---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-biofilm_MRCNNv1_halfjoin
  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-finetuned-biofilm_MRCNNv1_halfjoin

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the heroza/biofilm_MRCNNv1_halfjoin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0208
- Mean Iou: 0.4961
- Mean Accuracy: 0.9923
- Overall Accuracy: 0.9923
- Accuracy Background: 0.9923
- Accuracy Biofilm: nan
- Iou Background: 0.9923
- Iou Biofilm: 0.0

## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Biofilm | Iou Background | Iou Biofilm |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.0713        | 1.0   | 478   | 0.0381          | 0.4953   | 0.9906        | 0.9906           | 0.9906              | nan              | 0.9906         | 0.0         |
| 0.044         | 2.0   | 956   | 0.0202          | 0.4975   | 0.9949        | 0.9949           | 0.9949              | nan              | 0.9949         | 0.0         |
| 0.041         | 3.0   | 1434  | 0.0181          | 0.4972   | 0.9945        | 0.9945           | 0.9945              | nan              | 0.9945         | 0.0         |
| 0.0361        | 4.0   | 1912  | 0.0203          | 0.4963   | 0.9926        | 0.9926           | 0.9926              | nan              | 0.9926         | 0.0         |
| 0.0357        | 5.0   | 2390  | 0.0163          | 0.4971   | 0.9942        | 0.9942           | 0.9942              | nan              | 0.9942         | 0.0         |
| 0.0336        | 6.0   | 2868  | 0.0340          | 0.4958   | 0.9915        | 0.9915           | 0.9915              | nan              | 0.9915         | 0.0         |
| 0.0295        | 7.0   | 3346  | 0.0126          | 0.4978   | 0.9955        | 0.9955           | 0.9955              | nan              | 0.9955         | 0.0         |
| 0.0251        | 8.0   | 3824  | 0.0220          | 0.4957   | 0.9915        | 0.9915           | 0.9915              | nan              | 0.9915         | 0.0         |
| 0.0265        | 9.0   | 4302  | 0.0182          | 0.4966   | 0.9933        | 0.9933           | 0.9933              | nan              | 0.9933         | 0.0         |
| 0.0238        | 10.0  | 4780  | 0.0155          | 0.4970   | 0.9940        | 0.9940           | 0.9940              | nan              | 0.9940         | 0.0         |
| 0.0258        | 11.0  | 5258  | 0.0181          | 0.4966   | 0.9931        | 0.9931           | 0.9931              | nan              | 0.9931         | 0.0         |
| 0.0264        | 12.0  | 5736  | 0.0179          | 0.4969   | 0.9938        | 0.9938           | 0.9938              | nan              | 0.9938         | 0.0         |
| 0.0265        | 13.0  | 6214  | 0.0222          | 0.4959   | 0.9917        | 0.9917           | 0.9917              | nan              | 0.9917         | 0.0         |
| 0.0219        | 14.0  | 6692  | 0.0200          | 0.4962   | 0.9925        | 0.9925           | 0.9925              | nan              | 0.9925         | 0.0         |
| 0.0213        | 15.0  | 7170  | 0.0234          | 0.4958   | 0.9916        | 0.9916           | 0.9916              | nan              | 0.9916         | 0.0         |
| 0.0192        | 16.0  | 7648  | 0.0199          | 0.4961   | 0.9922        | 0.9922           | 0.9922              | nan              | 0.9922         | 0.0         |
| 0.0232        | 17.0  | 8126  | 0.0208          | 0.4961   | 0.9923        | 0.9923           | 0.9923              | nan              | 0.9923         | 0.0         |
| 0.0219        | 18.0  | 8604  | 0.0245          | 0.4955   | 0.9909        | 0.9909           | 0.9909              | nan              | 0.9909         | 0.0         |
| 0.0201        | 19.0  | 9082  | 0.0211          | 0.4961   | 0.9922        | 0.9922           | 0.9922              | nan              | 0.9922         | 0.0         |
| 0.0192        | 20.0  | 9560  | 0.0207          | 0.4962   | 0.9923        | 0.9923           | 0.9923              | nan              | 0.9923         | 0.0         |
| 0.0175        | 20.92 | 10000 | 0.0208          | 0.4961   | 0.9923        | 0.9923           | 0.9923              | nan              | 0.9923         | 0.0         |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.14.4
- Tokenizers 0.15.1