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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: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8446601941747572
---
<!-- 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: 0.3894
- Accuracy: 0.8447
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 1.0761 | 0.5469 |
| 1.1435 | 2.0 | 10 | 0.6466 | 0.7735 |
| 1.1435 | 3.0 | 15 | 0.4962 | 0.8123 |
| 0.5372 | 4.0 | 20 | 0.4365 | 0.8252 |
| 0.5372 | 5.0 | 25 | 0.4118 | 0.8382 |
| 0.362 | 6.0 | 30 | 0.4031 | 0.8414 |
| 0.362 | 7.0 | 35 | 0.3944 | 0.8511 |
| 0.3028 | 8.0 | 40 | 0.3930 | 0.8414 |
| 0.3028 | 9.0 | 45 | 0.3928 | 0.8479 |
| 0.2708 | 10.0 | 50 | 0.3894 | 0.8447 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.4
- Tokenizers 0.14.1
|