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---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-miic-tl
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-b0-miic-tl
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3415
- Mean Iou: 0.4569
- Mean Accuracy: 0.9138
- Overall Accuracy: 0.9138
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.9138
- Iou Unlabeled: 0.0
- Iou Circuit: 0.9138
- Dice Coefficient: 0.8323
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Circuit | Iou Unlabeled | Iou Circuit | Dice Coefficient |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
| 0.3496 | 3.12 | 250 | 0.3203 | 0.4832 | 0.9665 | 0.9665 | nan | 0.9665 | 0.0 | 0.9665 | 0.8163 |
| 0.2808 | 6.25 | 500 | 0.3289 | 0.4814 | 0.9629 | 0.9629 | nan | 0.9629 | 0.0 | 0.9629 | 0.8271 |
| 0.2582 | 9.38 | 750 | 0.3404 | 0.4670 | 0.9339 | 0.9339 | nan | 0.9339 | 0.0 | 0.9339 | 0.8327 |
| 0.2791 | 12.5 | 1000 | 0.3033 | 0.4591 | 0.9181 | 0.9181 | nan | 0.9181 | 0.0 | 0.9181 | 0.8300 |
| 0.2668 | 15.62 | 1250 | 0.3117 | 0.4559 | 0.9118 | 0.9118 | nan | 0.9118 | 0.0 | 0.9118 | 0.8285 |
| 0.2531 | 18.75 | 1500 | 0.2652 | 0.4686 | 0.9373 | 0.9373 | nan | 0.9373 | 0.0 | 0.9373 | 0.8432 |
| 0.2326 | 21.88 | 1750 | 0.3256 | 0.4604 | 0.9208 | 0.9208 | nan | 0.9208 | 0.0 | 0.9208 | 0.8315 |
| 0.2361 | 25.0 | 2000 | 0.3129 | 0.4656 | 0.9313 | 0.9313 | nan | 0.9313 | 0.0 | 0.9313 | 0.8400 |
| 0.2167 | 28.12 | 2250 | 0.3135 | 0.4558 | 0.9116 | 0.9116 | nan | 0.9116 | 0.0 | 0.9116 | 0.8290 |
| 0.2133 | 31.25 | 2500 | 0.3132 | 0.4560 | 0.9120 | 0.9120 | nan | 0.9120 | 0.0 | 0.9120 | 0.8219 |
| 0.1769 | 34.38 | 2750 | 0.3200 | 0.4441 | 0.8882 | 0.8882 | nan | 0.8882 | 0.0 | 0.8882 | 0.8176 |
| 0.1899 | 37.5 | 3000 | 0.3342 | 0.4612 | 0.9224 | 0.9224 | nan | 0.9224 | 0.0 | 0.9224 | 0.8363 |
| 0.1765 | 40.62 | 3250 | 0.3445 | 0.4625 | 0.9249 | 0.9249 | nan | 0.9249 | 0.0 | 0.9249 | 0.8369 |
| 0.1739 | 43.75 | 3500 | 0.3235 | 0.4608 | 0.9216 | 0.9216 | nan | 0.9216 | 0.0 | 0.9216 | 0.8373 |
| 0.1639 | 46.88 | 3750 | 0.3527 | 0.4591 | 0.9181 | 0.9181 | nan | 0.9181 | 0.0 | 0.9181 | 0.8342 |
| 0.1734 | 50.0 | 4000 | 0.3415 | 0.4569 | 0.9138 | 0.9138 | nan | 0.9138 | 0.0 | 0.9138 | 0.8323 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu115
- Datasets 2.15.0
- Tokenizers 0.15.0
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