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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-outputs
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/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
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