---
license: other
tags:
- vision
- semantic-segmentation
- generated_from_trainer
datasets:
- ds_tag1
- ds_tag2
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-2
  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-2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5766
- Mean Iou: 0.1371
- Mean Accuracy: 0.1845
- Overall Accuracy: 0.7137
- Per Category Iou: [nan, 0.4878460273549076, 0.7555073936058639, 0.0, 0.021023119916983492, 6.661075803708754e-07, nan, 0.0, 0.0, 0.0, 0.5945035814400078, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5297440422960749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7198551864914086, 0.489491922020114, 0.7904739581298643, 0.0, 0.0, 0.0, 0.0]
- Per Category Accuracy: [nan, 0.8122402393699828, 0.916307187222316, 0.0, 0.02103704204254936, 6.661204478993891e-07, nan, 0.0, 0.0, 0.0, 0.8989685161351728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8771164563053133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9407576559617489, 0.6174625729307718, 0.821407178606353, 0.0, 0.0, 0.0, 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                 | Per Category Accuracy                                                                                                                                                                                                                                                                                                                          |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 1.7378        | 0.5   | 100  | 1.8155          | 0.1247   | 0.1711        | 0.6916           | [nan, 0.46555602373647126, 0.7353888954834877, 0.0, 0.009228814643632902, 0.00010122529220478676, nan, 0.0, 0.0, 0.0, 0.5739398587525921, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5094931557231258, 0.0, 5.3423644732762526e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6692211552011066, 0.2651198767217891, 0.7633341956468725, 0.0, 0.0, 0.0, 0.0] | [nan, 0.7515868666775908, 0.9079116468110205, 0.0, 0.009233668577520995, 0.00010125030808070715, nan, 0.0, 0.0, 0.0, 0.8690972710699856, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8791886402701642, 0.0, 5.361129513400909e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9598752209264458, 0.3079896302845186, 0.7892434570182117, 0.0, 0.0, 0.0, 0.0] |
| 1.6547        | 1.0   | 200  | 1.5766          | 0.1371   | 0.1845        | 0.7137           | [nan, 0.4878460273549076, 0.7555073936058639, 0.0, 0.021023119916983492, 6.661075803708754e-07, nan, 0.0, 0.0, 0.0, 0.5945035814400078, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5297440422960749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7198551864914086, 0.489491922020114, 0.7904739581298643, 0.0, 0.0, 0.0, 0.0]                       | [nan, 0.8122402393699828, 0.916307187222316, 0.0, 0.02103704204254936, 6.661204478993891e-07, nan, 0.0, 0.0, 0.0, 0.8989685161351728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8771164563053133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9407576559617489, 0.6174625729307718, 0.821407178606353, 0.0, 0.0, 0.0, 0.0]                       |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1