---
license: other
base_model: nvidia/mit-b5
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: FINAL_ecc_segformer
  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. -->

# FINAL_ecc_segformer

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset_exhaustive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0749
- Mean Iou: 0.1968
- Mean Accuracy: 0.3939
- Overall Accuracy: 0.3939
- Accuracy Background: nan
- Accuracy Crack: 0.3939
- Iou Background: 0.0
- Iou Crack: 0.3936

## 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: 2
- eval_batch_size: 2
- 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 Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0534        | 1.0   | 548   | 0.0614          | 0.1368   | 0.2750        | 0.2750           | nan                 | 0.2750         | 0.0            | 0.2736    |
| 0.058         | 2.0   | 1096  | 0.1018          | 0.2093   | 0.4238        | 0.4238           | nan                 | 0.4238         | 0.0            | 0.4186    |
| 0.0482        | 3.0   | 1644  | 0.0508          | 0.1791   | 0.4315        | 0.4315           | nan                 | 0.4315         | 0.0            | 0.3582    |
| 0.0338        | 4.0   | 2192  | 0.0569          | 0.1849   | 0.3716        | 0.3716           | nan                 | 0.3716         | 0.0            | 0.3698    |
| 0.0395        | 5.0   | 2740  | 0.0597          | 0.1745   | 0.3506        | 0.3506           | nan                 | 0.3506         | 0.0            | 0.3490    |
| 0.0372        | 6.0   | 3288  | 0.0509          | 0.2298   | 0.4635        | 0.4635           | nan                 | 0.4635         | 0.0            | 0.4597    |
| 0.0402        | 7.0   | 3836  | 0.0620          | 0.1751   | 0.3507        | 0.3507           | nan                 | 0.3507         | 0.0            | 0.3503    |
| 0.038         | 8.0   | 4384  | 0.0681          | 0.1905   | 0.3815        | 0.3815           | nan                 | 0.3815         | 0.0            | 0.3810    |
| 0.0393        | 9.0   | 4932  | 0.0685          | 0.2213   | 0.4433        | 0.4433           | nan                 | 0.4433         | 0.0            | 0.4425    |
| 0.0376        | 10.0  | 5480  | 0.0590          | 0.1962   | 0.3929        | 0.3929           | nan                 | 0.3929         | 0.0            | 0.3924    |
| 0.0381        | 11.0  | 6028  | 0.0626          | 0.1891   | 0.3801        | 0.3801           | nan                 | 0.3801         | 0.0            | 0.3783    |
| 0.034         | 12.0  | 6576  | 0.0623          | 0.2061   | 0.4162        | 0.4162           | nan                 | 0.4162         | 0.0            | 0.4122    |
| 0.0301        | 13.0  | 7124  | 0.0831          | 0.1832   | 0.3669        | 0.3669           | nan                 | 0.3669         | 0.0            | 0.3664    |
| 0.034         | 14.0  | 7672  | 0.0636          | 0.2059   | 0.4119        | 0.4119           | nan                 | 0.4119         | 0.0            | 0.4118    |
| 0.0303        | 15.0  | 8220  | 0.0705          | 0.1931   | 0.3864        | 0.3864           | nan                 | 0.3864         | 0.0            | 0.3862    |
| 0.0338        | 16.0  | 8768  | 0.0685          | 0.2101   | 0.4206        | 0.4206           | nan                 | 0.4206         | 0.0            | 0.4202    |
| 0.0229        | 17.0  | 9316  | 0.0706          | 0.2099   | 0.4204        | 0.4204           | nan                 | 0.4204         | 0.0            | 0.4197    |
| 0.0337        | 18.0  | 9864  | 0.0742          | 0.1982   | 0.3968        | 0.3968           | nan                 | 0.3968         | 0.0            | 0.3965    |
| 0.0257        | 18.25 | 10000 | 0.0749          | 0.1968   | 0.3939        | 0.3939           | nan                 | 0.3939         | 0.0            | 0.3936    |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cpu
- Datasets 2.14.6
- Tokenizers 0.14.1