---
base_model: nateraw/vit-age-classifier
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
  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: 0.34375
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# image_classification

This model is a fine-tuned version of [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8469
- Accuracy: 0.3438

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- 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: 0.3
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 1    | 1.8452          | 0.3125   |
| No log        | 1.6   | 2    | 1.8435          | 0.35     |
| No log        | 2.4   | 3    | 1.8282          | 0.3688   |
| No log        | 4.0   | 5    | 1.8112          | 0.3563   |
| No log        | 4.8   | 6    | 1.8180          | 0.3312   |
| No log        | 5.6   | 7    | 1.8291          | 0.3375   |
| No log        | 6.4   | 8    | 1.8036          | 0.3563   |
| 1.6711        | 8.0   | 10   | 1.8134          | 0.3375   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1