---
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-patch16-224-finetuned-landscape-test
  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: 0.909375
---

<!-- 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-large-patch16-224-finetuned-landscape-test

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3101
- Accuracy: 0.9094

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3906        | 1.0   | 10   | 1.1521          | 0.4969   |
| 0.914         | 2.0   | 20   | 0.7812          | 0.6687   |
| 0.6704        | 3.0   | 30   | 0.5566          | 0.7688   |
| 0.4609        | 4.0   | 40   | 0.4363          | 0.8313   |
| 0.404         | 5.0   | 50   | 0.4807          | 0.8156   |
| 0.3948        | 6.0   | 60   | 0.4216          | 0.8531   |
| 0.3535        | 7.0   | 70   | 0.3281          | 0.8688   |
| 0.3107        | 8.0   | 80   | 0.2972          | 0.9      |
| 0.3086        | 9.0   | 90   | 0.3328          | 0.8812   |
| 0.2564        | 10.0  | 100  | 0.3517          | 0.8875   |
| 0.2654        | 11.0  | 110  | 0.3985          | 0.8594   |
| 0.2733        | 12.0  | 120  | 0.2870          | 0.9062   |
| 0.2511        | 13.0  | 130  | 0.4177          | 0.8875   |
| 0.2762        | 14.0  | 140  | 0.3579          | 0.8938   |
| 0.2188        | 15.0  | 150  | 0.3348          | 0.8906   |
| 0.2265        | 16.0  | 160  | 0.3046          | 0.9031   |
| 0.2054        | 17.0  | 170  | 0.3305          | 0.8969   |
| 0.1951        | 18.0  | 180  | 0.3576          | 0.8812   |
| 0.1762        | 19.0  | 190  | 0.3985          | 0.8812   |
| 0.2264        | 20.0  | 200  | 0.3711          | 0.9031   |
| 0.1958        | 21.0  | 210  | 0.3259          | 0.8875   |
| 0.1765        | 22.0  | 220  | 0.3804          | 0.8938   |
| 0.1859        | 23.0  | 230  | 0.3464          | 0.9      |
| 0.1915        | 24.0  | 240  | 0.3742          | 0.8906   |
| 0.1667        | 25.0  | 250  | 0.3200          | 0.9062   |
| 0.1744        | 26.0  | 260  | 0.3545          | 0.8938   |
| 0.1595        | 27.0  | 270  | 0.3101          | 0.9094   |
| 0.1793        | 28.0  | 280  | 0.3230          | 0.8969   |
| 0.1596        | 29.0  | 290  | 0.3268          | 0.9      |
| 0.169         | 30.0  | 300  | 0.3321          | 0.8969   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1