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---
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
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