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# vit-utility-poles

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the utility-poles-local dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1032
- Accuracy: 0.1818
- Precision: 0.1363
- Recall: 0.1818
- F1: 0.1347

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 20   | 4.4879          | 0.0260   | 0.0017    | 0.0260 | 0.0031 |
| No log        | 2.0   | 40   | 4.4301          | 0.0390   | 0.0018    | 0.0390 | 0.0034 |
| No log        | 3.0   | 60   | 4.3691          | 0.0649   | 0.0549    | 0.0649 | 0.0379 |
| No log        | 4.0   | 80   | 4.3187          | 0.0779   | 0.1134    | 0.0779 | 0.0565 |
| 3.9392        | 5.0   | 100  | 4.2822          | 0.1169   | 0.1133    | 0.1169 | 0.0926 |
| 3.9392        | 6.0   | 120  | 4.2440          | 0.1299   | 0.1187    | 0.1299 | 0.0998 |
| 3.9392        | 7.0   | 140  | 4.2341          | 0.1169   | 0.1239    | 0.1169 | 0.1001 |
| 3.9392        | 8.0   | 160  | 4.2013          | 0.1558   | 0.1449    | 0.1558 | 0.1262 |
| 3.9392        | 9.0   | 180  | 4.1658          | 0.1688   | 0.1301    | 0.1688 | 0.1303 |
| 2.5046        | 10.0  | 200  | 4.1695          | 0.1429   | 0.1168    | 0.1429 | 0.1098 |
| 2.5046        | 11.0  | 220  | 4.1433          | 0.1688   | 0.1245    | 0.1688 | 0.1257 |
| 2.5046        | 12.0  | 240  | 4.1359          | 0.1818   | 0.1428    | 0.1818 | 0.1400 |
| 2.5046        | 13.0  | 260  | 4.1293          | 0.1688   | 0.1276    | 0.1688 | 0.1259 |
| 2.5046        | 14.0  | 280  | 4.1201          | 0.1688   | 0.1246    | 0.1688 | 0.1237 |
| 1.6736        | 15.0  | 300  | 4.1130          | 0.1818   | 0.1445    | 0.1818 | 0.1413 |
| 1.6736        | 16.0  | 320  | 4.1112          | 0.1818   | 0.1367    | 0.1818 | 0.1354 |
| 1.6736        | 17.0  | 340  | 4.1046          | 0.1688   | 0.1285    | 0.1688 | 0.1267 |
| 1.6736        | 18.0  | 360  | 4.1025          | 0.1818   | 0.1361    | 0.1818 | 0.1344 |
| 1.6736        | 19.0  | 380  | 4.1043          | 0.1818   | 0.1363    | 0.1818 | 0.1347 |
| 1.3141        | 20.0  | 400  | 4.1032          | 0.1818   | 0.1363    | 0.1818 | 0.1347 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu118
- Datasets 3.5.0
- Tokenizers 0.21.1

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