---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-v-mesh-0
  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-v-mesh-0

This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the Onegafer/vehicle_segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3350
- Mean Iou: 0.3373
- Mean Accuracy: 0.6746
- Overall Accuracy: 0.6746
- Accuracy Background: nan
- Accuracy Windows: 0.6746
- Iou Background: 0.0
- Iou Windows: 0.6746

## 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: 0.2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Windows | Iou Background | Iou Windows |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.4394        | 0.16  | 20   | 0.3350          | 0.3373   | 0.6746        | 0.6746           | nan                 | 0.6746           | 0.0            | 0.6746      |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3