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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: smtn_girls_likeOrNot
split: train
args: smtn_girls_likeOrNot
metrics:
- name: Accuracy
type: accuracy
value: 0.8286558345642541
---
<!-- 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: 0.3887
- Accuracy: 0.8287
## 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: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5824 | 0.99 | 42 | 0.5195 | 0.7829 |
| 0.4574 | 2.0 | 85 | 0.4473 | 0.8154 |
| 0.4165 | 2.99 | 127 | 0.3977 | 0.8316 |
| 0.346 | 4.0 | 170 | 0.3881 | 0.8390 |
| 0.3025 | 4.99 | 212 | 0.3950 | 0.8213 |
| 0.3085 | 6.0 | 255 | 0.3965 | 0.8139 |
| 0.2646 | 6.99 | 297 | 0.3895 | 0.8552 |
| 0.3022 | 8.0 | 340 | 0.3828 | 0.8390 |
| 0.2384 | 8.99 | 382 | 0.3878 | 0.8375 |
| 0.2162 | 9.88 | 420 | 0.3887 | 0.8287 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|