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# vit-Facial-Confidence

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 FacialConfidence dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2560
- Accuracy: 0.8970

## Model description

Facial Confidence is an image classification model which takes a black and white image of a persons headshot and classifies it as confident or unconfident.

## Intended uses & limitations

The model is intended to help with behavioral analysis tasks. The model is limited to black and white images where the image is a zoomed in headshot of a person (For best output the input image should be as zoomed in on the subjects face as possible without cutting any aspects of their head)


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6103        | 0.0557 | 100  | 0.5715          | 0.7310   |
| 0.554         | 0.1114 | 200  | 0.5337          | 0.7194   |
| 0.4275        | 0.1671 | 300  | 0.5142          | 0.7549   |
| 0.5831        | 0.2228 | 400  | 0.5570          | 0.7345   |
| 0.5804        | 0.2786 | 500  | 0.4909          | 0.7660   |
| 0.5652        | 0.3343 | 600  | 0.4956          | 0.7764   |
| 0.4513        | 0.3900 | 700  | 0.4294          | 0.7972   |
| 0.4217        | 0.4457 | 800  | 0.4619          | 0.7924   |
| 0.435         | 0.5014 | 900  | 0.4563          | 0.7901   |
| 0.3943        | 0.5571 | 1000 | 0.4324          | 0.7917   |
| 0.4136        | 0.6128 | 1100 | 0.4131          | 0.8110   |
| 0.3302        | 0.6685 | 1200 | 0.4516          | 0.8054   |
| 0.4945        | 0.7242 | 1300 | 0.4135          | 0.8164   |
| 0.3729        | 0.7799 | 1400 | 0.4010          | 0.8139   |
| 0.4865        | 0.8357 | 1500 | 0.4145          | 0.8174   |
| 0.4011        | 0.8914 | 1600 | 0.4098          | 0.8112   |
| 0.4287        | 0.9471 | 1700 | 0.3914          | 0.8181   |
| 0.3644        | 1.0028 | 1800 | 0.3948          | 0.8188   |
| 0.3768        | 1.0585 | 1900 | 0.4044          | 0.8266   |
| 0.383         | 1.1142 | 2000 | 0.4363          | 0.8064   |
| 0.4011        | 1.1699 | 2100 | 0.4424          | 0.8025   |
| 0.4079        | 1.2256 | 2200 | 0.4384          | 0.7853   |
| 0.2791        | 1.2813 | 2300 | 0.4491          | 0.8089   |
| 0.3159        | 1.3370 | 2400 | 0.3863          | 0.8274   |
| 0.4306        | 1.3928 | 2500 | 0.3944          | 0.8158   |
| 0.3386        | 1.4485 | 2600 | 0.3835          | 0.8305   |
| 0.395         | 1.5042 | 2700 | 0.3812          | 0.8261   |
| 0.3041        | 1.5599 | 2800 | 0.3736          | 0.8312   |
| 0.3365        | 1.6156 | 2900 | 0.4420          | 0.8097   |
| 0.3697        | 1.6713 | 3000 | 0.3808          | 0.8353   |
| 0.3661        | 1.7270 | 3100 | 0.4046          | 0.8084   |
| 0.3208        | 1.7827 | 3200 | 0.4042          | 0.8328   |
| 0.3511        | 1.8384 | 3300 | 0.4113          | 0.8192   |
| 0.3246        | 1.8942 | 3400 | 0.3611          | 0.8377   |
| 0.3616        | 1.9499 | 3500 | 0.4207          | 0.8231   |
| 0.2726        | 2.0056 | 3600 | 0.3650          | 0.8342   |
| 0.1879        | 2.0613 | 3700 | 0.4334          | 0.8359   |
| 0.2981        | 2.1170 | 3800 | 0.3657          | 0.8435   |
| 0.227         | 2.1727 | 3900 | 0.3948          | 0.8399   |
| 0.3184        | 2.2284 | 4000 | 0.4229          | 0.8377   |
| 0.2391        | 2.2841 | 4100 | 0.3824          | 0.8405   |
| 0.2019        | 2.3398 | 4200 | 0.4628          | 0.8345   |
| 0.1931        | 2.3955 | 4300 | 0.3848          | 0.8448   |
| 0.238         | 2.4513 | 4400 | 0.3948          | 0.8398   |
| 0.2633        | 2.5070 | 4500 | 0.3779          | 0.8440   |
| 0.1829        | 2.5627 | 4600 | 0.3901          | 0.8455   |
| 0.2286        | 2.6184 | 4700 | 0.3797          | 0.8481   |
| 0.2123        | 2.6741 | 4800 | 0.4203          | 0.8502   |
| 0.266         | 2.7298 | 4900 | 0.4073          | 0.8455   |
| 0.1768        | 2.7855 | 5000 | 0.3750          | 0.8498   |
| 0.1659        | 2.8412 | 5100 | 0.3906          | 0.8427   |
| 0.1644        | 2.8969 | 5200 | 0.3833          | 0.8466   |
| 0.241         | 2.9526 | 5300 | 0.4071          | 0.8476   |
| 0.16          | 3.0084 | 5400 | 0.3691          | 0.8530   |
| 0.0788        | 3.0641 | 5500 | 0.4656          | 0.8514   |
| 0.1244        | 3.1198 | 5600 | 0.4990          | 0.8484   |
| 0.1423        | 3.1755 | 5700 | 0.5219          | 0.8475   |
| 0.1279        | 3.2312 | 5800 | 0.5687          | 0.8515   |
| 0.0974        | 3.2869 | 5900 | 0.5386          | 0.8458   |
| 0.065         | 3.3426 | 6000 | 0.5215          | 0.8454   |
| 0.0497        | 3.3983 | 6100 | 0.5161          | 0.8483   |
| 0.1871        | 3.4540 | 6200 | 0.5148          | 0.8523   |
| 0.0891        | 3.5097 | 6300 | 0.4915          | 0.8527   |
| 0.1375        | 3.5655 | 6400 | 0.5067          | 0.8509   |
| 0.1333        | 3.6212 | 6500 | 0.5272          | 0.8532   |
| 0.2635        | 3.6769 | 6600 | 0.5170          | 0.8516   |
| 0.0375        | 3.7326 | 6700 | 0.5148          | 0.8534   |
| 0.1286        | 3.7883 | 6800 | 0.4945          | 0.8543   |
| 0.091         | 3.8440 | 6900 | 0.4948          | 0.8540   |
| 0.1088        | 3.8997 | 7000 | 0.4985          | 0.8532   |
| 0.0598        | 3.9554 | 7100 | 0.4969          | 0.8514   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1

confident unconfident