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# segformer-b0-finetuned-segments-ic-chip-sample

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: 0.0772
- Mean Iou: 0.4863
- Mean Accuracy: 0.9725
- Overall Accuracy: 0.9725
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.9725
- Iou Unlabeled: 0.0
- Iou Circuit: 0.9725

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
| 0.0885        | 1.0   | 20   | 0.0858          | 0.4907   | 0.9815        | 0.9815           | nan                | 0.9815           | 0.0           | 0.9815      |
| 0.1764        | 2.0   | 40   | 0.0854          | 0.4734   | 0.9469        | 0.9469           | nan                | 0.9469           | 0.0           | 0.9469      |
| 0.0569        | 3.0   | 60   | 0.0854          | 0.4702   | 0.9404        | 0.9404           | nan                | 0.9404           | 0.0           | 0.9404      |
| 0.0959        | 4.0   | 80   | 0.0851          | 0.4893   | 0.9786        | 0.9786           | nan                | 0.9786           | 0.0           | 0.9786      |
| 0.2969        | 5.0   | 100  | 0.0825          | 0.4863   | 0.9727        | 0.9727           | nan                | 0.9727           | 0.0           | 0.9727      |
| 0.1979        | 6.0   | 120  | 0.0824          | 0.4873   | 0.9746        | 0.9746           | nan                | 0.9746           | 0.0           | 0.9746      |
| 0.0906        | 7.0   | 140  | 0.0824          | 0.4740   | 0.9480        | 0.9480           | nan                | 0.9480           | 0.0           | 0.9480      |
| 0.2879        | 8.0   | 160  | 0.0821          | 0.4882   | 0.9764        | 0.9764           | nan                | 0.9764           | 0.0           | 0.9764      |
| 0.1366        | 9.0   | 180  | 0.0807          | 0.4833   | 0.9666        | 0.9666           | nan                | 0.9666           | 0.0           | 0.9666      |
| 0.1664        | 10.0  | 200  | 0.0813          | 0.4860   | 0.9720        | 0.9720           | nan                | 0.9720           | 0.0           | 0.9720      |
| 0.1521        | 11.0  | 220  | 0.0831          | 0.4830   | 0.9660        | 0.9660           | nan                | 0.9660           | 0.0           | 0.9660      |
| 0.2004        | 12.0  | 240  | 0.0795          | 0.4825   | 0.9651        | 0.9651           | nan                | 0.9651           | 0.0           | 0.9651      |
| 0.1547        | 13.0  | 260  | 0.0793          | 0.4812   | 0.9625        | 0.9625           | nan                | 0.9625           | 0.0           | 0.9625      |
| 0.4191        | 14.0  | 280  | 0.0788          | 0.4830   | 0.9659        | 0.9659           | nan                | 0.9659           | 0.0           | 0.9659      |
| 0.0431        | 15.0  | 300  | 0.0782          | 0.4815   | 0.9630        | 0.9630           | nan                | 0.9630           | 0.0           | 0.9630      |
| 1.3911        | 16.0  | 320  | 0.0793          | 0.4820   | 0.9640        | 0.9640           | nan                | 0.9640           | 0.0           | 0.9640      |
| 0.0217        | 17.0  | 340  | 0.0814          | 0.4836   | 0.9671        | 0.9671           | nan                | 0.9671           | 0.0           | 0.9671      |
| 0.1116        | 18.0  | 360  | 0.0789          | 0.4839   | 0.9678        | 0.9678           | nan                | 0.9678           | 0.0           | 0.9678      |
| 0.3295        | 19.0  | 380  | 0.0791          | 0.4763   | 0.9526        | 0.9526           | nan                | 0.9526           | 0.0           | 0.9526      |
| 0.0327        | 20.0  | 400  | 0.0792          | 0.4829   | 0.9658        | 0.9658           | nan                | 0.9658           | 0.0           | 0.9658      |
| 0.2542        | 21.0  | 420  | 0.0787          | 0.4861   | 0.9722        | 0.9722           | nan                | 0.9722           | 0.0           | 0.9722      |
| 0.1587        | 22.0  | 440  | 0.0783          | 0.4772   | 0.9543        | 0.9543           | nan                | 0.9543           | 0.0           | 0.9543      |
| 0.2721        | 23.0  | 460  | 0.0804          | 0.4913   | 0.9825        | 0.9825           | nan                | 0.9825           | 0.0           | 0.9825      |
| 0.0505        | 24.0  | 480  | 0.0781          | 0.4827   | 0.9655        | 0.9655           | nan                | 0.9655           | 0.0           | 0.9655      |
| 0.1417        | 25.0  | 500  | 0.0801          | 0.4834   | 0.9669        | 0.9669           | nan                | 0.9669           | 0.0           | 0.9669      |
