---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-rwymarkings-2-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-2-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: 2.2162
- Mean Iou: 0.0387
- Mean Accuracy: 0.1129
- Overall Accuracy: 0.1050
- Accuracy  Backgound : nan
- Accuracy Tdz: 0.0493
- Accuracy Aim: 0.2144
- Accuracy Desig: 0.0922
- Accuracy Rwythr: 0.1765
- Accuracy Thrbar: 0.0140
- Accuracy Disp: 0.2710
- Accuracy Chevron: 0.0023
- Accuracy Arrow: 0.0834
- Iou  Backgound : 0.0
- Iou Tdz: 0.0399
- Iou Aim: 0.1158
- Iou Desig: 0.0443
- Iou Rwythr: 0.0980
- Iou Thrbar: 0.0131
- Iou Disp: 0.0266
- Iou Chevron: 0.0020
- Iou Arrow: 0.0085

## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 2

### 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:------------:|:------------:|:--------------:|:---------------:|:---------------:|:-------------:|:----------------:|:--------------:|:---------------:|:-------:|:-------:|:---------:|:----------:|:----------:|:--------:|:-----------:|:---------:|
| 2.2475        | 0.0455 | 2    | 2.2162          | 0.0387   | 0.1129        | 0.1050           | nan                  | 0.0493       | 0.2144       | 0.0922         | 0.1765          | 0.0140          | 0.2710        | 0.0023           | 0.0834         | 0.0             | 0.0399  | 0.1158  | 0.0443    | 0.0980     | 0.0131     | 0.0266   | 0.0020      | 0.0085    |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1