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segformer_mixed-set2-788img-rgb_mit-b5_17epochs

Image Segmentation
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Safetensors
segformer
vision
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segformer_mixed-set2-788img-rgb_mit-b5_17epochs / README.md
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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: SegFormer_Mixed_Set2_788images_mit-b5_RGB
    results: []

SegFormer_Mixed_Set2_788images_mit-b5_RGB

This model is a fine-tuned version of nvidia/mit-b5 on the Hasano20/Mixed_Set2_788images dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0179
  • Mean Iou: 0.9757
  • Mean Accuracy: 0.9872
  • Overall Accuracy: 0.9938
  • Accuracy Background: 0.9959
  • Accuracy Melt: 0.9697
  • Accuracy Substrate: 0.9959
  • Iou Background: 0.9922
  • Iou Melt: 0.9437
  • Iou Substrate: 0.9911

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: 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: 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
0.1292 0.7042 50 0.1861 0.7698 0.8223 0.9387 0.9844 0.5153 0.9673 0.9318 0.4625 0.9152
0.1161 1.4085 100 0.1307 0.8463 0.9335 0.9543 0.9851 0.8721 0.9433 0.9596 0.6514 0.9279
0.072 2.1127 150 0.0675 0.9075 0.9607 0.9762 0.9887 0.9179 0.9755 0.9821 0.7779 0.9625
0.0425 2.8169 200 0.0622 0.9078 0.9322 0.9781 0.9868 0.8138 0.9959 0.9838 0.7746 0.9652
0.0214 3.5211 250 0.0372 0.9458 0.9688 0.9868 0.9905 0.9223 0.9935 0.9870 0.8702 0.9802
0.0397 4.2254 300 0.0373 0.9428 0.9802 0.9858 0.9948 0.9635 0.9824 0.9892 0.8617 0.9774
0.0515 4.9296 350 0.0411 0.9399 0.9735 0.9846 0.9902 0.9438 0.9864 0.9865 0.8583 0.9750
0.0171 5.6338 400 0.0267 0.9587 0.9782 0.9898 0.9937 0.9477 0.9931 0.9900 0.9017 0.9843
0.0274 6.3380 450 0.0262 0.9621 0.9780 0.9906 0.9935 0.9454 0.9951 0.9900 0.9107 0.9857
0.0105 7.0423 500 0.0272 0.9597 0.9844 0.9900 0.9924 0.9695 0.9913 0.9898 0.9041 0.9852
0.0143 7.7465 550 0.0250 0.9638 0.9824 0.9911 0.9946 0.9593 0.9931 0.9907 0.9142 0.9865
0.0153 8.4507 600 0.0226 0.9670 0.9826 0.9918 0.9947 0.9585 0.9946 0.9909 0.9223 0.9878
0.011 9.1549 650 0.0201 0.9711 0.9841 0.9926 0.9936 0.9622 0.9965 0.9908 0.9330 0.9893
0.009 9.8592 700 0.0199 0.9707 0.9858 0.9926 0.9962 0.9676 0.9936 0.9913 0.9315 0.9891
0.017 10.5634 750 0.0206 0.9692 0.9869 0.9923 0.9964 0.9723 0.9921 0.9911 0.9279 0.9886
0.0095 11.2676 800 0.0184 0.9733 0.9870 0.9933 0.9954 0.9704 0.9950 0.9917 0.9379 0.9902
0.0142 11.9718 850 0.0179 0.9740 0.9862 0.9935 0.9957 0.9671 0.9957 0.9919 0.9395 0.9905
0.0134 12.6761 900 0.0180 0.9739 0.9882 0.9934 0.9948 0.9747 0.9951 0.9919 0.9394 0.9903
0.0096 13.3803 950 0.0179 0.9744 0.9864 0.9936 0.9960 0.9675 0.9956 0.9922 0.9406 0.9905
0.0089 14.0845 1000 0.0174 0.9744 0.9881 0.9936 0.9958 0.9737 0.9949 0.9922 0.9404 0.9908
0.0094 14.7887 1050 0.0174 0.9754 0.9864 0.9938 0.9962 0.9671 0.9960 0.9924 0.9428 0.9911
0.0089 15.4930 1100 0.0192 0.9748 0.9860 0.9935 0.9945 0.9666 0.9968 0.9918 0.9421 0.9905
0.0087 16.1972 1150 0.0179 0.9757 0.9872 0.9938 0.9959 0.9697 0.9959 0.9922 0.9437 0.9911

Framework versions

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