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yijisuk
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segformer-b5-miic-tl

Image Segmentation
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segformer
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segformer-b5-miic-tl

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

  • Loss: 0.2247
  • Mean Iou: 0.4565
  • Mean Accuracy: 0.9129
  • Overall Accuracy: 0.9129
  • Accuracy Unlabeled: nan
  • Accuracy Circuit: 0.9129
  • Iou Unlabeled: 0.0
  • Iou Circuit: 0.9129
  • Dice Coefficient: 0.8406

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
0.2801 3.12 250 0.2305 0.4832 0.9663 0.9663 nan 0.9663 0.0 0.9663 0.8527
0.2785 6.25 500 0.2715 0.4800 0.9601 0.9601 nan 0.9601 0.0 0.9601 0.8511
0.208 9.38 750 0.2681 0.4811 0.9622 0.9622 nan 0.9622 0.0 0.9622 0.8538
0.2042 12.5 1000 0.2959 0.4650 0.9299 0.9299 nan 0.9299 0.0 0.9299 0.7879
0.1649 15.62 1250 0.2407 0.4340 0.8679 0.8679 nan 0.8679 0.0 0.8679 0.8150
0.1353 18.75 1500 0.2530 0.4543 0.9085 0.9085 nan 0.9085 0.0 0.9085 0.8336
0.126 21.88 1750 0.4934 0.4559 0.9119 0.9119 nan 0.9119 0.0 0.9119 0.7678
0.1196 25.0 2000 0.2896 0.4604 0.9209 0.9209 nan 0.9209 0.0 0.9209 0.7807
0.1149 28.12 2250 0.2210 0.4634 0.9268 0.9268 nan 0.9268 0.0 0.9268 0.8470
0.1095 31.25 2500 0.2215 0.4534 0.9067 0.9067 nan 0.9067 0.0 0.9067 0.8380
0.109 34.38 2750 0.2256 0.4243 0.8487 0.8487 nan 0.8487 0.0 0.8487 0.8077
0.1062 37.5 3000 0.2172 0.4497 0.8994 0.8994 nan 0.8994 0.0 0.8994 0.8363
0.1046 40.62 3250 0.2401 0.4551 0.9102 0.9102 nan 0.9102 0.0 0.9102 0.8387
0.1096 43.75 3500 0.2157 0.4582 0.9164 0.9164 nan 0.9164 0.0 0.9164 0.8425
0.1014 46.88 3750 0.2344 0.4573 0.9146 0.9146 nan 0.9146 0.0 0.9146 0.8411
0.1036 50.0 4000 0.2247 0.4565 0.9129 0.9129 nan 0.9129 0.0 0.9129 0.8406

Framework versions

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