---
library_name: transformers
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-segments-sidewalk-finetuned
  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-segments-sidewalk-finetuned

This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2634
- Mean Iou: 0.4383
- Mean Accuracy: 0.8765
- Overall Accuracy: 0.8765
- Accuracy Background: nan
- Accuracy Target: 0.8765
- Iou Background: 0.0
- Iou Target: 0.8765

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Target | Iou Background | Iou Target |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.4747        | 1.0   | 26   | 0.3768          | 0.4287   | 0.8573        | 0.8573           | nan                 | 0.8573          | 0.0            | 0.8573     |
| 0.3193        | 2.0   | 52   | 0.3138          | 0.4198   | 0.8395        | 0.8395           | nan                 | 0.8395          | 0.0            | 0.8395     |
| 0.2792        | 3.0   | 78   | 0.2899          | 0.4318   | 0.8636        | 0.8636           | nan                 | 0.8636          | 0.0            | 0.8636     |
| 0.2569        | 4.0   | 104  | 0.2723          | 0.4202   | 0.8405        | 0.8405           | nan                 | 0.8405          | 0.0            | 0.8405     |
| 0.2504        | 5.0   | 130  | 0.2634          | 0.4383   | 0.8765        | 0.8765           | nan                 | 0.8765          | 0.0            | 0.8765     |
| 0.2294        | 6.0   | 156  | 0.2572          | 0.4292   | 0.8584        | 0.8584           | nan                 | 0.8584          | 0.0            | 0.8584     |
| 0.2337        | 7.0   | 182  | 0.2567          | 0.4292   | 0.8584        | 0.8584           | nan                 | 0.8584          | 0.0            | 0.8584     |
| 0.2255        | 8.0   | 208  | 0.2546          | 0.4354   | 0.8707        | 0.8707           | nan                 | 0.8707          | 0.0            | 0.8707     |
| 0.2213        | 9.0   | 234  | 0.2557          | 0.4299   | 0.8597        | 0.8597           | nan                 | 0.8597          | 0.0            | 0.8597     |
| 0.2203        | 10.0  | 260  | 0.2552          | 0.4372   | 0.8744        | 0.8744           | nan                 | 0.8744          | 0.0            | 0.8744     |


### Framework versions

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