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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: google-vit-base-patch16-224-cartoon-face-recognition
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9004629629629629
- name: Precision
type: precision
value: 0.9066341895316832
- name: Recall
type: recall
value: 0.9004629629629629
- name: F1
type: f1
value: 0.8984296743444529
---
<!-- 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. -->
# google-vit-base-patch16-224-cartoon-face-recognition
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Accuracy: 0.9005
- Precision: 0.9066
- Recall: 0.9005
- F1: 0.8984
## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.89 | 6 | 0.5459 | 0.8611 | 0.8683 | 0.8611 | 0.8577 |
| 0.0812 | 1.89 | 12 | 0.4703 | 0.8796 | 0.8833 | 0.8796 | 0.8764 |
| 0.0812 | 2.89 | 18 | 0.4430 | 0.8935 | 0.8969 | 0.8935 | 0.8906 |
| 0.0307 | 3.89 | 24 | 0.4045 | 0.8819 | 0.8849 | 0.8819 | 0.8767 |
| 0.0091 | 4.89 | 30 | 0.3672 | 0.9005 | 0.9025 | 0.9005 | 0.8980 |
| 0.0091 | 5.89 | 36 | 0.3841 | 0.9028 | 0.9125 | 0.9028 | 0.9011 |
| 0.0043 | 6.89 | 42 | 0.3926 | 0.9005 | 0.9073 | 0.9005 | 0.8972 |
| 0.0043 | 7.89 | 48 | 0.3786 | 0.8958 | 0.9005 | 0.8958 | 0.8931 |
| 0.0031 | 8.89 | 54 | 0.3791 | 0.9028 | 0.9091 | 0.9028 | 0.9007 |
| 0.002 | 9.89 | 60 | 0.3677 | 0.9028 | 0.9106 | 0.9028 | 0.9001 |
| 0.002 | 10.89 | 66 | 0.3740 | 0.9028 | 0.9099 | 0.9028 | 0.9007 |
| 0.0027 | 11.89 | 72 | 0.3869 | 0.8981 | 0.9043 | 0.8981 | 0.8956 |
| 0.0027 | 12.89 | 78 | 0.3801 | 0.8981 | 0.9021 | 0.8981 | 0.8954 |
| 0.004 | 13.89 | 84 | 0.3674 | 0.9051 | 0.9113 | 0.9051 | 0.9028 |
| 0.0024 | 14.89 | 90 | 0.3620 | 0.9051 | 0.9096 | 0.9051 | 0.9027 |
| 0.0024 | 15.89 | 96 | 0.3670 | 0.9028 | 0.9089 | 0.9028 | 0.9006 |
| 0.0021 | 16.89 | 102 | 0.3827 | 0.9005 | 0.9065 | 0.9005 | 0.8980 |
| 0.0021 | 17.89 | 108 | 0.3748 | 0.8981 | 0.9049 | 0.8981 | 0.8958 |
| 0.0022 | 18.89 | 114 | 0.3825 | 0.9028 | 0.9101 | 0.9028 | 0.9006 |
| 0.0019 | 19.89 | 120 | 0.3707 | 0.9005 | 0.9066 | 0.9005 | 0.8984 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
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