---
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: 0.2387
- Mean Iou: 0.8160
- Mean Accuracy: 0.8955
- Overall Accuracy: 0.9070
- Accuracy Background: 0.9351
- Accuracy Target: 0.8559
- Iou Background: 0.8665
- Iou Target: 0.7655

## 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: 4
- eval_batch_size: 4
- 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Target | Iou Background | Iou Target |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.2223        | 1.0   | 51   | 0.2299          | 0.8346   | 0.9082        | 0.9123           | 0.9335              | 0.8830          | 0.8608         | 0.8085     |
| 0.1991        | 2.0   | 102  | 0.2313          | 0.8371   | 0.9103        | 0.9136           | 0.9307              | 0.8900          | 0.8622         | 0.8120     |
| 0.1905        | 3.0   | 153  | 0.2269          | 0.8398   | 0.9112        | 0.9153           | 0.9368              | 0.8856          | 0.8653         | 0.8143     |
| 0.2218        | 4.0   | 204  | 0.2287          | 0.8407   | 0.9119        | 0.9158           | 0.9361              | 0.8877          | 0.8659         | 0.8155     |
| 0.2145        | 5.0   | 255  | 0.2275          | 0.8397   | 0.9125        | 0.9150           | 0.9279              | 0.8971          | 0.8637         | 0.8156     |
| 0.1905        | 6.0   | 306  | 0.2301          | 0.8395   | 0.9108        | 0.9152           | 0.9383              | 0.8832          | 0.8654         | 0.8137     |
| 0.2056        | 7.0   | 357  | 0.2278          | 0.8413   | 0.9116        | 0.9163           | 0.9410              | 0.8821          | 0.8672         | 0.8155     |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3