---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-oct-22
  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-oct-22

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the maratuly/Pseudo-echo dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3463
- Mean Iou: 0.7622
- Mean Accuracy: 0.9528
- Overall Accuracy: 0.9581
- Accuracy Unlabeled: nan
- Accuracy Lv: 0.9931
- Accuracy Rv: 0.9354
- Accuracy La: 0.9533
- Accuracy Ra: 0.9293
- Iou Unlabeled: 0.0
- Iou Lv: 0.9931
- Iou Rv: 0.9354
- Iou La: 0.9533
- Iou Ra: 0.9293

## 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 Lv | Accuracy Rv | Accuracy La | Accuracy Ra | Iou Unlabeled | Iou Lv | Iou Rv | Iou La | Iou Ra |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:|
| 1.0046        | 5.0   | 20   | 1.3540          | 0.4931   | 0.7263        | 0.7606           | nan                | 0.9742      | 0.6789      | 0.5862      | 0.6657      | 0.0           | 0.6545 | 0.6789 | 0.5540 | 0.5783 |
| 0.7304        | 10.0  | 40   | 0.8262          | 0.6901   | 0.8908        | 0.9017           | nan                | 0.9906      | 0.8147      | 0.9192      | 0.8387      | 0.0           | 0.9028 | 0.8145 | 0.9046 | 0.8287 |
| 0.5957        | 15.0  | 60   | 0.5683          | 0.7395   | 0.9277        | 0.9367           | nan                | 0.9896      | 0.9141      | 0.9225      | 0.8845      | 0.0           | 0.9766 | 0.9141 | 0.9225 | 0.8845 |
| 0.5094        | 20.0  | 80   | 0.4881          | 0.7541   | 0.9426        | 0.9499           | nan                | 0.9909      | 0.9368      | 0.9312      | 0.9116      | 0.0           | 0.9909 | 0.9368 | 0.9312 | 0.9116 |
| 0.4681        | 25.0  | 100  | 0.4384          | 0.7689   | 0.9612        | 0.9653           | nan                | 0.9942      | 0.9434      | 0.9650      | 0.9421      | 0.0           | 0.9942 | 0.9434 | 0.9650 | 0.9421 |
| 0.4045        | 30.0  | 120  | 0.4078          | 0.7639   | 0.9549        | 0.9602           | nan                | 0.9933      | 0.9418      | 0.9519      | 0.9328      | 0.0           | 0.9933 | 0.9418 | 0.9519 | 0.9328 |
| 0.3956        | 35.0  | 140  | 0.3844          | 0.7664   | 0.9580        | 0.9625           | nan                | 0.9939      | 0.9406      | 0.9551      | 0.9425      | 0.0           | 0.9939 | 0.9406 | 0.9551 | 0.9425 |
| 0.3736        | 40.0  | 160  | 0.3736          | 0.7687   | 0.9609        | 0.9652           | nan                | 0.9961      | 0.9409      | 0.9631      | 0.9436      | 0.0           | 0.9961 | 0.9409 | 0.9631 | 0.9436 |
| 0.3431        | 45.0  | 180  | 0.3528          | 0.7622   | 0.9528        | 0.9577           | nan                | 0.9923      | 0.9321      | 0.9539      | 0.9327      | 0.0           | 0.9923 | 0.9321 | 0.9539 | 0.9327 |
| 0.3428        | 50.0  | 200  | 0.3463          | 0.7622   | 0.9528        | 0.9581           | nan                | 0.9931      | 0.9354      | 0.9533      | 0.9293      | 0.0           | 0.9931 | 0.9354 | 0.9533 | 0.9293 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0