---
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: 1.6449
- Mean Iou: 0.1548
- Mean Accuracy: 0.2076
- Overall Accuracy: 0.7095
- Per Category Iou: [nan, 0.5140599138533896, 0.7174504614949924, 0.0, 0.2891488100731331, 0.0017519579090739337, nan, 2.896471421964715e-05, 0.0, 0.0, 0.572608873148249, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.5022343403146414, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7363557838688449, 0.5136205023614682, 0.7964013451546083, 0.0, 0.0, 0.0, 0.0]
- Per Category Accuracy: [nan, 0.8064241561480351, 0.9062036975406429, 0.0, 0.30153947289328054, 0.0017733699866858271, nan, 2.8972126882463944e-05, 0.0, 0.0, 0.8850507869466231, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8639780836322506, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9144389240012535, 0.668388685537115, 0.8810818896148822, 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                                                                                                                                                                                                                                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 1.9382        | 0.5   | 100  | 1.8048          | 0.1339   | 0.1853        | 0.6789           | [nan, 0.4752344017185758, 0.694067134540047, 0.0, 0.12409993164299513, 0.0008311245506368295, nan, 0.0013254481219605065, 0.0, 0.0, 0.5409277406718473, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.4841581403327513, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.690024139245331, 0.2320795469313362, 0.7749690806204844, 0.0, 0.0, 0.0, 0.0] | [nan, 0.7639758158865736, 0.8976245985998512, 0.0, 0.12474373026419283, 0.0008415168706412761, nan, 0.0013278891487795974, 0.0, 0.0, 0.880573362170307, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8526119656818829, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9393214014210579, 0.26355731572405905, 0.8357039981550286, 0.0, 0.0, 0.0, 0.0] |
| 1.5173        | 1.0   | 200  | 1.6449          | 0.1548   | 0.2076        | 0.7095           | [nan, 0.5140599138533896, 0.7174504614949924, 0.0, 0.2891488100731331, 0.0017519579090739337, nan, 2.896471421964715e-05, 0.0, 0.0, 0.572608873148249, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.5022343403146414, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7363557838688449, 0.5136205023614682, 0.7964013451546083, 0.0, 0.0, 0.0, 0.0] | [nan, 0.8064241561480351, 0.9062036975406429, 0.0, 0.30153947289328054, 0.0017733699866858271, nan, 2.8972126882463944e-05, 0.0, 0.0, 0.8850507869466231, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8639780836322506, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9144389240012535, 0.668388685537115, 0.8810818896148822, 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