segformer-b0-finetuned-segments-sidewalk-2

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

  • Loss: 1.5766
  • Mean Iou: 0.1371
  • Mean Accuracy: 0.1845
  • Overall Accuracy: 0.7137
  • Per Category Iou: [nan, 0.4878460273549076, 0.7555073936058639, 0.0, 0.021023119916983492, 6.661075803708754e-07, nan, 0.0, 0.0, 0.0, 0.5945035814400078, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5297440422960749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7198551864914086, 0.489491922020114, 0.7904739581298643, 0.0, 0.0, 0.0, 0.0]
  • Per Category Accuracy: [nan, 0.8122402393699828, 0.916307187222316, 0.0, 0.02103704204254936, 6.661204478993891e-07, nan, 0.0, 0.0, 0.0, 0.8989685161351728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8771164563053133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9407576559617489, 0.6174625729307718, 0.821407178606353, 0.0, 0.0, 0.0, 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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
1.7378 0.5 100 1.8155 0.1247 0.1711 0.6916 [nan, 0.46555602373647126, 0.7353888954834877, 0.0, 0.009228814643632902, 0.00010122529220478676, nan, 0.0, 0.0, 0.0, 0.5739398587525921, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5094931557231258, 0.0, 5.3423644732762526e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6692211552011066, 0.2651198767217891, 0.7633341956468725, 0.0, 0.0, 0.0, 0.0] [nan, 0.7515868666775908, 0.9079116468110205, 0.0, 0.009233668577520995, 0.00010125030808070715, nan, 0.0, 0.0, 0.0, 0.8690972710699856, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8791886402701642, 0.0, 5.361129513400909e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9598752209264458, 0.3079896302845186, 0.7892434570182117, 0.0, 0.0, 0.0, 0.0]
1.6547 1.0 200 1.5766 0.1371 0.1845 0.7137 [nan, 0.4878460273549076, 0.7555073936058639, 0.0, 0.021023119916983492, 6.661075803708754e-07, nan, 0.0, 0.0, 0.0, 0.5945035814400078, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5297440422960749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7198551864914086, 0.489491922020114, 0.7904739581298643, 0.0, 0.0, 0.0, 0.0] [nan, 0.8122402393699828, 0.916307187222316, 0.0, 0.02103704204254936, 6.661204478993891e-07, nan, 0.0, 0.0, 0.0, 0.8989685161351728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8771164563053133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9407576559617489, 0.6174625729307718, 0.821407178606353, 0.0, 0.0, 0.0, 0.0]

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.1
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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