---
license: other
base_model: nvidia/mit-b4
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer_Clean_Set1_95images
  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_Clean_Set1_95images

This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the Hasano20/Clean_Set1_95images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0223
- Mean Iou: 0.6447
- Mean Accuracy: 0.9824
- Overall Accuracy: 0.9886
- Accuracy Background: nan
- Accuracy Melt: 0.9724
- Accuracy Substrate: 0.9923
- Iou Background: 0.0
- Iou Melt: 0.9458
- Iou Substrate: 0.9882

## 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: 0.0001
- 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: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.2051        | 1.1765  | 20   | 0.3764          | 0.3339   | 0.5766        | 0.8354           | nan                 | 0.1639        | 0.9894             | 0.0            | 0.1612   | 0.8404        |
| 0.3486        | 2.3529  | 40   | 0.1932          | 0.4595   | 0.7687        | 0.8745           | nan                 | 0.6000        | 0.9375             | 0.0            | 0.4928   | 0.8858        |
| 0.0831        | 3.5294  | 60   | 0.2016          | 0.4101   | 0.6782        | 0.8792           | nan                 | 0.3576        | 0.9988             | 0.0            | 0.3570   | 0.8732        |
| 0.0809        | 4.7059  | 80   | 0.0763          | 0.5787   | 0.9243        | 0.9507           | nan                 | 0.8822        | 0.9664             | 0.0            | 0.7830   | 0.9531        |
| 0.0325        | 5.8824  | 100  | 0.0694          | 0.6028   | 0.9436        | 0.9618           | nan                 | 0.9146        | 0.9727             | 0.0            | 0.8479   | 0.9606        |
| 0.0279        | 7.0588  | 120  | 0.0460          | 0.6142   | 0.9520        | 0.9712           | nan                 | 0.9213        | 0.9826             | 0.0            | 0.8739   | 0.9686        |
| 0.0493        | 8.2353  | 140  | 0.0353          | 0.6297   | 0.9648        | 0.9802           | nan                 | 0.9404        | 0.9893             | 0.0            | 0.9092   | 0.9797        |
| 0.0286        | 9.4118  | 160  | 0.0366          | 0.6261   | 0.9643        | 0.9765           | nan                 | 0.9449        | 0.9837             | 0.0            | 0.8997   | 0.9787        |
| 0.0463        | 10.5882 | 180  | 0.0258          | 0.6425   | 0.9798        | 0.9879           | nan                 | 0.9669        | 0.9927             | 0.0            | 0.9414   | 0.9862        |
| 0.0145        | 11.7647 | 200  | 0.0302          | 0.6324   | 0.9652        | 0.9821           | nan                 | 0.9382        | 0.9922             | 0.0            | 0.9162   | 0.9810        |
| 0.0221        | 12.9412 | 220  | 0.0262          | 0.6379   | 0.9733        | 0.9850           | nan                 | 0.9547        | 0.9919             | 0.0            | 0.9289   | 0.9848        |
| 0.0109        | 14.1176 | 240  | 0.0236          | 0.6417   | 0.9764        | 0.9869           | nan                 | 0.9595        | 0.9932             | 0.0            | 0.9379   | 0.9871        |
| 0.0122        | 15.2941 | 260  | 0.0252          | 0.6407   | 0.9812        | 0.9866           | nan                 | 0.9725        | 0.9898             | 0.0            | 0.9358   | 0.9864        |
| 0.0101        | 16.4706 | 280  | 0.0239          | 0.6417   | 0.9799        | 0.9869           | nan                 | 0.9686        | 0.9911             | 0.0            | 0.9382   | 0.9870        |
| 0.0113        | 17.6471 | 300  | 0.0231          | 0.6425   | 0.9798        | 0.9874           | nan                 | 0.9675        | 0.9920             | 0.0            | 0.9399   | 0.9875        |
| 0.0086        | 18.8235 | 320  | 0.0225          | 0.6444   | 0.9826        | 0.9885           | nan                 | 0.9733        | 0.9919             | 0.0            | 0.9451   | 0.9882        |
| 0.0086        | 20.0    | 340  | 0.0223          | 0.6447   | 0.9824        | 0.9886           | nan                 | 0.9724        | 0.9923             | 0.0            | 0.9458   | 0.9882        |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1