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metadata
license: other
base_model: nvidia/mit-b1
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b1-miic-tl
    results: []

segformer-b1-miic-tl

This model is a fine-tuned version of nvidia/mit-b1 on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2212
  • Mean Iou: 0.4723
  • Mean Accuracy: 0.9446
  • Overall Accuracy: 0.9446
  • Accuracy Unlabeled: nan
  • Accuracy Circuit: 0.9446
  • Iou Unlabeled: 0.0
  • Iou Circuit: 0.9446
  • Dice Coefficient: 0.8541

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Circuit Iou Unlabeled Iou Circuit Dice Coefficient
0.3419 3.12 250 0.2745 0.4850 0.9701 0.9701 nan 0.9701 0.0 0.9701 0.8149
0.2785 6.25 500 0.2789 0.4828 0.9657 0.9657 nan 0.9657 0.0 0.9657 0.8285
0.2549 9.38 750 0.2888 0.4721 0.9443 0.9443 nan 0.9443 0.0 0.9443 0.8372
0.2728 12.5 1000 0.2426 0.4699 0.9397 0.9397 nan 0.9397 0.0 0.9397 0.8424
0.2625 15.62 1250 0.1990 0.4632 0.9264 0.9264 nan 0.9264 0.0 0.9264 0.8520
0.2449 18.75 1500 0.2121 0.4706 0.9412 0.9412 nan 0.9412 0.0 0.9412 0.8508
0.2173 21.88 1750 0.2768 0.4780 0.9559 0.9559 nan 0.9559 0.0 0.9559 0.8485
0.2158 25.0 2000 0.2772 0.4643 0.9287 0.9287 nan 0.9287 0.0 0.9287 0.8383
0.1843 28.12 2250 0.1818 0.4671 0.9343 0.9343 nan 0.9343 0.0 0.9343 0.8685
0.1608 31.25 2500 0.1794 0.4591 0.9182 0.9182 nan 0.9182 0.0 0.9182 0.8618
0.1504 34.38 2750 0.1805 0.4586 0.9172 0.9172 nan 0.9172 0.0 0.9172 0.8647
0.1495 37.5 3000 0.2090 0.4773 0.9545 0.9545 nan 0.9545 0.0 0.9545 0.8595
0.142 40.62 3250 0.2048 0.4750 0.9500 0.9500 nan 0.9500 0.0 0.9500 0.8666
0.1401 43.75 3500 0.2131 0.4756 0.9512 0.9512 nan 0.9512 0.0 0.9512 0.8580
0.1339 46.88 3750 0.2469 0.4773 0.9546 0.9546 nan 0.9546 0.0 0.9546 0.8481
0.1303 50.0 4000 0.2212 0.4723 0.9446 0.9446 nan 0.9446 0.0 0.9446 0.8541

Framework versions

  • Transformers 4.36.2
  • Pytorch 1.11.0+cu115
  • Datasets 2.15.0
  • Tokenizers 0.15.0