---
license: other
base_model: nvidia/mit-b3
tags:
- generated_from_trainer
model-index:
- name: segformer_cracks_v2
  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_cracks_v2

This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0487
- Mean Iou: 0.7770
- Mean Accuracy: 0.8378
- Overall Accuracy: 0.9803
- Per Category Iou: [0.9797338315128605, 0.5741766454919031]
- Per Category Accuracy: [0.9922972168222592, 0.683379436844461]

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Per Category Iou                         | Per Category Accuracy                    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------:|:----------------------------------------:|
| 0.0651        | 1.0   | 1541  | 0.0557          | 0.7543   | 0.8089        | 0.9785           | [0.9779508845325062, 0.5306877773356943] | [0.9928050418870356, 0.6249652426334233] |
| 0.0491        | 2.0   | 3082  | 0.0522          | 0.7629   | 0.8195        | 0.9792           | [0.9786365168660656, 0.547127323866261]  | [0.9926515789371488, 0.6463984572343288] |
| 0.047         | 3.0   | 4623  | 0.0546          | 0.7519   | 0.7995        | 0.9787           | [0.9782301112609909, 0.5255903669562288] | [0.9938779990669149, 0.6050380673194482] |
| 0.0456        | 4.0   | 6164  | 0.0510          | 0.7671   | 0.8248        | 0.9795           | [0.9789779157265575, 0.5552760281623849] | [0.9925739681553413, 0.6570897409273347] |
| 0.0442        | 5.0   | 7705  | 0.0509          | 0.7714   | 0.8373        | 0.9794           | [0.978875460970978, 0.5639303830124743]  | [0.9914357800230804, 0.6831791371245445] |
| 0.0439        | 6.0   | 9246  | 0.0502          | 0.7701   | 0.8254        | 0.9799           | [0.9794166328395213, 0.5607787508694683] | [0.9929877378064772, 0.6578722690116804] |
| 0.0433        | 7.0   | 10787 | 0.0500          | 0.7738   | 0.8361        | 0.9799           | [0.979329611841869, 0.5681732097856239]  | [0.9920131586953994, 0.6802192080134767] |
| 0.0427        | 8.0   | 12328 | 0.0499          | 0.7775   | 0.8406        | 0.9802           | [0.9796585654955907, 0.575368007293997]  | [0.9919935734749642, 0.6891110848654891] |
| 0.0424        | 9.0   | 13869 | 0.0495          | 0.7780   | 0.8411        | 0.9802           | [0.9797042588379468, 0.5763456606457494] | [0.9919931330972266, 0.6902882749336847] |
| 0.042         | 10.0  | 15410 | 0.0487          | 0.7770   | 0.8378        | 0.9803           | [0.9797338315128605, 0.5741766454919031] | [0.9922972168222592, 0.683379436844461]  |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3