---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
datasets:
- webdataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vit-base-patch16-384-finetuned_v2024-7-25-frost
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: webdataset
      type: webdataset
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9746666666666667
    - name: F1
      type: f1
      value: 0.9372937293729373
    - name: Precision
      type: precision
      value: 0.9342105263157895
    - name: Recall
      type: recall
      value: 0.9403973509933775
---

<!-- 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-patch16-384-finetuned_v2024-7-25-frost

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0795
- Accuracy: 0.9747
- F1: 0.9373
- Precision: 0.9342
- Recall: 0.9404

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0624        | 1.7544  | 100  | 0.0458          | 0.9867   | 0.9665 | 0.9774    | 0.9558 |
| 0.0729        | 3.5088  | 200  | 0.0942          | 0.9689   | 0.9220 | 0.9303    | 0.9139 |
| 0.0566        | 5.2632  | 300  | 0.0802          | 0.972    | 0.9311 | 0.9221    | 0.9404 |
| 0.051         | 7.0175  | 400  | 0.0965          | 0.9631   | 0.9066 | 0.9243    | 0.8896 |
| 0.0686        | 8.7719  | 500  | 0.0795          | 0.9747   | 0.9373 | 0.9342    | 0.9404 |
| 0.0271        | 10.5263 | 600  | 0.0935          | 0.9693   | 0.9239 | 0.9229    | 0.9249 |
| 0.0273        | 12.2807 | 700  | 0.0975          | 0.9716   | 0.9300 | 0.9219    | 0.9382 |
| 0.0445        | 14.0351 | 800  | 0.0910          | 0.9698   | 0.9248 | 0.9268    | 0.9227 |
| 0.0217        | 15.7895 | 900  | 0.0942          | 0.9698   | 0.9243 | 0.9326    | 0.9161 |
| 0.0257        | 17.5439 | 1000 | 0.0906          | 0.9684   | 0.9210 | 0.9283    | 0.9139 |
| 0.0188        | 19.2982 | 1100 | 0.1028          | 0.9676   | 0.9181 | 0.9338    | 0.9029 |
| 0.0196        | 21.0526 | 1200 | 0.1020          | 0.9698   | 0.9244 | 0.9306    | 0.9183 |
| 0.025         | 22.8070 | 1300 | 0.1005          | 0.9702   | 0.9258 | 0.9289    | 0.9227 |
| 0.009         | 24.5614 | 1400 | 0.0976          | 0.9729   | 0.9324 | 0.9356    | 0.9294 |
| 0.0184        | 26.3158 | 1500 | 0.0987          | 0.9716   | 0.9290 | 0.9332    | 0.9249 |
| 0.0048        | 28.0702 | 1600 | 0.0958          | 0.972    | 0.9301 | 0.9353    | 0.9249 |
| 0.0072        | 29.8246 | 1700 | 0.0948          | 0.972    | 0.9301 | 0.9353    | 0.9249 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1