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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: vit-base-patch16-224-in21k-finetuned-FER2013
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: 0.8732149076976663
---
<!-- 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-in21k-finetuned-FER2013
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: 0.3264
- Accuracy: 0.8732
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4811 | 1.0 | 202 | 0.4315 | 0.8004 |
| 0.4287 | 2.0 | 404 | 0.3579 | 0.8433 |
| 0.4184 | 3.0 | 606 | 0.3517 | 0.8467 |
| 0.3931 | 4.0 | 808 | 0.3308 | 0.8555 |
| 0.3667 | 5.0 | 1010 | 0.3204 | 0.8610 |
| 0.3545 | 6.0 | 1212 | 0.3144 | 0.8659 |
| 0.3137 | 7.0 | 1414 | 0.3308 | 0.8642 |
| 0.3178 | 8.0 | 1616 | 0.3230 | 0.8645 |
| 0.2998 | 9.0 | 1818 | 0.3206 | 0.8708 |
| 0.2773 | 10.0 | 2020 | 0.3264 | 0.8732 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|