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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.972
---
<!-- 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-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1017
- Accuracy: 0.972
## 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: 450
- eval_batch_size: 450
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1800
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3243 | 1.0 | 46 | 0.2033 | 0.944 |
| 0.1247 | 2.0 | 92 | 0.0791 | 0.976 |
| 0.0937 | 3.0 | 138 | 0.0971 | 0.963 |
| 0.0716 | 4.0 | 184 | 0.0778 | 0.972 |
| 0.0543 | 5.0 | 230 | 0.0654 | 0.98 |
| 0.0367 | 6.0 | 276 | 0.0913 | 0.972 |
| 0.0292 | 7.0 | 322 | 0.0778 | 0.979 |
| 0.0204 | 8.0 | 368 | 0.0914 | 0.971 |
| 0.0161 | 9.0 | 414 | 0.1026 | 0.971 |
| 0.0154 | 10.0 | 460 | 0.1017 | 0.972 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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