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

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the as-cle-bert/breastcancer-semantic-segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2330
- Mean Iou: 0.3012
- Mean Accuracy: 0.5964
- Overall Accuracy: 0.8331
- Accuracy Background: nan
- Accuracy Benign Breast Cancer: 0.1308
- Accuracy Malignant Breast Cancer: 0.8042
- Accuracy Ignore: 0.8543
- Iou Background: 0.0
- Iou Benign Breast Cancer: 0.0749
- Iou Malignant Breast Cancer: 0.2943
- Iou Ignore: 0.8356

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Benign Breast Cancer | Accuracy Malignant Breast Cancer | Accuracy Ignore | Iou Background | Iou Benign Breast Cancer | Iou Malignant Breast Cancer | Iou Ignore |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-----------------------------:|:--------------------------------:|:---------------:|:--------------:|:------------------------:|:---------------------------:|:----------:|
| 1.1649        | 0.625 | 10   | 1.2330          | 0.3012   | 0.5964        | 0.8331           | nan                 | 0.1308                        | 0.8042                           | 0.8543          | 0.0            | 0.0749                   | 0.2943                      | 0.8356     |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cpu
- Datasets 3.0.0
- Tokenizers 0.19.1