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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: vit-base-patch16-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.78
- name: Precision
type: precision
value: 0.781535758027584
- name: Recall
type: recall
value: 0.78
---
<!-- 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-base-patch16-224
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: 0.4819
- Accuracy: 0.78
- Precision: 0.7815
- Recall: 0.78
- F1 Score: 0.7807
## 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: 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.5936 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
| No log | 2.0 | 8 | 0.5702 | 0.7208 | 0.6468 | 0.7208 | 0.6283 |
| No log | 3.0 | 12 | 0.5834 | 0.7125 | 0.6933 | 0.7125 | 0.7000 |
| No log | 4.0 | 16 | 0.5471 | 0.7375 | 0.7034 | 0.7375 | 0.6846 |
| No log | 5.0 | 20 | 0.5487 | 0.725 | 0.6938 | 0.725 | 0.6982 |
| No log | 6.0 | 24 | 0.5253 | 0.7458 | 0.7182 | 0.7458 | 0.7116 |
| No log | 7.0 | 28 | 0.5556 | 0.7417 | 0.7393 | 0.7417 | 0.7404 |
| 0.5648 | 8.0 | 32 | 0.5183 | 0.7417 | 0.7155 | 0.7417 | 0.7165 |
| 0.5648 | 9.0 | 36 | 0.5159 | 0.7667 | 0.7504 | 0.7667 | 0.7522 |
| 0.5648 | 10.0 | 40 | 0.5137 | 0.7708 | 0.7579 | 0.7708 | 0.7609 |
| 0.5648 | 11.0 | 44 | 0.5014 | 0.7833 | 0.7693 | 0.7833 | 0.7643 |
| 0.5648 | 12.0 | 48 | 0.5157 | 0.75 | 0.7524 | 0.75 | 0.7511 |
| 0.5648 | 13.0 | 52 | 0.5151 | 0.7417 | 0.7441 | 0.7417 | 0.7428 |
| 0.5648 | 14.0 | 56 | 0.4908 | 0.7792 | 0.7653 | 0.7792 | 0.7663 |
| 0.3814 | 15.0 | 60 | 0.4901 | 0.7833 | 0.7723 | 0.7833 | 0.7747 |
| 0.3814 | 16.0 | 64 | 0.4993 | 0.7667 | 0.7689 | 0.7667 | 0.7677 |
| 0.3814 | 17.0 | 68 | 0.4814 | 0.7792 | 0.7642 | 0.7792 | 0.7627 |
| 0.3814 | 18.0 | 72 | 0.5165 | 0.7583 | 0.7796 | 0.7583 | 0.7656 |
| 0.3814 | 19.0 | 76 | 0.4817 | 0.7958 | 0.7915 | 0.7958 | 0.7933 |
| 0.3814 | 20.0 | 80 | 0.4748 | 0.8083 | 0.8036 | 0.8083 | 0.8054 |
| 0.3814 | 21.0 | 84 | 0.4831 | 0.8042 | 0.8033 | 0.8042 | 0.8037 |
| 0.3814 | 22.0 | 88 | 0.4795 | 0.8083 | 0.8013 | 0.8083 | 0.8032 |
| 0.2354 | 23.0 | 92 | 0.5048 | 0.7708 | 0.7790 | 0.7708 | 0.7743 |
| 0.2354 | 24.0 | 96 | 0.4838 | 0.8042 | 0.7974 | 0.8042 | 0.7995 |
| 0.2354 | 25.0 | 100 | 0.4894 | 0.7833 | 0.7833 | 0.7833 | 0.7833 |
| 0.2354 | 26.0 | 104 | 0.4852 | 0.8 | 0.7914 | 0.8 | 0.7933 |
| 0.2354 | 27.0 | 108 | 0.4882 | 0.8 | 0.7982 | 0.8 | 0.7990 |
| 0.2354 | 28.0 | 112 | 0.4932 | 0.7875 | 0.7929 | 0.7875 | 0.7898 |
| 0.2354 | 29.0 | 116 | 0.4883 | 0.8083 | 0.8036 | 0.8083 | 0.8054 |
| 0.1479 | 30.0 | 120 | 0.4886 | 0.8042 | 0.7974 | 0.8042 | 0.7995 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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