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---
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
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