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---
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
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