transformers tensorboard safetensors segformer image-segmentation vision generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-biofilm2
  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-biofilm2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the heroza/biofilm2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0797
- Mean Iou: 0.4786
- Mean Accuracy: 0.9572
- Overall Accuracy: 0.9572
- Accuracy Background: nan
- Accuracy Biofilm: 0.9572
- Iou Background: 0.0
- Iou Biofilm: 0.9572

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.1622        | 1.0   | 280   | 0.1158          | 0.4714   | 0.9428        | 0.9428           | nan                 | 0.9428           | 0.0            | 0.9428      |
| 0.0742        | 2.0   | 560   | 0.0643          | 0.4545   | 0.9090        | 0.9090           | nan                 | 0.9090           | 0.0            | 0.9090      |
| 0.0549        | 3.0   | 840   | 0.0582          | 0.4797   | 0.9594        | 0.9594           | nan                 | 0.9594           | 0.0            | 0.9594      |
| 0.0459        | 4.0   | 1120  | 0.0508          | 0.4737   | 0.9475        | 0.9475           | nan                 | 0.9475           | 0.0            | 0.9475      |
| 0.0506        | 5.0   | 1400  | 0.0405          | 0.4705   | 0.9411        | 0.9411           | nan                 | 0.9411           | 0.0            | 0.9411      |
| 0.0411        | 6.0   | 1680  | 0.0476          | 0.4865   | 0.9729        | 0.9729           | nan                 | 0.9729           | 0.0            | 0.9729      |
| 0.0456        | 7.0   | 1960  | 0.0476          | 0.4754   | 0.9509        | 0.9509           | nan                 | 0.9509           | 0.0            | 0.9509      |
| 0.0381        | 8.0   | 2240  | 0.0554          | 0.4792   | 0.9584        | 0.9584           | nan                 | 0.9584           | 0.0            | 0.9584      |
| 0.0348        | 9.0   | 2520  | 0.0559          | 0.4889   | 0.9779        | 0.9779           | nan                 | 0.9779           | 0.0            | 0.9779      |
| 0.0388        | 10.0  | 2800  | 0.0513          | 0.4757   | 0.9514        | 0.9514           | nan                 | 0.9514           | 0.0            | 0.9514      |
| 0.0385        | 11.0  | 3080  | 0.0660          | 0.4883   | 0.9767        | 0.9767           | nan                 | 0.9767           | 0.0            | 0.9767      |
| 0.0309        | 12.0  | 3360  | 0.0589          | 0.4808   | 0.9616        | 0.9616           | nan                 | 0.9616           | 0.0            | 0.9616      |
| 0.0322        | 13.0  | 3640  | 0.0539          | 0.4796   | 0.9592        | 0.9592           | nan                 | 0.9592           | 0.0            | 0.9592      |
| 0.0361        | 14.0  | 3920  | 0.0621          | 0.4812   | 0.9625        | 0.9625           | nan                 | 0.9625           | 0.0            | 0.9625      |
| 0.0277        | 15.0  | 4200  | 0.0576          | 0.4836   | 0.9672        | 0.9672           | nan                 | 0.9672           | 0.0            | 0.9672      |
| 0.0324        | 16.0  | 4480  | 0.0503          | 0.4702   | 0.9404        | 0.9404           | nan                 | 0.9404           | 0.0            | 0.9404      |
| 0.0355        | 17.0  | 4760  | 0.0583          | 0.4801   | 0.9601        | 0.9601           | nan                 | 0.9601           | 0.0            | 0.9601      |
| 0.032         | 18.0  | 5040  | 0.0528          | 0.4679   | 0.9358        | 0.9358           | nan                 | 0.9358           | 0.0            | 0.9358      |
| 0.0275        | 19.0  | 5320  | 0.0682          | 0.4828   | 0.9656        | 0.9656           | nan                 | 0.9656           | 0.0            | 0.9656      |
| 0.0329        | 20.0  | 5600  | 0.0712          | 0.4796   | 0.9591        | 0.9591           | nan                 | 0.9591           | 0.0            | 0.9591      |
| 0.0284        | 21.0  | 5880  | 0.0769          | 0.4868   | 0.9737        | 0.9737           | nan                 | 0.9737           | 0.0            | 0.9737      |
| 0.028         | 22.0  | 6160  | 0.0615          | 0.4826   | 0.9651        | 0.9651           | nan                 | 0.9651           | 0.0            | 0.9651      |
| 0.0275        | 23.0  | 6440  | 0.0640          | 0.4797   | 0.9595        | 0.9595           | nan                 | 0.9595           | 0.0            | 0.9595      |
| 0.0263        | 24.0  | 6720  | 0.0805          | 0.4819   | 0.9639        | 0.9639           | nan                 | 0.9639           | 0.0            | 0.9639      |
| 0.0252        | 25.0  | 7000  | 0.0700          | 0.4830   | 0.9661        | 0.9661           | nan                 | 0.9661           | 0.0            | 0.9661      |
| 0.0309        | 26.0  | 7280  | 0.0747          | 0.4854   | 0.9709        | 0.9709           | nan                 | 0.9709           | 0.0            | 0.9709      |
| 0.0238        | 27.0  | 7560  | 0.0704          | 0.4814   | 0.9628        | 0.9628           | nan                 | 0.9628           | 0.0            | 0.9628      |
| 0.0277        | 28.0  | 7840  | 0.0757          | 0.4858   | 0.9716        | 0.9716           | nan                 | 0.9716           | 0.0            | 0.9716      |
| 0.0281        | 29.0  | 8120  | 0.0847          | 0.4830   | 0.9661        | 0.9661           | nan                 | 0.9661           | 0.0            | 0.9661      |
| 0.0259        | 30.0  | 8400  | 0.0741          | 0.4820   | 0.9640        | 0.9640           | nan                 | 0.9640           | 0.0            | 0.9640      |
| 0.0231        | 31.0  | 8680  | 0.0726          | 0.4794   | 0.9587        | 0.9587           | nan                 | 0.9587           | 0.0            | 0.9587      |
| 0.0234        | 32.0  | 8960  | 0.0739          | 0.4779   | 0.9557        | 0.9557           | nan                 | 0.9557           | 0.0            | 0.9557      |
| 0.0226        | 33.0  | 9240  | 0.0743          | 0.4806   | 0.9613        | 0.9613           | nan                 | 0.9613           | 0.0            | 0.9613      |
| 0.0242        | 34.0  | 9520  | 0.0776          | 0.4792   | 0.9584        | 0.9584           | nan                 | 0.9584           | 0.0            | 0.9584      |
| 0.0211        | 35.0  | 9800  | 0.0775          | 0.4765   | 0.9529        | 0.9529           | nan                 | 0.9529           | 0.0            | 0.9529      |
| 0.0223        | 35.71 | 10000 | 0.0797          | 0.4786   | 0.9572        | 0.9572           | nan                 | 0.9572           | 0.0            | 0.9572      |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.4
- Tokenizers 0.15.1
