---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-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-b5-miic-tl

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the yijisuk/ic-chip-sample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2247
- Mean Iou: 0.4565
- Mean Accuracy: 0.9129
- Overall Accuracy: 0.9129
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.9129
- Iou Unlabeled: 0.0
- Iou Circuit: 0.9129
- Dice Coefficient: 0.8406

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
| 0.2801        | 3.12  | 250  | 0.2305          | 0.4832   | 0.9663        | 0.9663           | nan                | 0.9663           | 0.0           | 0.9663      | 0.8527           |
| 0.2785        | 6.25  | 500  | 0.2715          | 0.4800   | 0.9601        | 0.9601           | nan                | 0.9601           | 0.0           | 0.9601      | 0.8511           |
| 0.208         | 9.38  | 750  | 0.2681          | 0.4811   | 0.9622        | 0.9622           | nan                | 0.9622           | 0.0           | 0.9622      | 0.8538           |
| 0.2042        | 12.5  | 1000 | 0.2959          | 0.4650   | 0.9299        | 0.9299           | nan                | 0.9299           | 0.0           | 0.9299      | 0.7879           |
| 0.1649        | 15.62 | 1250 | 0.2407          | 0.4340   | 0.8679        | 0.8679           | nan                | 0.8679           | 0.0           | 0.8679      | 0.8150           |
| 0.1353        | 18.75 | 1500 | 0.2530          | 0.4543   | 0.9085        | 0.9085           | nan                | 0.9085           | 0.0           | 0.9085      | 0.8336           |
| 0.126         | 21.88 | 1750 | 0.4934          | 0.4559   | 0.9119        | 0.9119           | nan                | 0.9119           | 0.0           | 0.9119      | 0.7678           |
| 0.1196        | 25.0  | 2000 | 0.2896          | 0.4604   | 0.9209        | 0.9209           | nan                | 0.9209           | 0.0           | 0.9209      | 0.7807           |
| 0.1149        | 28.12 | 2250 | 0.2210          | 0.4634   | 0.9268        | 0.9268           | nan                | 0.9268           | 0.0           | 0.9268      | 0.8470           |
| 0.1095        | 31.25 | 2500 | 0.2215          | 0.4534   | 0.9067        | 0.9067           | nan                | 0.9067           | 0.0           | 0.9067      | 0.8380           |
| 0.109         | 34.38 | 2750 | 0.2256          | 0.4243   | 0.8487        | 0.8487           | nan                | 0.8487           | 0.0           | 0.8487      | 0.8077           |
| 0.1062        | 37.5  | 3000 | 0.2172          | 0.4497   | 0.8994        | 0.8994           | nan                | 0.8994           | 0.0           | 0.8994      | 0.8363           |
| 0.1046        | 40.62 | 3250 | 0.2401          | 0.4551   | 0.9102        | 0.9102           | nan                | 0.9102           | 0.0           | 0.9102      | 0.8387           |
| 0.1096        | 43.75 | 3500 | 0.2157          | 0.4582   | 0.9164        | 0.9164           | nan                | 0.9164           | 0.0           | 0.9164      | 0.8425           |
| 0.1014        | 46.88 | 3750 | 0.2344          | 0.4573   | 0.9146        | 0.9146           | nan                | 0.9146           | 0.0           | 0.9146      | 0.8411           |
| 0.1036        | 50.0  | 4000 | 0.2247          | 0.4565   | 0.9129        | 0.9129           | nan                | 0.9129           | 0.0           | 0.9129      | 0.8406           |


### Framework versions

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