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slm-segformer-080823

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

  • Train Loss: 0.0357
  • Validation Loss: 0.0383
  • Validation Mean Iou: 0.8453
  • Validation Mean Accuracy: 0.9366
  • Validation Overall Accuracy: 0.9869
  • Validation Per Category Iou: [0.98646921 0.70414361]
  • Validation Per Category Accuracy: [0.99072207 0.88237991]
  • Epoch: 9

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:

  • optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Validation Mean Iou Validation Mean Accuracy Validation Overall Accuracy Validation Per Category Iou Validation Per Category Accuracy Epoch
0.4798 0.1807 0.6747 0.7770 0.9674 [0.96669254 0.38268484] [0.98185208 0.57215982] 0
0.1552 0.1046 0.7352 0.7991 0.9779 [0.97745298 0.49298956] [0.99154204 0.60674898] 1
0.0981 0.1042 0.7744 0.9090 0.9779 [0.97719564 0.5715319 ] [0.98310851 0.8349177 ] 2
0.0744 0.0978 0.7876 0.9431 0.9784 [0.97773288 0.59755377] [0.98113179 0.90515736] 3
0.0611 0.0728 0.8224 0.9456 0.9836 [0.98310869 0.66170563] [0.98654807 0.90455283] 4
0.0513 0.0531 0.8330 0.9282 0.9856 [0.98518512 0.68084932] [0.99000668 0.86647783] 5
0.0469 0.0514 0.8326 0.9460 0.9850 [0.98451475 0.68075519] [0.9879771 0.90405278] 6
0.0413 0.0406 0.8452 0.9360 0.9869 [0.9864742 0.70392259] [0.99077125 0.88115845] 7
0.0385 0.0412 0.8495 0.9309 0.9875 [0.98715291 0.71182272] [0.99186047 0.86989475] 8
0.0357 0.0383 0.8453 0.9366 0.9869 [0.98646921 0.70414361] [0.99072207 0.88237991] 9

Framework versions

  • Transformers 4.31.0
  • TensorFlow 2.12.0
  • Tokenizers 0.13.3
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