Model description
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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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+ | 0.2566 | 0.6314 | 0.3271 | 0.3947 | 0.8466 | [0. 0.70998525 0.86678486 0.49194558 0.5537256 0.33629647
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+ nan 0.41985347 0.5135536 0.04026674 0.81694759 0.
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+ 0. nan 0. 0.57746551 0. 0.
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+ 0.69225859 0.14474648 0.39427342 0.33636012 0. nan
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+ 0. 0.35794269 0.17788878 0. 0.85568704 0.8282232
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+ 0.91236012 0.01923597 0.18998213 0.23102913 0. ] | [0. 0.80619283 0.95245983 0.57379426 0.72315118 0.42797816
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+ nan 0.57649234 0.7130394 0.04452638 0.92405685 0.
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+ 0. nan 0. 0.70041704 0. 0.
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+ 0.89411357 0.17005455 0.48671835 0.37690491 0. nan
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+ 0. 0.43708457 0.22123613 0. 0.95864813 0.92771201
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+ 0.96946207 0.0313183 0.43519983 0.27845097 0. ] | 21 |
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  ### Framework versions
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