| 0.1371        | 26.0  | 520  | 0.0777          | 0.4838   | 0.9676        | 0.9676           | nan                | 0.9676           | 0.0           | 0.9676      |
| 0.1282        | 27.0  | 540  | 0.0773          | 0.4807   | 0.9613        | 0.9613           | nan                | 0.9613           | 0.0           | 0.9613      |
| 0.057         | 28.0  | 560  | 0.0772          | 0.4829   | 0.9657        | 0.9657           | nan                | 0.9657           | 0.0           | 0.9657      |
| 0.2592        | 29.0  | 580  | 0.0807          | 0.4872   | 0.9744        | 0.9744           | nan                | 0.9744           | 0.0           | 0.9744      |
| 0.1687        | 30.0  | 600  | 0.0794          | 0.4825   | 0.9649        | 0.9649           | nan                | 0.9649           | 0.0           | 0.9649      |
| 0.499         | 31.0  | 620  | 0.0805          | 0.4853   | 0.9706        | 0.9706           | nan                | 0.9706           | 0.0           | 0.9706      |
| 0.1584        | 32.0  | 640  | 0.0790          | 0.4845   | 0.9691        | 0.9691           | nan                | 0.9691           | 0.0           | 0.9691      |
| 0.0689        | 33.0  | 660  | 0.0785          | 0.4845   | 0.9690        | 0.9690           | nan                | 0.9690           | 0.0           | 0.9690      |
| 1.3764        | 34.0  | 680  | 0.0790          | 0.4848   | 0.9696        | 0.9696           | nan                | 0.9696           | 0.0           | 0.9696      |
| 0.2597        | 35.0  | 700  | 0.0808          | 0.4875   | 0.9751        | 0.9751           | nan                | 0.9751           | 0.0           | 0.9751      |
| 1.0757        | 36.0  | 720  | 0.0761          | 0.4841   | 0.9681        | 0.9681           | nan                | 0.9681           | 0.0           | 0.9681      |
| 0.6112        | 37.0  | 740  | 0.0779          | 0.4825   | 0.9650        | 0.9650           | nan                | 0.9650           | 0.0           | 0.9650      |
| 0.2899        | 38.0  | 760  | 0.0787          | 0.4796   | 0.9591        | 0.9591           | nan                | 0.9591           | 0.0           | 0.9591      |
| 0.3402        | 39.0  | 780  | 0.0777          | 0.4838   | 0.9676        | 0.9676           | nan                | 0.9676           | 0.0           | 0.9676      |
| 0.0183        | 40.0  | 800  | 0.0771          | 0.4829   | 0.9657        | 0.9657           | nan                | 0.9657           | 0.0           | 0.9657      |
| 0.1407        | 41.0  | 820  | 0.0774          | 0.4809   | 0.9617        | 0.9617           | nan                | 0.9617           | 0.0           | 0.9617      |
| 0.4045        | 42.0  | 840  | 0.0767          | 0.4819   | 0.9638        | 0.9638           | nan                | 0.9638           | 0.0           | 0.9638      |
| 0.2159        | 43.0  | 860  | 0.0780          | 0.4850   | 0.9699        | 0.9699           | nan                | 0.9699           | 0.0           | 0.9699      |
| 0.0541        | 44.0  | 880  | 0.0768          | 0.4812   | 0.9624        | 0.9624           | nan                | 0.9624           | 0.0           | 0.9624      |
| 0.0638        | 45.0  | 900  | 0.0774          | 0.4863   | 0.9726        | 0.9726           | nan                | 0.9726           | 0.0           | 0.9726      |
| 0.0409        | 46.0  | 920  | 0.0788          | 0.4875   | 0.9749        | 0.9749           | nan                | 0.9749           | 0.0           | 0.9749      |
| 0.1662        | 47.0  | 940  | 0.0774          | 0.4871   | 0.9743        | 0.9743           | nan                | 0.9743           | 0.0           | 0.9743      |
| 0.1636        | 48.0  | 960  | 0.0783          | 0.4860   | 0.9720        | 0.9720           | nan                | 0.9720           | 0.0           | 0.9720      |
| 0.033         | 49.0  | 980  | 0.0791          | 0.4882   | 0.9764        | 0.9764           | nan                | 0.9764           | 0.0           | 0.9764      |
| 0.171         | 50.0  | 1000 | 0.0772          | 0.4863   | 0.9725        | 0.9725           | nan                | 0.9725           | 0.0           | 0.9725      |


### Framework versions

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

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-finetuned-segments-ic-chip-sample", "results": []}]}
transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us