
<!-- 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. -->

# vit-base-patch16-224-in21k-v2024-11-07

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1875
- Accuracy: 0.9449
- F1: 0.8664
- Precision: 0.8559
- Recall: 0.8772

## 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: 0.00025
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0808        | 1.1905  | 100  | 0.1574          | 0.9408   | 0.8531 | 0.8614    | 0.8450 |
| 0.0908        | 2.3810  | 200  | 0.1861          | 0.9318   | 0.8327 | 0.8321    | 0.8333 |
| 0.1393        | 3.5714  | 300  | 0.2000          | 0.9298   | 0.8297 | 0.8191    | 0.8406 |
| 0.0911        | 4.7619  | 400  | 0.1639          | 0.9360   | 0.8448 | 0.8345    | 0.8553 |
| 0.095         | 5.9524  | 500  | 0.1779          | 0.9393   | 0.8507 | 0.8519    | 0.8494 |
| 0.0767        | 7.1429  | 600  | 0.1691          | 0.9411   | 0.8563 | 0.8501    | 0.8626 |
| 0.0918        | 8.3333  | 700  | 0.1709          | 0.9375   | 0.8476 | 0.8415    | 0.8538 |
| 0.0742        | 9.5238  | 800  | 0.1703          | 0.9378   | 0.8471 | 0.8477    | 0.8465 |
| 0.0931        | 10.7143 | 900  | 0.1779          | 0.9351   | 0.8388 | 0.8488    | 0.8289 |
| 0.085         | 11.9048 | 1000 | 0.1835          | 0.9351   | 0.8427 | 0.8319    | 0.8538 |
| 0.0712        | 13.0952 | 1100 | 0.1886          | 0.9339   | 0.8377 | 0.8377    | 0.8377 |
| 0.0616        | 14.2857 | 1200 | 0.1863          | 0.9351   | 0.8429 | 0.8310    | 0.8553 |
| 0.0628        | 15.4762 | 1300 | 0.1815          | 0.9387   | 0.8499 | 0.8474    | 0.8523 |
| 0.0571        | 16.6667 | 1400 | 0.1749          | 0.9449   | 0.8685 | 0.8451    | 0.8933 |
| 0.0496        | 17.8571 | 1500 | 0.1781          | 0.9384   | 0.8484 | 0.8502    | 0.8465 |
| 0.0484        | 19.0476 | 1600 | 0.1859          | 0.9354   | 0.8406 | 0.8449    | 0.8363 |
| 0.0487        | 20.2381 | 1700 | 0.1697          | 0.9446   | 0.8642 | 0.8630    | 0.8655 |
| 0.0485        | 21.4286 | 1800 | 0.1876          | 0.9369   | 0.8470 | 0.8362    | 0.8582 |
| 0.042         | 22.6190 | 1900 | 0.1835          | 0.9414   | 0.8576 | 0.8484    | 0.8670 |
| 0.0367        | 23.8095 | 2000 | 0.1844          | 0.9432   | 0.8613 | 0.8557    | 0.8670 |
| 0.0339        | 25.0    | 2100 | 0.1816          | 0.9411   | 0.8578 | 0.8432    | 0.8728 |
| 0.0317        | 26.1905 | 2200 | 0.1817          | 0.9423   | 0.8602 | 0.8480    | 0.8728 |
| 0.0349        | 27.3810 | 2300 | 0.1799          | 0.9426   | 0.8592 | 0.8574    | 0.8611 |
| 0.0355        | 28.5714 | 2400 | 0.1932          | 0.9402   | 0.8540 | 0.8485    | 0.8596 |
| 0.0296        | 29.7619 | 2500 | 0.1875          | 0.9449   | 0.8664 | 0.8559    | 0.8772 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

snowing raining sunny cloudy night snow_on_road partial_snow_on_road clear_pavement wet_pavement iced_lens