---
library_name: transformers
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b01-finetuned-wrinkle
  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-b01-finetuned-wrinkle

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the face-wrinkles dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0002
- eval_mean_iou: 0.0
- eval_mean_accuracy: nan
- eval_overall_accuracy: nan
- eval_accuracy_unlabeled: nan
- eval_accuracy_wrinkle: nan
- eval_iou_unlabeled: 0.0
- eval_iou_wrinkle: 0.0
- eval_runtime: 13.0305
- eval_samples_per_second: 10.053
- eval_steps_per_second: 5.065
- epoch: 2.3978
- step: 880

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3