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SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001

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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - generated_from_trainer
model-index:
  - name: SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001
    results: []

SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001

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: 0.0217
  • Mean Iou: 0.9708
  • Mean Accuracy: 0.9835
  • Overall Accuracy: 0.9941
  • Accuracy Background: 0.9965
  • Accuracy Melt: 0.9584
  • Accuracy Substrate: 0.9957
  • Iou Background: 0.9940
  • Iou Melt: 0.9288
  • Iou Substrate: 0.9895

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 20
  • mixed_precision_training: Native AMP

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
0.1107 0.8850 50 0.1152 0.8138 0.8439 0.9627 0.9781 0.5623 0.9914 0.9677 0.5412 0.9325
0.0564 1.7699 100 0.0520 0.9163 0.9432 0.9829 0.9967 0.8488 0.9841 0.9806 0.7963 0.9721
0.0296 2.6549 150 0.0270 0.9557 0.9821 0.9906 0.9916 0.9621 0.9928 0.9893 0.8939 0.9839
0.042 3.5398 200 0.0226 0.9619 0.9763 0.9922 0.9934 0.9384 0.9969 0.9917 0.9077 0.9862
0.0166 4.4248 250 0.0300 0.9616 0.9768 0.9904 0.9957 0.9446 0.9903 0.9872 0.9153 0.9823
0.0159 5.3097 300 0.0203 0.9658 0.9863 0.9931 0.9946 0.9701 0.9941 0.9923 0.9169 0.9883
0.0121 6.1947 350 0.0221 0.9645 0.9795 0.9928 0.9937 0.9480 0.9968 0.9923 0.9141 0.9872
0.0149 7.0796 400 0.0220 0.9648 0.9821 0.9930 0.9949 0.9565 0.9951 0.9930 0.9138 0.9874
0.0352 7.9646 450 0.0215 0.9658 0.9764 0.9933 0.9959 0.9361 0.9971 0.9935 0.9158 0.9880
0.0106 8.8496 500 0.0201 0.9696 0.9820 0.9939 0.9961 0.9535 0.9962 0.9938 0.9256 0.9892
0.0095 9.7345 550 0.0216 0.9674 0.9796 0.9936 0.9955 0.9463 0.9969 0.9936 0.9202 0.9886
0.009 10.6195 600 0.0209 0.9702 0.9821 0.9941 0.9966 0.9539 0.9960 0.9940 0.9273 0.9894
0.0106 11.5044 650 0.0211 0.9700 0.9830 0.9940 0.9964 0.9568 0.9958 0.9940 0.9266 0.9893
0.0099 12.3894 700 0.0217 0.9708 0.9835 0.9941 0.9965 0.9584 0.9957 0.9940 0.9288 0.9895

Framework versions

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