---
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:
- eval_loss: 1.4971
- eval_mean_iou: 0.1068
- eval_mean_accuracy: 0.2327
- eval_overall_accuracy: 0.8437
- eval_accuracy_artery: nan
- eval_accuracy_vein: 0.0062
- eval_accuracy_nerve: 0.0022
- eval_accuracy_muscle1: 0.0
- eval_accuracy_muscle2: 0.9224
- eval_accuracy_muscle3: nan
- eval_accuracy_muscle4: nan
- eval_accuracy_unknown: nan
- eval_iou_artery: 0.0
- eval_iou_vein: 0.0061
- eval_iou_nerve: 0.0021
- eval_iou_muscle1: 0.0
- eval_iou_muscle2: 0.8458
- eval_iou_muscle3: 0.0
- eval_iou_muscle4: 0.0
- eval_iou_unknown: 0.0
- eval_runtime: 11.3293
- eval_samples_per_second: 1.765
- eval_steps_per_second: 0.883
- epoch: 2.25
- step: 90

## 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: 1e-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

### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1