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---
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
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