---
license: other
base_model: ironchanchellor/segformer-b0_DsA
tags:
- generated_from_trainer
model-index:
- name: segformer-b0_DsApollo_ILT
  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_DsApollo_ILT

This model is a fine-tuned version of [ironchanchellor/segformer-b0_DsA](https://huggingface.co/ironchanchellor/segformer-b0_DsA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0116
- Mean Iou: 0.7963
- Mean Accuracy: 0.9731
- Overall Accuracy: 0.9942
- Accuracy Background: nan
- Accuracy Haz: 0.9965
- Accuracy Matrix: 0.9883
- Accuracy Porosity: 0.9077
- Accuracy Carbides: 0.9757
- Accuracy Substrate: 0.9970
- Iou Background: 0.0
- Iou Haz: 0.9932
- Iou Matrix: 0.9767
- Iou Porosity: 0.8590
- Iou Carbides: 0.9547
- Iou Substrate: 0.9942

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Haz | Accuracy Matrix | Accuracy Porosity | Accuracy Carbides | Accuracy Substrate | Iou Background | Iou Haz | Iou Matrix | Iou Porosity | Iou Carbides | Iou Substrate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:---------------:|:-----------------:|:-----------------:|:------------------:|:--------------:|:-------:|:----------:|:------------:|:------------:|:-------------:|
| 0.2652        | 1.0   | 591  | 0.0181          | 0.7893   | 0.9731        | 0.9914           | nan                 | 0.9952       | 0.9836          | 0.9148            | 0.9794            | 0.9926             | 0.0            | 0.9872  | 0.9741     | 0.8340       | 0.9516       | 0.9886        |
| 0.0167        | 2.0   | 1182 | 0.0196          | 0.7899   | 0.9662        | 0.9920           | nan                 | 0.9919       | 0.9849          | 0.8780            | 0.9793            | 0.9967             | 0.0            | 0.9884  | 0.9746     | 0.8346       | 0.9522       | 0.9899        |
| 0.0272        | 3.0   | 1773 | 0.0152          | 0.7895   | 0.9652        | 0.9923           | nan                 | 0.9970       | 0.9906          | 0.8787            | 0.9672            | 0.9926             | 0.0            | 0.9890  | 0.9753     | 0.8311       | 0.9512       | 0.9903        |
| 0.0117        | 4.0   | 2364 | 0.0131          | 0.7924   | 0.9663        | 0.9933           | nan                 | 0.9951       | 0.9902          | 0.8801            | 0.9698            | 0.9966             | 0.0            | 0.9912  | 0.9759     | 0.8425       | 0.9524       | 0.9924        |
| 0.0176        | 5.0   | 2955 | 0.0160          | 0.7919   | 0.9658        | 0.9930           | nan                 | 0.9939       | 0.9871          | 0.8731            | 0.9780            | 0.9968             | 0.0            | 0.9903  | 0.9760     | 0.8396       | 0.9536       | 0.9916        |
| 0.0772        | 6.0   | 3546 | 0.0125          | 0.7928   | 0.9671        | 0.9937           | nan                 | 0.9949       | 0.9858          | 0.8767            | 0.9804            | 0.9976             | 0.0            | 0.9922  | 0.9760     | 0.8419       | 0.9535       | 0.9933        |
| 0.0568        | 7.0   | 4137 | 0.0126          | 0.7950   | 0.9697        | 0.9937           | nan                 | 0.9978       | 0.9895          | 0.8933            | 0.9735            | 0.9946             | 0.0            | 0.9920  | 0.9766     | 0.8540       | 0.9540       | 0.9931        |
| 0.0228        | 8.0   | 4728 | 0.0158          | 0.7956   | 0.9766        | 0.9934           | nan                 | 0.9933       | 0.9882          | 0.9283            | 0.9749            | 0.9980             | 0.0            | 0.9911  | 0.9766     | 0.8591       | 0.9543       | 0.9923        |
| 0.0058        | 9.0   | 5319 | 0.0120          | 0.7960   | 0.9731        | 0.9940           | nan                 | 0.9958       | 0.9873          | 0.9068            | 0.9782            | 0.9972             | 0.0            | 0.9927  | 0.9766     | 0.8580       | 0.9547       | 0.9938        |
| 0.0038        | 10.0  | 5910 | 0.0116          | 0.7963   | 0.9731        | 0.9942           | nan                 | 0.9965       | 0.9883          | 0.9077            | 0.9757            | 0.9970             | 0.0            | 0.9932  | 0.9767     | 0.8590       | 0.9547       | 0.9942        |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0