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# segformer-b1-finetuned-segments-ic-chip-sample

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: 0.1227
- Mean Iou: 0.4744
- Mean Accuracy: 0.9489
- Overall Accuracy: 0.9489
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.9489
- Iou Unlabeled: 0.0
- Iou Circuit: 0.9489

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
| 0.4185        | 1.0   | 20   | 0.5878          | 0.3632   | 0.7265        | 0.7265           | nan                | 0.7265           | 0.0           | 0.7265      |
| 0.4477        | 2.0   | 40   | 0.4288          | 0.4894   | 0.9788        | 0.9788           | nan                | 0.9788           | 0.0           | 0.9788      |
| 0.9304        | 3.0   | 60   | 0.2053          | 0.4520   | 0.9041        | 0.9041           | nan                | 0.9041           | 0.0           | 0.9041      |
| 0.1409        | 4.0   | 80   | 0.1817          | 0.4738   | 0.9477        | 0.9477           | nan                | 0.9477           | 0.0           | 0.9477      |
| 0.392         | 5.0   | 100  | 0.1824          | 0.4900   | 0.9800        | 0.9800           | nan                | 0.9800           | 0.0           | 0.9800      |
| 0.1589        | 6.0   | 120  | 0.1594          | 0.4814   | 0.9628        | 0.9628           | nan                | 0.9628           | 0.0           | 0.9628      |
| 0.1848        | 7.0   | 140  | 0.1551          | 0.4625   | 0.9251        | 0.9251           | nan                | 0.9251           | 0.0           | 0.9251      |
| 0.0874        | 8.0   | 160  | 0.1503          | 0.4829   | 0.9657        | 0.9657           | nan                | 0.9657           | 0.0           | 0.9657      |
| 0.2172        | 9.0   | 180  | 0.1558          | 0.4591   | 0.9182        | 0.9182           | nan                | 0.9182           | 0.0           | 0.9182      |
| 0.9914        | 10.0  | 200  | 0.1457          | 0.4698   | 0.9396        | 0.9396           | nan                | 0.9396           | 0.0           | 0.9396      |
| 0.2387        | 11.0  | 220  | 0.1494          | 0.4709   | 0.9419        | 0.9419           | nan                | 0.9419           | 0.0           | 0.9419      |
| 0.1242        | 12.0  | 240  | 0.1463          | 0.4743   | 0.9486        | 0.9486           | nan                | 0.9486           | 0.0           | 0.9486      |
| 0.0819        | 13.0  | 260  | 0.1492          | 0.4757   | 0.9515        | 0.9515           | nan                | 0.9515           | 0.0           | 0.9515      |
| 0.6077        | 14.0  | 280  | 0.1442          | 0.4793   | 0.9586        | 0.9586           | nan                | 0.9586           | 0.0           | 0.9586      |
| 0.3156        | 15.0  | 300  | 0.1430          | 0.4813   | 0.9627        | 0.9627           | nan                | 0.9627           | 0.0           | 0.9627      |
| 0.2564        | 16.0  | 320  | 0.1483          | 0.4673   | 0.9347        | 0.9347           | nan                | 0.9347           | 0.0           | 0.9347      |
| 0.107         | 17.0  | 340  | 0.1467          | 0.4695   | 0.9390        | 0.9390           | nan                | 0.9390           | 0.0           | 0.9390      |
| 1.1592        | 18.0  | 360  | 0.1437          | 0.4814   | 0.9628        | 0.9628           | nan                | 0.9628           | 0.0           | 0.9628      |
| 0.0586        | 19.0  | 380  | 0.1396          | 0.4811   | 0.9622        | 0.9622           | nan                | 0.9622           | 0.0           | 0.9622      |
| 0.9815        | 20.0  | 400  | 0.1399          | 0.4812   | 0.9624        | 0.9624           | nan                | 0.9624           | 0.0           | 0.9624      |
| 0.3101        | 21.0  | 420  | 0.1411          | 0.4836   | 0.9672        | 0.9672           | nan                | 0.9672           | 0.0           | 0.9672      |
| 0.2325        | 22.0  | 440  | 0.1395          | 0.4672   | 0.9344        | 0.9344           | nan                | 0.9344           | 0.0           | 0.9344      |
| 0.1504        | 23.0  | 460  | 0.1420          | 0.4720   | 0.9441        | 0.9441           | nan                | 0.9441           | 0.0           | 0.9441      |
| 0.2831        | 24.0  | 480  | 0.1393          | 0.4697   | 0.9395        | 0.9395           | nan                | 0.9395           | 0.0           | 0.9395      |
| 0.0921        | 25.0  | 500  | 0.1418          | 0.4701   | 0.9401        | 0.9401           | nan                | 0.9401           | 0.0           | 0.9401      |
