---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: face_poofing_detection
  results: []
---

<!-- 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_poofing_detection

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6273
- Accuracy: 0.9871

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 6.3243        | 0.9846 | 48   | 5.6154          | 0.8919   |
| 4.4794        | 1.9897 | 97   | 4.3516          | 0.9202   |
| 3.8293        | 2.9949 | 146  | 3.6687          | 0.9730   |
| 3.2121        | 4.0    | 195  | 3.1092          | 0.9820   |
| 2.733         | 4.9846 | 243  | 2.6919          | 0.9743   |
| 2.3114        | 5.9897 | 292  | 2.2633          | 0.9923   |
| 1.9962        | 6.9949 | 341  | 1.9594          | 0.9923   |
| 1.7789        | 8.0    | 390  | 1.7641          | 0.9897   |
| 1.6642        | 8.9846 | 438  | 1.6506          | 0.9910   |
| 1.6005        | 9.8462 | 480  | 1.6273          | 0.9871   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1