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segformer-b0-finetuned-segments-sidewalk-outputs

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metadata
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-segments-sidewalk-outputs
    results: []

segformer-b0-finetuned-segments-sidewalk-outputs

This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0650
  • Mean Iou: 0.5841
  • Mean Accuracy: 0.8778
  • Overall Accuracy: 0.9588
  • Accuracy Background: nan
  • Accuracy Ground: 0.9811
  • Accuracy Pallet: 0.7744
  • Iou Background: 0.0
  • Iou Ground: 0.9805
  • Iou Pallet: 0.7717

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Ground Accuracy Pallet Iou Background Iou Ground Iou Pallet
0.0421 0.1471 20 0.0709 0.5844 0.8798 0.9604 nan 0.9826 0.7771 0.0 0.9808 0.7724
0.0276 0.2941 40 0.0624 0.6015 0.9042 0.9646 nan 0.9813 0.8271 0.0 0.9809 0.8236
0.0211 0.4412 60 0.0691 0.5664 0.8509 0.9552 nan 0.9840 0.7179 0.0 0.9835 0.7157
0.0235 0.5882 80 0.0621 0.5617 0.8437 0.9565 nan 0.9876 0.6998 0.0 0.9866 0.6984
0.0271 0.7353 100 0.0602 0.5877 0.8832 0.9567 nan 0.9770 0.7893 0.0 0.9767 0.7863
0.024 0.8824 120 0.0682 0.5923 0.8911 0.9600 nan 0.9790 0.8033 0.0 0.9785 0.7985
0.0407 1.0294 140 0.0691 0.5892 0.8859 0.9580 nan 0.9779 0.7940 0.0 0.9773 0.7903
0.0277 1.1765 160 0.0683 0.5788 0.8697 0.9571 nan 0.9812 0.7582 0.0 0.9804 0.7560
0.017 1.3235 180 0.0679 0.5845 0.8789 0.9566 nan 0.9780 0.7798 0.0 0.9770 0.7764
0.0275 1.4706 200 0.0634 0.5992 0.9014 0.9639 nan 0.9812 0.8216 0.0 0.9800 0.8175
0.0122 1.6176 220 0.0602 0.5859 0.8813 0.9590 nan 0.9804 0.7821 0.0 0.9794 0.7782
0.0149 1.7647 240 0.0662 0.5827 0.8757 0.9591 nan 0.9820 0.7694 0.0 0.9817 0.7664
0.0169 1.9118 260 0.0628 0.5994 0.9019 0.9614 nan 0.9778 0.8259 0.0 0.9776 0.8205
0.0324 2.0588 280 0.0677 0.5859 0.8809 0.9584 nan 0.9798 0.7819 0.0 0.9792 0.7785
0.0229 2.2059 300 0.0693 0.5983 0.9003 0.9619 nan 0.9789 0.8217 0.0 0.9784 0.8166
0.0204 2.3529 320 0.0729 0.5850 0.8792 0.9586 nan 0.9805 0.7780 0.0 0.9800 0.7749
0.0102 2.5 340 0.0655 0.5899 0.8868 0.9603 nan 0.9806 0.7931 0.0 0.9802 0.7895
0.0235 2.6471 360 0.0682 0.5781 0.8688 0.9556 nan 0.9795 0.7580 0.0 0.9789 0.7552
0.0239 2.7941 380 0.0633 0.5961 0.8965 0.9615 nan 0.9794 0.8136 0.0 0.9789 0.8093
0.0305 2.9412 400 0.0593 0.5832 0.8764 0.9593 nan 0.9822 0.7706 0.0 0.9817 0.7678
0.0183 3.0882 420 0.0600 0.5867 0.8816 0.9613 nan 0.9832 0.7799 0.0 0.9827 0.7775
0.031 3.2353 440 0.0612 0.5933 0.8917 0.9614 nan 0.9806 0.8029 0.0 0.9799 0.8000
0.0174 3.3824 460 0.0645 0.5836 0.8769 0.9590 nan 0.9816 0.7722 0.0 0.9811 0.7696
0.0456 3.5294 480 0.0651 0.5770 0.8669 0.9577 nan 0.9827 0.7512 0.0 0.9821 0.7489
0.0187 3.6765 500 0.0659 0.5831 0.8765 0.9578 nan 0.9803 0.7727 0.0 0.9798 0.7695
0.0329 3.8235 520 0.0690 0.5787 0.8697 0.9568 nan 0.9808 0.7587 0.0 0.9801 0.7560
0.0241 3.9706 540 0.0651 0.5847 0.8789 0.9584 nan 0.9803 0.7774 0.0 0.9798 0.7743
0.0304 4.1176 560 0.0652 0.5871 0.8823 0.9589 nan 0.9800 0.7846 0.0 0.9795 0.7817
0.0086 4.2647 580 0.0662 0.5851 0.8793 0.9584 nan 0.9802 0.7784 0.0 0.9797 0.7756
0.0194 4.4118 600 0.0678 0.5889 0.8853 0.9581 nan 0.9781 0.7925 0.0 0.9777 0.7890
0.0114 4.5588 620 0.0664 0.5877 0.8834 0.9582 nan 0.9789 0.7880 0.0 0.9784 0.7847
0.0183 4.7059 640 0.0663 0.5843 0.8782 0.9571 nan 0.9789 0.7776 0.0 0.9784 0.7744
0.0139 4.8529 660 0.0652 0.5872 0.8826 0.9590 nan 0.9801 0.7852 0.0 0.9796 0.7820
0.0197 5.0 680 0.0650 0.5841 0.8778 0.9588 nan 0.9811 0.7744 0.0 0.9805 0.7717

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3