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BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB

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metadata
license: apache-2.0
base_model: microsoft/beit-base-finetuned-ade-640-640
tags:
  - generated_from_trainer
model-index:
  - name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB
    results: []

BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB

This model is a fine-tuned version of microsoft/beit-base-finetuned-ade-640-640 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0652
  • Mean Iou: 0.9446
  • Mean Accuracy: 0.9687
  • Overall Accuracy: 0.9855
  • Accuracy Background: 0.9893
  • Accuracy Melt: 0.9263
  • Accuracy Substrate: 0.9905
  • Iou Background: 0.9796
  • Iou Melt: 0.8757
  • Iou Substrate: 0.9785

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 50

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
0.1893 3.7037 100 0.1358 0.9180 0.9486 0.9815 0.9923 0.8651 0.9884 0.9821 0.7997 0.9723
0.0951 7.4074 200 0.0746 0.9483 0.9791 0.9876 0.9909 0.9575 0.9890 0.9860 0.8775 0.9814
0.0892 11.1111 300 0.0631 0.9489 0.9772 0.9866 0.9875 0.9536 0.9903 0.9817 0.8854 0.9796
0.0878 14.8148 400 0.0692 0.9375 0.9687 0.9829 0.9853 0.9332 0.9877 0.9742 0.8632 0.9750
0.07 18.5185 500 0.0631 0.9419 0.9700 0.9844 0.9865 0.9339 0.9897 0.9777 0.8714 0.9767
0.0507 22.2222 600 0.0646 0.9379 0.9659 0.9829 0.9879 0.9230 0.9869 0.9738 0.8653 0.9747
0.0523 25.9259 700 0.0569 0.9485 0.9724 0.9864 0.9903 0.9371 0.9898 0.9809 0.8855 0.9791
0.0402 29.6296 800 0.0622 0.9422 0.9686 0.9848 0.9881 0.9279 0.9897 0.9777 0.8711 0.9778
0.0406 33.3333 900 0.0631 0.9427 0.9683 0.9852 0.9894 0.9256 0.9899 0.9790 0.8707 0.9785
0.0389 37.0370 1000 0.0650 0.9436 0.9695 0.9852 0.9879 0.9303 0.9904 0.9784 0.8739 0.9784
0.0396 40.7407 1100 0.0634 0.9457 0.9700 0.9857 0.9900 0.9301 0.9899 0.9798 0.8787 0.9787
0.036 44.4444 1200 0.0653 0.9443 0.9684 0.9855 0.9894 0.9254 0.9906 0.9797 0.8748 0.9786
0.0224 48.1481 1300 0.0652 0.9446 0.9687 0.9855 0.9893 0.9263 0.9905 0.9796 0.8757 0.9785

Framework versions

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