---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: weather_classification_ViT
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9679266895761741
    - name: Precision
      type: precision
      value: 0.9679235596755258
    - name: Recall
      type: recall
      value: 0.9679266895761741
    - name: F1
      type: f1
      value: 0.9678827379290899
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# weather_classification_ViT

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1268
- Accuracy: 0.9679
- Precision: 0.9679
- Recall: 0.9679
- F1: 0.9679
- Auc: 0.9974

## 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
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.2811        | 0.2288 | 100  | 0.3139          | 0.8958   | 0.9147    | 0.8958 | 0.8970 | 0.9903 |
| 0.1396        | 0.4577 | 200  | 0.2454          | 0.9278   | 0.9307    | 0.9278 | 0.9282 | 0.9919 |
| 0.3761        | 0.6865 | 300  | 0.2952          | 0.9072   | 0.9117    | 0.9072 | 0.9071 | 0.9889 |
| 0.2365        | 0.9153 | 400  | 0.1797          | 0.9444   | 0.9447    | 0.9444 | 0.9445 | 0.9940 |
| 0.2528        | 1.1442 | 500  | 0.2470          | 0.9278   | 0.9307    | 0.9278 | 0.9278 | 0.9924 |
| 0.2364        | 1.3730 | 600  | 0.2448          | 0.9261   | 0.9306    | 0.9261 | 0.9264 | 0.9934 |
| 0.34          | 1.6018 | 700  | 0.1986          | 0.9404   | 0.9409    | 0.9404 | 0.9405 | 0.9929 |
| 0.2001        | 1.8307 | 800  | 0.1525          | 0.9542   | 0.9548    | 0.9542 | 0.9539 | 0.9960 |
| 0.0958        | 2.0595 | 900  | 0.1783          | 0.9507   | 0.9515    | 0.9507 | 0.9505 | 0.9952 |
| 0.1862        | 2.2883 | 1000 | 0.1654          | 0.9553   | 0.9558    | 0.9553 | 0.9551 | 0.9952 |
| 0.1021        | 2.5172 | 1100 | 0.1654          | 0.9462   | 0.9472    | 0.9462 | 0.9459 | 0.9958 |
| 0.1178        | 2.7460 | 1200 | 0.1591          | 0.9525   | 0.9536    | 0.9525 | 0.9523 | 0.9960 |
| 0.0474        | 2.9748 | 1300 | 0.1299          | 0.9633   | 0.9635    | 0.9633 | 0.9633 | 0.9975 |
| 0.046         | 3.2037 | 1400 | 0.1384          | 0.9628   | 0.9628    | 0.9628 | 0.9627 | 0.9972 |
| 0.0294        | 3.4325 | 1500 | 0.1388          | 0.9645   | 0.9644    | 0.9645 | 0.9644 | 0.9969 |
| 0.1833        | 3.6613 | 1600 | 0.1346          | 0.9633   | 0.9634    | 0.9633 | 0.9633 | 0.9971 |
| 0.0548        | 3.8902 | 1700 | 0.1268          | 0.9679   | 0.9679    | 0.9679 | 0.9679 | 0.9974 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1