---
license: other
base_model: nvidia/mit-b4
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-oct-22
  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-oct-22

This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0243
- Mean Iou: 0.9582
- Mean Accuracy: 0.9792
- Overall Accuracy: 0.9965
- Accuracy Unlabeled: 0.9981
- Accuracy Numero: 0.9603
- Iou Unlabeled: 0.9963
- Iou Numero: 0.9200

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Numero | Iou Unlabeled | Iou Numero |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:----------:|
| 0.1406        | 5.0   | 20   | 0.1672          | 0.7389   | 0.7497        | 0.9790           | 1.0000             | 0.4994          | 0.9785        | 0.4993     |
| 0.045         | 10.0  | 40   | 0.0498          | 0.9398   | 0.9476        | 0.9951           | 0.9994             | 0.8958          | 0.9949        | 0.8846     |
| 0.0361        | 15.0  | 60   | 0.0296          | 0.9575   | 0.9811        | 0.9964           | 0.9978             | 0.9643          | 0.9963        | 0.9187     |
| 0.026         | 20.0  | 80   | 0.0243          | 0.9582   | 0.9792        | 0.9965           | 0.9981             | 0.9603          | 0.9963        | 0.9200     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0