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

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: 0.0499
- Mean Iou: 0.7718
- Mean Accuracy: 0.8317
- Overall Accuracy: 0.9798
- Per Category Iou: [0.9792869895386617, 0.564265846038068]
- Per Category Accuracy: [0.9923313345080351, 0.671108360646227]

## 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: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                         | Per Category Accuracy                    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------:|:----------------------------------------:|
| 0.0686        | 1.0   | 1541  | 0.0557          | 0.7541   | 0.8082        | 0.9785           | [0.9779708221514636, 0.5303006858963294] | [0.9928845967047768, 0.6234677160845897] |
| 0.049         | 2.0   | 3082  | 0.0527          | 0.7633   | 0.8239        | 0.9790           | [0.9784073819168878, 0.5481400031368636] | [0.9920481107810992, 0.6557623260692017] |
| 0.0468        | 3.0   | 4623  | 0.0547          | 0.7526   | 0.7996        | 0.9788           | [0.9783360187606548, 0.5269084757862701] | [0.993975994702418, 0.6052805015161615]  |
| 0.0456        | 4.0   | 6164  | 0.0509          | 0.7677   | 0.8276        | 0.9794           | [0.9788937969015667, 0.556522438909845]  | [0.9922581622702671, 0.6629042271896711] |
| 0.044         | 5.0   | 7705  | 0.0505          | 0.7678   | 0.8265        | 0.9795           | [0.9789809420595871, 0.5566804258721124] | [0.9924358981457169, 0.6606494246283242] |
| 0.0436        | 6.0   | 9246  | 0.0502          | 0.7696   | 0.8265        | 0.9798           | [0.9792607857315766, 0.5598563478221208] | [0.9927329763880554, 0.6603118480646505] |
| 0.0431        | 7.0   | 10787 | 0.0499          | 0.7718   | 0.8317        | 0.9798           | [0.9792869895386617, 0.564265846038068]  | [0.9923313345080351, 0.671108360646227]  |


### Framework versions

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