---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: gemini-beauty
  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: 0.5158495350803043
---

<!-- 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. -->

# gemini-beauty

This model is a fine-tuned version of [](https://huggingface.co/) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1226
- Accuracy: 0.5158

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3724        | 1.0   | 148  | 1.2028          | 0.4586   |
| 1.3217        | 2.0   | 296  | 1.1831          | 0.4812   |
| 1.2649        | 3.0   | 444  | 1.1674          | 0.4981   |
| 1.2456        | 4.0   | 592  | 1.1236          | 0.5146   |
| 1.2176        | 5.0   | 740  | 1.1384          | 0.5040   |
| 1.2069        | 6.0   | 888  | 1.1165          | 0.5207   |
| 1.2083        | 7.0   | 1036 | 1.1663          | 0.4985   |
| 1.1663        | 8.0   | 1184 | 1.1226          | 0.5158   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0