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# vit-base-patch16-224-in21k-v2025-1-31

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 webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3391
- Accuracy: 0.8973
- F1: 0.7668
- Precision: 0.7866
- Recall: 0.7480

## 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: 0.0002
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4871        | 0.5682  | 100  | 0.4866          | 0.7903   | 0.1400 | 0.9449    | 0.0756 |
| 0.4151        | 1.1364  | 200  | 0.4007          | 0.8361   | 0.4540 | 0.9159    | 0.3018 |
| 0.3517        | 1.7045  | 300  | 0.3460          | 0.8671   | 0.6481 | 0.8060    | 0.5419 |
| 0.3337        | 2.2727  | 400  | 0.3202          | 0.8777   | 0.7034 | 0.7768    | 0.6427 |
| 0.3128        | 2.8409  | 500  | 0.2995          | 0.8774   | 0.6943 | 0.7940    | 0.6169 |
| 0.3199        | 3.4091  | 600  | 0.2980          | 0.8771   | 0.6960 | 0.7880    | 0.6232 |
| 0.3094        | 3.9773  | 700  | 0.3051          | 0.8764   | 0.7031 | 0.7679    | 0.6484 |
| 0.3068        | 4.5455  | 800  | 0.2753          | 0.8900   | 0.7409 | 0.7915    | 0.6963 |
| 0.3003        | 5.1136  | 900  | 0.2699          | 0.8890   | 0.7351 | 0.7973    | 0.6818 |
| 0.3012        | 5.6818  | 1000 | 0.2860          | 0.8799   | 0.7256 | 0.7495    | 0.7032 |
| 0.267         | 6.25    | 1100 | 0.2848          | 0.8832   | 0.7216 | 0.7812    | 0.6704 |
| 0.2364        | 6.8182  | 1200 | 0.2608          | 0.8896   | 0.7399 | 0.7903    | 0.6957 |
| 0.2401        | 7.3864  | 1300 | 0.2695          | 0.8885   | 0.7406 | 0.7798    | 0.7051 |
| 0.219         | 7.9545  | 1400 | 0.2599          | 0.8909   | 0.7413 | 0.7975    | 0.6925 |
| 0.1985        | 8.5227  | 1500 | 0.2668          | 0.8898   | 0.7421 | 0.7863    | 0.7026 |
| 0.1986        | 9.0909  | 1600 | 0.2762          | 0.8851   | 0.7316 | 0.7737    | 0.6938 |
| 0.1988        | 9.6591  | 1700 | 0.2765          | 0.8862   | 0.7404 | 0.7632    | 0.7190 |
| 0.167         | 10.2273 | 1800 | 0.2630          | 0.8940   | 0.7594 | 0.7788    | 0.7410 |
| 0.207         | 10.7955 | 1900 | 0.2637          | 0.8923   | 0.7557 | 0.7745    | 0.7379 |
| 0.1811        | 11.3636 | 2000 | 0.2568          | 0.8946   | 0.7609 | 0.7798    | 0.7429 |
| 0.171         | 11.9318 | 2100 | 0.2607          | 0.8935   | 0.7527 | 0.7906    | 0.7183 |
| 0.1571        | 12.5    | 2200 | 0.2552          | 0.8972   | 0.7708 | 0.7755    | 0.7662 |
| 0.1234        | 13.0682 | 2300 | 0.2676          | 0.8993   | 0.7694 | 0.7964    | 0.7442 |
| 0.1299        | 13.6364 | 2400 | 0.2683          | 0.8970   | 0.7655 | 0.7875    | 0.7448 |
| 0.1335        | 14.2045 | 2500 | 0.2823          | 0.8949   | 0.7559 | 0.7944    | 0.7209 |
| 0.1235        | 14.7727 | 2600 | 0.2753          | 0.8976   | 0.7671 | 0.7880    | 0.7473 |
| 0.1163        | 15.3409 | 2700 | 0.2884          | 0.8962   | 0.7644 | 0.7836    | 0.7461 |
| 0.1111        | 15.9091 | 2800 | 0.2770          | 0.8973   | 0.7675 | 0.7847    | 0.7511 |
| 0.1128        | 16.4773 | 2900 | 0.2773          | 0.8987   | 0.7722 | 0.7843    | 0.7606 |
| 0.0982        | 17.0455 | 3000 | 0.2754          | 0.8993   | 0.7716 | 0.7905    | 0.7536 |
| 0.1115        | 17.6136 | 3100 | 0.2956          | 0.8972   | 0.7640 | 0.7927    | 0.7372 |
| 0.07          | 18.1818 | 3200 | 0.2961          | 0.8977   | 0.7683 | 0.7863    | 0.7511 |
| 0.0993        | 18.75   | 3300 | 0.3041          | 0.8959   | 0.7639 | 0.7826    | 0.7461 |
| 0.0779        | 19.3182 | 3400 | 0.3012          | 0.9      | 0.7745 | 0.7889    | 0.7606 |
| 0.0691        | 19.8864 | 3500 | 0.3075          | 0.8964   | 0.7674 | 0.7784    | 0.7568 |
| 0.063         | 20.4545 | 3600 | 0.3271          | 0.8912   | 0.7509 | 0.7770    | 0.7265 |
| 0.0668        | 21.0227 | 3700 | 0.3229          | 0.8952   | 0.7649 | 0.7745    | 0.7555 |
| 0.0573        | 21.5909 | 3800 | 0.3236          | 0.8960   | 0.7626 | 0.7869    | 0.7398 |
| 0.0668        | 22.1591 | 3900 | 0.3251          | 0.8972   | 0.7629 | 0.7955    | 0.7328 |
| 0.062         | 22.7273 | 4000 | 0.3221          | 0.8987   | 0.7702 | 0.7895    | 0.7517 |
| 0.0647        | 23.2955 | 4100 | 0.3179          | 0.8959   | 0.7663 | 0.7767    | 0.7561 |
| 0.0417        | 23.8636 | 4200 | 0.3323          | 0.8969   | 0.7662 | 0.7847    | 0.7486 |
| 0.0623        | 24.4318 | 4300 | 0.3396          | 0.8945   | 0.7602 | 0.7804    | 0.7410 |
| 0.0361        | 25.0    | 4400 | 0.3418          | 0.8959   | 0.7623 | 0.7863    | 0.7398 |
| 0.0334        | 25.5682 | 4500 | 0.3404          | 0.8984   | 0.7703 | 0.7870    | 0.7543 |
| 0.0326        | 26.1364 | 4600 | 0.3376          | 0.8967   | 0.7676 | 0.7801    | 0.7555 |
| 0.052         | 26.7045 | 4700 | 0.3395          | 0.8972   | 0.7679 | 0.7827    | 0.7536 |
| 0.0341        | 27.2727 | 4800 | 0.3440          | 0.8953   | 0.7638 | 0.7783    | 0.7498 |
| 0.0459        | 27.8409 | 4900 | 0.3406          | 0.8980   | 0.7689 | 0.7869    | 0.7517 |
| 0.0392        | 28.4091 | 5000 | 0.3389          | 0.8977   | 0.7680 | 0.7870    | 0.7498 |
| 0.0407        | 28.9773 | 5100 | 0.3410          | 0.8976   | 0.7677 | 0.7865    | 0.7498 |
| 0.0445        | 29.5455 | 5200 | 0.3395          | 0.8969   | 0.7661 | 0.7851    | 0.7480 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

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