| 0.141         | 26.0  | 520  | 0.1318          | 0.4648   | 0.9296        | 0.9296           | nan                | 0.9296           | 0.0           | 0.9296      |
| 0.1381        | 27.0  | 540  | 0.1316          | 0.4697   | 0.9395        | 0.9395           | nan                | 0.9395           | 0.0           | 0.9395      |
| 1.1864        | 28.0  | 560  | 0.1292          | 0.4774   | 0.9548        | 0.9548           | nan                | 0.9548           | 0.0           | 0.9548      |
| 0.9492        | 29.0  | 580  | 0.1290          | 0.4709   | 0.9418        | 0.9418           | nan                | 0.9418           | 0.0           | 0.9418      |
| 0.3061        | 30.0  | 600  | 0.1303          | 0.4536   | 0.9071        | 0.9071           | nan                | 0.9071           | 0.0           | 0.9071      |
| 0.2511        | 31.0  | 620  | 0.1318          | 0.4725   | 0.9451        | 0.9451           | nan                | 0.9451           | 0.0           | 0.9451      |
| 0.2706        | 32.0  | 640  | 0.1284          | 0.4790   | 0.9580        | 0.9580           | nan                | 0.9580           | 0.0           | 0.9580      |
| 0.1508        | 33.0  | 660  | 0.1264          | 0.4698   | 0.9396        | 0.9396           | nan                | 0.9396           | 0.0           | 0.9396      |
| 0.2802        | 34.0  | 680  | 0.1308          | 0.4733   | 0.9467        | 0.9467           | nan                | 0.9467           | 0.0           | 0.9467      |
| 0.1897        | 35.0  | 700  | 0.1315          | 0.4681   | 0.9361        | 0.9361           | nan                | 0.9361           | 0.0           | 0.9361      |
| 0.1981        | 36.0  | 720  | 0.1289          | 0.4766   | 0.9531        | 0.9531           | nan                | 0.9531           | 0.0           | 0.9531      |
| 0.2742        | 37.0  | 740  | 0.1284          | 0.4818   | 0.9635        | 0.9635           | nan                | 0.9635           | 0.0           | 0.9635      |
| 0.0418        | 38.0  | 760  | 0.1240          | 0.4762   | 0.9525        | 0.9525           | nan                | 0.9525           | 0.0           | 0.9525      |
| 0.1946        | 39.0  | 780  | 0.1253          | 0.4750   | 0.9500        | 0.9500           | nan                | 0.9500           | 0.0           | 0.9500      |
| 0.1692        | 40.0  | 800  | 0.1253          | 0.4836   | 0.9672        | 0.9672           | nan                | 0.9672           | 0.0           | 0.9672      |
| 0.3071        | 41.0  | 820  | 0.1227          | 0.4751   | 0.9503        | 0.9503           | nan                | 0.9503           | 0.0           | 0.9503      |
| 0.2003        | 42.0  | 840  | 0.1250          | 0.4762   | 0.9524        | 0.9524           | nan                | 0.9524           | 0.0           | 0.9524      |
| 0.2099        | 43.0  | 860  | 0.1235          | 0.4740   | 0.9480        | 0.9480           | nan                | 0.9480           | 0.0           | 0.9480      |
| 0.1218        | 44.0  | 880  | 0.1222          | 0.4743   | 0.9486        | 0.9486           | nan                | 0.9486           | 0.0           | 0.9486      |
| 0.1583        | 45.0  | 900  | 0.1226          | 0.4708   | 0.9415        | 0.9415           | nan                | 0.9415           | 0.0           | 0.9415      |
| 0.1506        | 46.0  | 920  | 0.1215          | 0.4686   | 0.9372        | 0.9372           | nan                | 0.9372           | 0.0           | 0.9372      |
| 0.0643        | 47.0  | 940  | 0.1234          | 0.4779   | 0.9559        | 0.9559           | nan                | 0.9559           | 0.0           | 0.9559      |
| 0.2006        | 48.0  | 960  | 0.1213          | 0.4757   | 0.9515        | 0.9515           | nan                | 0.9515           | 0.0           | 0.9515      |
| 0.0783        | 49.0  | 980  | 0.1241          | 0.4726   | 0.9452        | 0.9452           | nan                | 0.9452           | 0.0           | 0.9452      |
| 0.0552        | 50.0  | 1000 | 0.1227          | 0.4744   | 0.9489        | 0.9489           | nan                | 0.9489           | 0.0           | 0.9489      |


### 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-b1", "model-index": [{"name": "segformer-b1-finetuned-segments-ic-chip-sample", "results": []}]}
transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b1 license:other endpoints_compatible region:us