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SegFormer_mit-b5_Clean-Set3_Augmented_Medium

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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - generated_from_trainer
model-index:
  - name: SegFormer_mit-b5_Clean-Set3_Augmented_Medium
    results: []

SegFormer_mit-b5_Clean-Set3_Augmented_Medium

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

  • Loss: 0.0205
  • Mean Iou: 0.9709
  • Mean Accuracy: 0.9837
  • Overall Accuracy: 0.9928
  • Accuracy Background: 0.9979
  • Accuracy Melt: 0.9593
  • Accuracy Substrate: 0.9938
  • Iou Background: 0.9927
  • Iou Melt: 0.9315
  • Iou Substrate: 0.9885

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: 0.0002
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Melt Accuracy Substrate Iou Background Iou Melt Iou Substrate
0.1481 0.3968 50 0.1951 0.7748 0.8972 0.9261 0.9808 0.8114 0.8996 0.9583 0.4859 0.8802
0.1364 0.7937 100 0.0745 0.8855 0.9155 0.9727 0.9933 0.7647 0.9884 0.9753 0.7206 0.9604
0.0776 1.1905 150 0.1238 0.8046 0.8351 0.9565 0.9922 0.5172 0.9960 0.9754 0.5059 0.9325
0.0851 1.5873 200 0.0878 0.8651 0.8901 0.9687 0.9923 0.6841 0.9940 0.9791 0.6665 0.9498
0.1893 1.9841 250 0.0602 0.9077 0.9628 0.9766 0.9958 0.9233 0.9693 0.9872 0.7755 0.9602
0.1236 2.3810 300 0.0643 0.9042 0.9455 0.9768 0.9971 0.8609 0.9783 0.9758 0.7669 0.9699
0.0809 2.7778 350 0.0387 0.9408 0.9699 0.9857 0.9964 0.9271 0.9862 0.9895 0.8562 0.9769
0.0357 3.1746 400 0.0364 0.9431 0.9701 0.9860 0.9948 0.9274 0.9880 0.9897 0.8629 0.9767
0.0408 3.5714 450 0.0424 0.9349 0.9815 0.9834 0.9936 0.9745 0.9765 0.9904 0.8434 0.9708
0.0973 3.9683 500 0.0541 0.9172 0.9798 0.9785 0.9941 0.9796 0.9655 0.9903 0.7997 0.9615
0.0274 4.3651 550 0.0256 0.9636 0.9830 0.9904 0.9967 0.9629 0.9894 0.9893 0.9170 0.9844
0.04 4.7619 600 0.0329 0.9482 0.9696 0.9877 0.9977 0.9210 0.9899 0.9888 0.8743 0.9815
0.0301 5.1587 650 0.0247 0.9609 0.9756 0.9905 0.9975 0.9361 0.9933 0.9917 0.9067 0.9844
0.0137 5.5556 700 0.0214 0.9667 0.9779 0.9919 0.9962 0.9414 0.9962 0.9924 0.9206 0.9870
0.019 5.9524 750 0.0243 0.9619 0.9817 0.9907 0.9959 0.9577 0.9915 0.9916 0.9090 0.9851
0.021 6.3492 800 0.0200 0.9678 0.9853 0.9920 0.9972 0.9668 0.9917 0.9926 0.9237 0.9871
0.0156 6.7460 850 0.0211 0.9689 0.9813 0.9924 0.9965 0.9520 0.9954 0.9926 0.9259 0.9880
0.0153 7.1429 900 0.0205 0.9685 0.9842 0.9923 0.9970 0.9626 0.9930 0.9930 0.9249 0.9876
0.0125 7.5397 950 0.0205 0.9709 0.9837 0.9928 0.9979 0.9593 0.9938 0.9927 0.9315 0.9885

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.19.2
  • Tokenizers 0.19.1