---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: face_predict
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:800]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5625
---

<!-- 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. -->

# face_predict

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: 1.2322
- Accuracy: 0.5625

## 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: 6
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.9   | 3    | 2.0747          | 0.1187   |
| No log        | 1.8   | 6    | 2.0728          | 0.1375   |
| 2.0713        | 3.0   | 10   | 2.0449          | 0.2      |
| 2.0713        | 3.9   | 13   | 2.0225          | 0.2562   |
| 2.0713        | 4.8   | 16   | 1.9779          | 0.2938   |
| 1.9642        | 6.0   | 20   | 1.8985          | 0.3688   |
| 1.9642        | 6.9   | 23   | 1.8440          | 0.4188   |
| 1.9642        | 7.8   | 26   | 1.7593          | 0.4437   |
| 1.7442        | 9.0   | 30   | 1.6551          | 0.4875   |
| 1.7442        | 9.9   | 33   | 1.5996          | 0.4875   |
| 1.7442        | 10.8  | 36   | 1.5324          | 0.5188   |
| 1.5402        | 12.0  | 40   | 1.5053          | 0.525    |
| 1.5402        | 12.9  | 43   | 1.4543          | 0.5188   |
| 1.5402        | 13.8  | 46   | 1.4335          | 0.5188   |
| 1.4064        | 15.0  | 50   | 1.3768          | 0.5938   |
| 1.4064        | 15.9  | 53   | 1.3583          | 0.6      |
| 1.4064        | 16.8  | 56   | 1.3464          | 0.575    |
| 1.2844        | 18.0  | 60   | 1.3245          | 0.6125   |
| 1.2844        | 18.9  | 63   | 1.3265          | 0.5563   |
| 1.2844        | 19.8  | 66   | 1.2899          | 0.5813   |
| 1.1834        | 21.0  | 70   | 1.2863          | 0.5625   |
| 1.1834        | 21.9  | 73   | 1.2939          | 0.5687   |
| 1.1834        | 22.8  | 76   | 1.2508          | 0.5938   |
| 1.1046        | 24.0  | 80   | 1.2604          | 0.5563   |
| 1.1046        | 24.9  | 83   | 1.2344          | 0.6062   |
| 1.1046        | 25.8  | 86   | 1.2124          | 0.6125   |
| 1.0379        | 27.0  | 90   | 1.2053          | 0.6312   |
| 1.0379        | 27.9  | 93   | 1.3067          | 0.5375   |
| 1.0379        | 28.8  | 96   | 1.2247          | 0.5875   |
| 1.0064        | 30.0  | 100  | 1.2060          | 0.625    |
| 1.0064        | 30.9  | 103  | 1.2308          | 0.575    |
| 1.0064        | 31.8  | 106  | 1.1936          | 0.6188   |
| 0.9611        | 33.0  | 110  | 1.2257          | 0.5938   |
| 0.9611        | 33.9  | 113  | 1.2302          | 0.5563   |
| 0.9611        | 34.8  | 116  | 1.2172          | 0.6      |
| 0.9351        | 36.0  | 120  | 1.2355          | 0.55     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1