---
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: 0.3532
- Mean Iou: 0.4001
- Mean Accuracy: 0.8002
- Overall Accuracy: 0.8002
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.8002
- Iou Unlabeled: 0.0
- Iou Circuit: 0.8002
- Dice Coefficient: 0.7456

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
| 0.5722        | 3.12  | 250  | 0.4296          | 0.3809   | 0.7618        | 0.7618           | nan                | 0.7618           | 0.0           | 0.7618      | 0.6766           |
| 0.547         | 6.25  | 500  | 0.3983          | 0.3370   | 0.6739        | 0.6739           | nan                | 0.6739           | 0.0           | 0.6739      | 0.6065           |
| 0.5147        | 9.38  | 750  | 0.3643          | 0.3487   | 0.6974        | 0.6974           | nan                | 0.6974           | 0.0           | 0.6974      | 0.6477           |
| 0.5083        | 12.5  | 1000 | 0.3505          | 0.3006   | 0.6012        | 0.6012           | nan                | 0.6012           | 0.0           | 0.6012      | 0.5586           |
| 0.4818        | 15.62 | 1250 | 0.3184          | 0.4400   | 0.8799        | 0.8799           | nan                | 0.8799           | 0.0           | 0.8799      | 0.7758           |
| 0.4664        | 18.75 | 1500 | 0.3622          | 0.4347   | 0.8693        | 0.8693           | nan                | 0.8693           | 0.0           | 0.8693      | 0.7755           |
| 0.4504        | 21.88 | 1750 | 0.3279          | 0.4327   | 0.8654        | 0.8654           | nan                | 0.8654           | 0.0           | 0.8654      | 0.7792           |
| 0.4427        | 25.0  | 2000 | 0.3168          | 0.4386   | 0.8771        | 0.8771           | nan                | 0.8771           | 0.0           | 0.8771      | 0.7840           |
| 0.4336        | 28.12 | 2250 | 0.2790          | 0.4100   | 0.8200        | 0.8200           | nan                | 0.8200           | 0.0           | 0.8200      | 0.7636           |
| 0.4226        | 31.25 | 2500 | 0.3237          | 0.4148   | 0.8295        | 0.8295           | nan                | 0.8295           | 0.0           | 0.8295      | 0.7641           |
| 0.4155        | 34.38 | 2750 | 0.3336          | 0.4169   | 0.8339        | 0.8339           | nan                | 0.8339           | 0.0           | 0.8339      | 0.7664           |
| 0.4082        | 37.5  | 3000 | 0.3787          | 0.4267   | 0.8533        | 0.8533           | nan                | 0.8533           | 0.0           | 0.8533      | 0.7760           |
| 0.403         | 40.62 | 3250 | 0.3541          | 0.3693   | 0.7387        | 0.7387           | nan                | 0.7387           | 0.0           | 0.7387      | 0.6942           |
| 0.398         | 43.75 | 3500 | 0.3361          | 0.3864   | 0.7728        | 0.7728           | nan                | 0.7728           | 0.0           | 0.7728      | 0.7244           |
| 0.3943        | 46.88 | 3750 | 0.3599          | 0.4053   | 0.8106        | 0.8106           | nan                | 0.8106           | 0.0           | 0.8106      | 0.7519           |
| 0.3951        | 50.0  | 4000 | 0.3532          | 0.4001   | 0.8002        | 0.8002           | nan                | 0.8002           | 0.0           | 0.8002      | 0.7456           |


### Framework versions

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