---
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b1-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-b1-miic-tl

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2212
- Mean Iou: 0.4723
- Mean Accuracy: 0.9446
- Overall Accuracy: 0.9446
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.9446
- Iou Unlabeled: 0.0
- Iou Circuit: 0.9446
- Dice Coefficient: 0.8541

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
| 0.3419        | 3.12  | 250  | 0.2745          | 0.4850   | 0.9701        | 0.9701           | nan                | 0.9701           | 0.0           | 0.9701      | 0.8149           |
| 0.2785        | 6.25  | 500  | 0.2789          | 0.4828   | 0.9657        | 0.9657           | nan                | 0.9657           | 0.0           | 0.9657      | 0.8285           |
| 0.2549        | 9.38  | 750  | 0.2888          | 0.4721   | 0.9443        | 0.9443           | nan                | 0.9443           | 0.0           | 0.9443      | 0.8372           |
| 0.2728        | 12.5  | 1000 | 0.2426          | 0.4699   | 0.9397        | 0.9397           | nan                | 0.9397           | 0.0           | 0.9397      | 0.8424           |
| 0.2625        | 15.62 | 1250 | 0.1990          | 0.4632   | 0.9264        | 0.9264           | nan                | 0.9264           | 0.0           | 0.9264      | 0.8520           |
| 0.2449        | 18.75 | 1500 | 0.2121          | 0.4706   | 0.9412        | 0.9412           | nan                | 0.9412           | 0.0           | 0.9412      | 0.8508           |
| 0.2173        | 21.88 | 1750 | 0.2768          | 0.4780   | 0.9559        | 0.9559           | nan                | 0.9559           | 0.0           | 0.9559      | 0.8485           |
| 0.2158        | 25.0  | 2000 | 0.2772          | 0.4643   | 0.9287        | 0.9287           | nan                | 0.9287           | 0.0           | 0.9287      | 0.8383           |
| 0.1843        | 28.12 | 2250 | 0.1818          | 0.4671   | 0.9343        | 0.9343           | nan                | 0.9343           | 0.0           | 0.9343      | 0.8685           |
| 0.1608        | 31.25 | 2500 | 0.1794          | 0.4591   | 0.9182        | 0.9182           | nan                | 0.9182           | 0.0           | 0.9182      | 0.8618           |
| 0.1504        | 34.38 | 2750 | 0.1805          | 0.4586   | 0.9172        | 0.9172           | nan                | 0.9172           | 0.0           | 0.9172      | 0.8647           |
| 0.1495        | 37.5  | 3000 | 0.2090          | 0.4773   | 0.9545        | 0.9545           | nan                | 0.9545           | 0.0           | 0.9545      | 0.8595           |
| 0.142         | 40.62 | 3250 | 0.2048          | 0.4750   | 0.9500        | 0.9500           | nan                | 0.9500           | 0.0           | 0.9500      | 0.8666           |
| 0.1401        | 43.75 | 3500 | 0.2131          | 0.4756   | 0.9512        | 0.9512           | nan                | 0.9512           | 0.0           | 0.9512      | 0.8580           |
| 0.1339        | 46.88 | 3750 | 0.2469          | 0.4773   | 0.9546        | 0.9546           | nan                | 0.9546           | 0.0           | 0.9546      | 0.8481           |
| 0.1303        | 50.0  | 4000 | 0.2212          | 0.4723   | 0.9446        | 0.9446           | nan                | 0.9446           | 0.0           | 0.9446      | 0.8541           |


### Framework versions

- Transformers 4.36.2
- Pytorch 1.11.0+cu115
- Datasets 2.15.0
- Tokenizers 0.15.0