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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- fair_face
metrics:
- accuracy
model-index:
- name: initial_ViT_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: fair_face
type: fair_face
config: '0.25'
split: validation
args: '0.25'
metrics:
- name: Accuracy
type: accuracy
value: 0.21252510498448055
---
<!-- 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. -->
# initial_ViT_model
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 fair_face dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6347
- Accuracy: 0.2125
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.7855 | 0.15 | 50 | 4.6444 | 0.0511 |
| 4.4242 | 0.29 | 100 | 4.2124 | 0.1418 |
| 4.0596 | 0.44 | 150 | 3.9402 | 0.1744 |
| 3.859 | 0.59 | 200 | 3.7823 | 0.1956 |
| 3.7392 | 0.74 | 250 | 3.6877 | 0.2105 |
| 3.6424 | 0.88 | 300 | 3.6347 | 0.2125 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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