---
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