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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-Facial-Confidence
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. -->
# 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 uncofident.
## 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
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