---
base_model: google/vit-base-patch16-224-in21k
datasets:
- webdataset
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: webdataset
      type: webdataset
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.963716814159292
      name: Accuracy
    - type: f1
      value: 0.9118279569892475
      name: F1
    - type: precision
      value: 0.905982905982906
      name: Precision
    - type: recall
      value: 0.9177489177489178
      name: Recall
---

<!-- 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-finetuned_v2024-7-24-frost

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: 0.0965
- Accuracy: 0.9637
- F1: 0.9118
- Precision: 0.9060
- Recall: 0.9177

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0728        | 1.5625  | 100  | 0.0659          | 0.9841   | 0.9607 | 0.9692    | 0.9524 |
| 0.0871        | 3.125   | 200  | 0.1244          | 0.9566   | 0.8942 | 0.8922    | 0.8961 |
| 0.0999        | 4.6875  | 300  | 0.1043          | 0.9637   | 0.9126 | 0.8992    | 0.9264 |
| 0.0743        | 6.25    | 400  | 0.1043          | 0.9611   | 0.9043 | 0.9083    | 0.9004 |
| 0.0655        | 7.8125  | 500  | 0.0965          | 0.9637   | 0.9118 | 0.9060    | 0.9177 |
| 0.0559        | 9.375   | 600  | 0.1038          | 0.9619   | 0.9087 | 0.8917    | 0.9264 |
| 0.0517        | 10.9375 | 700  | 0.0972          | 0.9584   | 0.8998 | 0.8866    | 0.9134 |
| 0.0407        | 12.5    | 800  | 0.1120          | 0.9637   | 0.9111 | 0.9130    | 0.9091 |
| 0.0513        | 14.0625 | 900  | 0.1093          | 0.9558   | 0.8894 | 0.9095    | 0.8701 |
| 0.0378        | 15.625  | 1000 | 0.1197          | 0.9549   | 0.8889 | 0.8947    | 0.8831 |
| 0.0487        | 17.1875 | 1100 | 0.0955          | 0.9646   | 0.9138 | 0.9099    | 0.9177 |
| 0.0272        | 18.75   | 1200 | 0.1088          | 0.9566   | 0.8928 | 0.9027    | 0.8831 |
| 0.0241        | 20.3125 | 1300 | 0.0979          | 0.9637   | 0.9114 | 0.9095    | 0.9134 |
| 0.0311        | 21.875  | 1400 | 0.1134          | 0.9655   | 0.9158 | 0.9138    | 0.9177 |
| 0.0303        | 23.4375 | 1500 | 0.1092          | 0.9628   | 0.9079 | 0.92      | 0.8961 |
| 0.0225        | 25.0    | 1600 | 0.1121          | 0.9628   | 0.9083 | 0.9163    | 0.9004 |
| 0.0292        | 26.5625 | 1700 | 0.1149          | 0.9619   | 0.9071 | 0.9052    | 0.9091 |
| 0.0261        | 28.125  | 1800 | 0.1107          | 0.9619   | 0.9079 | 0.8983    | 0.9177 |
| 0.0166        | 29.6875 | 1900 | 0.1110          | 0.9611   | 0.9052 | 0.9013    | 0.9091 |


### Framework versions

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