segformer-b0-finetuned-segments-sidewalk-oct-22

This model is a fine-tuned version of nvidia/mit-b0 on the adsjnajefwnb/di dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1425
  • Mean Iou: 0.4608
  • Mean Accuracy: 0.6144
  • Overall Accuracy: 0.9524
  • Accuracy Unlabeled: nan
  • Accuracy Anqvan: nan
  • Accuracy Baohuxiang: nan
  • Accuracy Biaozhi: nan
  • Accuracy Dianlan: 0.9591
  • Accuracy Miehuoqi: 0.8841
  • Accuracy Zhaoming: 0.0
  • Iou Unlabeled: 0.0
  • Iou Anqvan: nan
  • Iou Baohuxiang: nan
  • Iou Biaozhi: nan
  • Iou Dianlan: 0.9591
  • Iou Miehuoqi: 0.8841
  • Iou Zhaoming: 0.0

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Anqvan Accuracy Baohuxiang Accuracy Biaozhi Accuracy Dianlan Accuracy Miehuoqi Accuracy Zhaoming Iou Unlabeled Iou Anqvan Iou Baohuxiang Iou Biaozhi Iou Dianlan Iou Miehuoqi Iou Zhaoming
1.5348 10.0 20 1.8080 0.3181 0.6370 0.9720 nan nan nan nan 0.9776 0.9334 0.0 0.0 nan 0.0 0.0 0.9759 0.9328 0.0
1.5003 20.0 40 1.5287 0.3865 0.6442 0.9726 nan nan nan nan 0.9773 0.9552 0.0 0.0 nan nan 0.0 0.9773 0.9552 0.0
1.4909 30.0 60 1.2543 0.4770 0.6359 0.9695 nan nan nan nan 0.9750 0.9328 0.0 0.0 nan nan nan 0.9750 0.9328 0.0
1.331 40.0 80 1.1463 0.4671 0.6229 0.9613 nan nan nan nan 0.9677 0.9009 0.0 0.0 nan nan nan 0.9677 0.9009 0.0
1.0843 50.0 100 1.1425 0.4608 0.6144 0.9524 nan nan nan nan 0.9591 0.8841 0.0 0.0 nan nan nan 0.9591 0.8841 0.0

Framework versions

  • Transformers 4.46.3
  • Pytorch 1.12.1+cu113
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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