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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: 0.9033333333333333
- name: Precision
type: precision
value: 0.892075919335706
- name: Recall
type: recall
value: 0.9033333333333333
---
<!-- 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: 0.2446
- Accuracy: 0.9033
- Precision: 0.8921
- Recall: 0.9033
- F1 Score: 0.8889
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.5037 | 0.8667 | 0.8150 | 0.8667 | 0.8224 |
| No log | 2.0 | 8 | 0.3500 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| No log | 3.0 | 12 | 0.3154 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| 0.5284 | 4.0 | 16 | 0.2974 | 0.8833 | 0.8659 | 0.8833 | 0.8497 |
| 0.5284 | 5.0 | 20 | 0.2954 | 0.8875 | 0.8731 | 0.8875 | 0.8768 |
| 0.5284 | 6.0 | 24 | 0.2721 | 0.8958 | 0.8871 | 0.8958 | 0.8716 |
| 0.5284 | 7.0 | 28 | 0.2679 | 0.8875 | 0.8817 | 0.8875 | 0.8527 |
| 0.3362 | 8.0 | 32 | 0.2634 | 0.8875 | 0.8817 | 0.8875 | 0.8527 |
| 0.3362 | 9.0 | 36 | 0.2507 | 0.9042 | 0.8953 | 0.9042 | 0.8879 |
| 0.3362 | 10.0 | 40 | 0.2439 | 0.9083 | 0.9006 | 0.9083 | 0.8941 |
| 0.3362 | 11.0 | 44 | 0.2589 | 0.8917 | 0.8861 | 0.8917 | 0.8884 |
| 0.3017 | 12.0 | 48 | 0.2428 | 0.9083 | 0.9005 | 0.9083 | 0.9024 |
| 0.3017 | 13.0 | 52 | 0.2543 | 0.9 | 0.8949 | 0.9 | 0.8970 |
| 0.3017 | 14.0 | 56 | 0.2651 | 0.8958 | 0.8944 | 0.8958 | 0.8951 |
| 0.278 | 15.0 | 60 | 0.2637 | 0.8958 | 0.8944 | 0.8958 | 0.8951 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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