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---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5
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_240430_V2-Augmented_mit-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_240430_V2-Augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0899
- Mean Iou: 0.8524
- Mean Accuracy: 0.8932
- Overall Accuracy: 0.9653
- Accuracy Background: 0.9900
- Accuracy Melt: 0.7107
- Accuracy Substrate: 0.9788
- Iou Background: 0.9693
- Iou Melt: 0.6451
- Iou Substrate: 0.9429
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1394 | 1.6129 | 50 | 0.2486 | 0.6252 | 0.6776 | 0.9171 | 0.9800 | 0.0713 | 0.9816 | 0.9285 | 0.0680 | 0.8792 |
| 0.2482 | 3.2258 | 100 | 0.2178 | 0.6883 | 0.7470 | 0.9224 | 0.9831 | 0.3037 | 0.9543 | 0.9307 | 0.2490 | 0.8854 |
| 0.1697 | 4.8387 | 150 | 0.2044 | 0.6993 | 0.7613 | 0.9236 | 0.9847 | 0.3511 | 0.9480 | 0.9313 | 0.2796 | 0.8871 |
| 0.139 | 6.4516 | 200 | 0.1897 | 0.7250 | 0.7835 | 0.9317 | 0.9771 | 0.4086 | 0.9648 | 0.9415 | 0.3395 | 0.8940 |
| 0.0951 | 8.0645 | 250 | 0.1879 | 0.6863 | 0.7344 | 0.9291 | 0.9851 | 0.2414 | 0.9766 | 0.9372 | 0.2290 | 0.8928 |
| 0.0812 | 9.6774 | 300 | 0.1875 | 0.7513 | 0.8449 | 0.9285 | 0.9636 | 0.6338 | 0.9372 | 0.9370 | 0.4265 | 0.8903 |
| 0.1349 | 11.2903 | 350 | 0.2020 | 0.6825 | 0.7357 | 0.9247 | 0.9810 | 0.2577 | 0.9685 | 0.9328 | 0.2265 | 0.8882 |
| 0.1312 | 12.9032 | 400 | 0.1401 | 0.7627 | 0.8053 | 0.9465 | 0.9864 | 0.4477 | 0.9816 | 0.9624 | 0.4169 | 0.9090 |
| 0.1061 | 14.5161 | 450 | 0.1051 | 0.8297 | 0.8811 | 0.9586 | 0.9890 | 0.6853 | 0.9691 | 0.9657 | 0.5932 | 0.9302 |
| 0.0287 | 16.1290 | 500 | 0.1045 | 0.8349 | 0.8835 | 0.9598 | 0.9850 | 0.6905 | 0.9749 | 0.9640 | 0.6073 | 0.9335 |
| 0.2051 | 17.7419 | 550 | 0.0928 | 0.8466 | 0.8868 | 0.9644 | 0.9875 | 0.6906 | 0.9824 | 0.9687 | 0.6290 | 0.9420 |
| 0.0898 | 19.3548 | 600 | 0.0899 | 0.8524 | 0.8932 | 0.9653 | 0.9900 | 0.7107 | 0.9788 | 0.9693 | 0.6451 | 0.9429 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
|