---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-winter_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-winter_2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the johanhag/winter dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0535
- Mean Iou: 0.3344
- Mean Accuracy: 0.4082
- Overall Accuracy: 0.8584
- Accuracy Unlabeled: nan
- Accuracy Road: 0.5791
- Accuracy Side walk: 0.1367
- Accuracy Car: 0.7716
- Accuracy Pedestrian: 0.0
- Accuracy Sign: 0.0
- Accuracy Other: 0.9616
- Iou Unlabeled: nan
- Iou Road: 0.4852
- Iou Side walk: 0.1124
- Iou Car: 0.5493
- Iou Pedestrian: 0.0
- Iou Sign: 0.0
- Iou Other: 0.8593

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Road | Accuracy Side walk | Accuracy Car | Accuracy Pedestrian | Accuracy Sign | Accuracy Other | Iou Unlabeled | Iou Road | Iou Side walk | Iou Car | Iou Pedestrian | Iou Sign | Iou Other |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:------------------:|:------------:|:-------------------:|:-------------:|:--------------:|:-------------:|:--------:|:-------------:|:-------:|:--------------:|:--------:|:---------:|
| 1.5205        | 0.37  | 20   | 1.7165          | 0.2199   | 0.4062        | 0.7410           | nan                | 0.6131        | 0.0612             | 0.8217       | 0.0191              | 0.1249        | 0.7972         | 0.0           | 0.4142   | 0.0255        | 0.3422  | 0.0160         | 0.0075   | 0.7342    |
| 1.206         | 0.74  | 40   | 1.3022          | 0.2637   | 0.3793        | 0.8243           | nan                | 0.6081        | 0.1049             | 0.6391       | 0.0083              | 0.0           | 0.9156         | 0.0           | 0.4387   | 0.0670        | 0.5111  | 0.0083         | 0.0      | 0.8211    |
| 0.9859        | 1.11  | 60   | 1.1535          | 0.2756   | 0.4094        | 0.8356           | nan                | 0.6077        | 0.2204             | 0.7029       | 0.0                 | 0.0           | 0.9254         | 0.0           | 0.4650   | 0.1159        | 0.5125  | 0.0            | 0.0      | 0.8359    |
| 0.9968        | 1.48  | 80   | 1.0899          | 0.3171   | 0.3962        | 0.8452           | nan                | 0.5982        | 0.0850             | 0.7523       | 0.0                 | 0.0           | 0.9416         | nan           | 0.4673   | 0.0643        | 0.5239  | 0.0            | 0.0      | 0.8468    |
| 0.92          | 1.85  | 100  | 1.0535          | 0.3344   | 0.4082        | 0.8584           | nan                | 0.5791        | 0.1367             | 0.7716       | 0.0                 | 0.0           | 0.9616         | nan           | 0.4852   | 0.1124        | 0.5493  | 0.0            | 0.0      | 0.8593    |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0