transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-winter
  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-winter

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the johanhag/winter-test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1441
- Mean Iou: 0.8861
- Mean Accuracy: 0.9456
- Overall Accuracy: 0.9660
- Accuracy Unlabeled: nan
- Accuracy Object: nan
- Accuracy Road: 0.9769
- Accuracy Side walk: 0.8930
- Accuracy Car: 0.9347
- Accuracy Pedestrian: nan
- Accuracy Other: 0.9779
- Iou Unlabeled: nan
- Iou Object: nan
- Iou Road: 0.9197
- Iou Side walk: 0.8250
- Iou Car: 0.8319
- Iou Pedestrian: nan
- Iou Other: 0.9678

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Object | Accuracy Road | Accuracy Side walk | Accuracy Car | Accuracy Pedestrian | Accuracy Other | Iou Unlabeled | Iou Object | Iou Road | Iou Side walk | Iou Car | Iou Pedestrian | Iou Other |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:------------------:|:------------:|:-------------------:|:--------------:|:-------------:|:----------:|:--------:|:-------------:|:-------:|:--------------:|:---------:|
| 0.2207        | 4.0   | 20   | 0.2300          | 0.8576   | 0.9439        | 0.9583           | nan                | nan             | 0.9738        | 0.8767             | 0.9564       | nan                 | 0.9686         | nan           | nan        | 0.9018   | 0.8000        | 0.7670  | nan            | 0.9617    |
| 0.1792        | 8.0   | 40   | 0.2126          | 0.8696   | 0.9457        | 0.9614           | nan                | nan             | 0.9768        | 0.8911             | 0.9444       | nan                 | 0.9706         | nan           | nan        | 0.9106   | 0.8122        | 0.7924  | nan            | 0.9633    |
| 0.1527        | 12.0  | 60   | 0.1869          | 0.8769   | 0.9470        | 0.9634           | nan                | nan             | 0.9776        | 0.9023             | 0.9364       | nan                 | 0.9718         | nan           | nan        | 0.9180   | 0.8165        | 0.8085  | nan            | 0.9647    |
| 0.1329        | 16.0  | 80   | 0.1787          | 0.8783   | 0.9429        | 0.9634           | nan                | nan             | 0.9772        | 0.8880             | 0.9314       | nan                 | 0.9749         | nan           | nan        | 0.9126   | 0.8117        | 0.8229  | nan            | 0.9661    |
| 0.1746        | 20.0  | 100  | 0.1651          | 0.8864   | 0.9511        | 0.9668           | nan                | nan             | 0.9771        | 0.9126             | 0.9395       | nan                 | 0.9751         | nan           | nan        | 0.9258   | 0.8369        | 0.8157  | nan            | 0.9671    |
| 0.1218        | 24.0  | 120  | 0.1652          | 0.8798   | 0.9444        | 0.9643           | nan                | nan             | 0.9791        | 0.8858             | 0.9370       | nan                 | 0.9757         | nan           | nan        | 0.9140   | 0.8156        | 0.8224  | nan            | 0.9673    |
| 0.0816        | 28.0  | 140  | 0.1473          | 0.8921   | 0.9521        | 0.9684           | nan                | nan             | 0.9723        | 0.9199             | 0.9383       | nan                 | 0.9780         | nan           | nan        | 0.9299   | 0.8478        | 0.8231  | nan            | 0.9676    |
| 0.0893        | 32.0  | 160  | 0.1490          | 0.8892   | 0.9502        | 0.9672           | nan                | nan             | 0.9749        | 0.9140             | 0.9354       | nan                 | 0.9766         | nan           | nan        | 0.9260   | 0.8384        | 0.825   | nan            | 0.9673    |
| 0.0849        | 36.0  | 180  | 0.1517          | 0.8861   | 0.9476        | 0.9660           | nan                | nan             | 0.9791        | 0.8987             | 0.9367       | nan                 | 0.9760         | nan           | nan        | 0.9205   | 0.8258        | 0.8308  | nan            | 0.9674    |
| 0.1625        | 40.0  | 200  | 0.1519          | 0.8843   | 0.9468        | 0.9654           | nan                | nan             | 0.9777        | 0.8938             | 0.9394       | nan                 | 0.9763         | nan           | nan        | 0.9176   | 0.8235        | 0.8289  | nan            | 0.9673    |
| 0.1396        | 44.0  | 220  | 0.1500          | 0.8850   | 0.9476        | 0.9655           | nan                | nan             | 0.9791        | 0.8949             | 0.9408       | nan                 | 0.9757         | nan           | nan        | 0.9187   | 0.8223        | 0.8317  | nan            | 0.9674    |
| 0.0931        | 48.0  | 240  | 0.1441          | 0.8861   | 0.9456        | 0.9660           | nan                | nan             | 0.9769        | 0.8930             | 0.9347       | nan                 | 0.9779         | nan           | nan        | 0.9197   | 0.8250        | 0.8319  | nan            | 0.9678    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
