---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-v0
  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-v0

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the tontokoton/artery-ultrasound-siit dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5847
- Mean Iou: 0.1252
- Mean Accuracy: 0.2052
- Overall Accuracy: 0.2652
- Accuracy Artery: nan
- Accuracy Vein: 0.2256
- Accuracy Nerve: 0.0
- Accuracy Muscle1: 0.0
- Accuracy Muscle2: 0.3266
- Accuracy Muscle3: 0.0009
- Accuracy Muscle4: 0.6780
- Accuracy Unknown: nan
- Iou Artery: 0.0
- Iou Vein: 0.2256
- Iou Nerve: 0.0
- Iou Muscle1: 0.0
- Iou Muscle2: 0.2135
- Iou Muscle3: 0.0005
- Iou Muscle4: 0.4366
- Iou Unknown: nan

## 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 Artery | Accuracy Vein | Accuracy Nerve | Accuracy Muscle1 | Accuracy Muscle2 | Accuracy Muscle3 | Accuracy Muscle4 | Accuracy Unknown | Iou Artery | Iou Vein | Iou Nerve | Iou Muscle1 | Iou Muscle2 | Iou Muscle3 | Iou Muscle4 | Iou Unknown |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------:|:-------------:|:--------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------:|:--------:|:---------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 1.7835        | 10.0  | 10   | 1.9744          | 0.0620   | 0.1572        | 0.2117           | nan             | 0.0834        | 0.0            | 0.0              | 0.2330           | 0.0002           | 0.6266           | nan              | 0.0        | 0.0563   | 0.0       | 0.0         | 0.1422      | 0.0001      | 0.2975      | 0.0         |
| 1.4779        | 20.0  | 20   | 1.8578          | 0.0954   | 0.1794        | 0.2576           | nan             | 0.0670        | 0.0            | 0.0              | 0.3099           | 0.0              | 0.6992           | nan              | 0.0        | 0.0670   | 0.0       | 0.0         | 0.1857      | 0.0         | 0.4152      | nan         |
| 1.3434        | 30.0  | 30   | 1.6759          | 0.1239   | 0.2014        | 0.2743           | nan             | 0.1921        | 0.0            | 0.0              | 0.3566           | 0.0              | 0.6594           | nan              | 0.0        | 0.1921   | 0.0       | 0.0         | 0.2244      | 0.0         | 0.4509      | nan         |
| 1.2779        | 40.0  | 40   | 1.6276          | 0.1375   | 0.2263        | 0.3176           | nan             | 0.2159        | 0.0            | 0.0              | 0.4348           | 0.0              | 0.7073           | nan              | 0.0        | 0.2159   | 0.0       | 0.0         | 0.2784      | 0.0         | 0.4684      | nan         |
| 1.1988        | 50.0  | 50   | 1.5847          | 0.1252   | 0.2052        | 0.2652           | nan             | 0.2256        | 0.0            | 0.0              | 0.3266           | 0.0009           | 0.6780           | nan              | 0.0        | 0.2256   | 0.0       | 0.0         | 0.2135      | 0.0005      | 0.4366      | nan         |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3