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---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-rwymarkings-3k-steps
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-finetuned-rwymarkings-3k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Spatiallysaying/rwymarkings dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0182
- Mean Iou: 0.0441
- Mean Accuracy: 0.0510
- Overall Accuracy: 0.0800
- Accuracy Backgound : nan
- Accuracy Tdz: 0.0908
- Accuracy Aim: 0.2203
- Accuracy Desig: 0.0
- Accuracy Rwythr: 0.0971
- Accuracy Thrbar: 0.0
- Accuracy Disp: 0.0
- Accuracy Chevron: 0.0
- Accuracy Arrow: 0.0
- Iou Backgound : 0.0
- Iou Tdz: 0.0818
- Iou Aim: 0.2189
- Iou Desig: 0.0
- Iou Rwythr: 0.0958
- Iou Thrbar: 0.0
- Iou Disp: 0.0
- Iou Chevron: 0.0
- Iou Arrow: 0.0
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Backgound | Accuracy Tdz | Accuracy Aim | Accuracy Desig | Accuracy Rwythr | Accuracy Thrbar | Accuracy Disp | Accuracy Chevron | Accuracy Arrow | Iou Backgound | Iou Tdz | Iou Aim | Iou Desig | Iou Rwythr | Iou Thrbar | Iou Disp | Iou Chevron | Iou Arrow |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:------------:|:------------:|:--------------:|:---------------:|:---------------:|:-------------:|:----------------:|:--------------:|:---------------:|:-------:|:-------:|:---------:|:----------:|:----------:|:--------:|:-----------:|:---------:|
| 1.6294 | 1.0 | 173 | 0.5448 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.3371 | 2.0 | 346 | 0.1107 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0724 | 3.0 | 519 | 0.0483 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0508 | 4.0 | 692 | 0.0331 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0369 | 5.0 | 865 | 0.0289 | 0.0002 | 0.0002 | 0.0004 | nan | 0.0 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0272 | 6.0 | 1038 | 0.0276 | 0.0106 | 0.0120 | 0.0195 | nan | 0.0107 | 0.0853 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0105 | 0.0845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0258 | 7.0 | 1211 | 0.0233 | 0.0066 | 0.0075 | 0.0122 | nan | 0.0118 | 0.0480 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0117 | 0.0480 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0235 | 8.0 | 1384 | 0.0221 | 0.0150 | 0.0171 | 0.0277 | nan | 0.0233 | 0.1108 | 0.0 | 0.0024 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0224 | 0.1107 | 0.0 | 0.0024 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0213 | 9.0 | 1557 | 0.0209 | 0.0177 | 0.0200 | 0.0326 | nan | 0.0237 | 0.1351 | 0.0 | 0.0016 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0231 | 0.1346 | 0.0 | 0.0016 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0201 | 10.0 | 1730 | 0.0206 | 0.0277 | 0.0318 | 0.0512 | nan | 0.0595 | 0.1734 | 0.0 | 0.0211 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0559 | 0.1726 | 0.0 | 0.0211 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0203 | 11.0 | 1903 | 0.0198 | 0.0246 | 0.0281 | 0.0450 | nan | 0.0463 | 0.1512 | 0.0 | 0.0277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0432 | 0.1505 | 0.0 | 0.0277 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0172 | 12.0 | 2076 | 0.0192 | 0.0377 | 0.0435 | 0.0690 | nan | 0.0744 | 0.2145 | 0.0 | 0.0592 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0680 | 0.2119 | 0.0 | 0.0589 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0168 | 13.0 | 2249 | 0.0189 | 0.0331 | 0.0381 | 0.0607 | nan | 0.0704 | 0.1884 | 0.0 | 0.0462 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0645 | 0.1876 | 0.0 | 0.0461 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0169 | 14.0 | 2422 | 0.0185 | 0.0383 | 0.0442 | 0.0701 | nan | 0.0786 | 0.2124 | 0.0 | 0.0628 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0716 | 0.2112 | 0.0 | 0.0623 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0172 | 15.0 | 2595 | 0.0184 | 0.0476 | 0.0551 | 0.0864 | nan | 0.0917 | 0.2463 | 0.0 | 0.1028 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0830 | 0.2443 | 0.0 | 0.1013 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0159 | 16.0 | 2768 | 0.0182 | 0.0523 | 0.0615 | 0.0964 | nan | 0.1202 | 0.2493 | 0.0 | 0.1225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1044 | 0.2468 | 0.0 | 0.1199 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0163 | 17.0 | 2941 | 0.0181 | 0.0492 | 0.0571 | 0.0892 | nan | 0.0987 | 0.2414 | 0.0 | 0.1167 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0885 | 0.2397 | 0.0 | 0.1146 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0152 | 17.3410 | 3000 | 0.0182 | 0.0441 | 0.0510 | 0.0800 | nan | 0.0908 | 0.2203 | 0.0 | 0.0971 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0818 | 0.2189 | 0.0 | 0.0958 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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