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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: attraction-classifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: .faces
split: train
args: .faces
metrics:
- name: Accuracy
type: accuracy
value: 0.8254437869822485
---
<!-- 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. -->
# attraction-classifier
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: 0.4452
- Accuracy: 0.8254
## 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: 16
- eval_batch_size: 16
- seed: 69
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.622 | 0.99 | 42 | 0.4917 | 0.7825 |
| 0.5018 | 1.99 | 84 | 0.4727 | 0.7840 |
| 0.4308 | 2.98 | 126 | 0.4231 | 0.8254 |
| 0.3811 | 4.0 | 169 | 0.4085 | 0.8254 |
| 0.304 | 4.99 | 211 | 0.4239 | 0.8062 |
| 0.2844 | 5.99 | 253 | 0.4529 | 0.8047 |
| 0.2549 | 6.98 | 295 | 0.4248 | 0.8254 |
| 0.2162 | 8.0 | 338 | 0.4202 | 0.8195 |
| 0.2073 | 8.99 | 380 | 0.4388 | 0.8328 |
| 0.1751 | 9.94 | 420 | 0.4452 | 0.8254 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|