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This model is a fine-tuned version of nvidia/mit-b5 on the mraottth/all_locations_pooled dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0189
  • Mean Iou: 0.4050
  • Mean Accuracy: 0.8101
  • Overall Accuracy: 0.8101
  • Accuracy Unlabeled: nan
  • Accuracy Trash: 0.8101
  • Iou Unlabeled: 0.0
  • Iou Trash: 0.8101

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 3
  • eval_batch_size: 3
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Trash Iou Unlabeled Iou Trash
0.0592 1.0 90 0.0387 0.3723 0.7446 0.7446 nan 0.7446 0.0 0.7446
0.0402 2.0 180 0.0281 0.4123 0.8247 0.8247 nan 0.8247 0.0 0.8247
0.0209 3.0 270 0.0246 0.3691 0.7382 0.7382 nan 0.7382 0.0 0.7382
0.0117 4.0 360 0.0210 0.3882 0.7763 0.7763 nan 0.7763 0.0 0.7763
0.019 5.0 450 0.0198 0.3822 0.7644 0.7644 nan 0.7644 0.0 0.7644
0.0445 6.0 540 0.0199 0.3771 0.7542 0.7542 nan 0.7542 0.0 0.7542
0.0195 7.0 630 0.0191 0.4177 0.8354 0.8354 nan 0.8354 0.0 0.8354
0.008 8.0 720 0.0191 0.4060 0.8119 0.8119 nan 0.8119 0.0 0.8119
0.0268 9.0 810 0.0188 0.4083 0.8166 0.8166 nan 0.8166 0.0 0.8166
0.0061 10.0 900 0.0189 0.4050 0.8101 0.8101 nan 0.8101 0.0 0.8101

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2
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