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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: 0.7833333333333333
- name: Precision
type: precision
value: 0.7701923076923076
- name: Recall
type: recall
value: 0.7833333333333333
---
<!-- 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: 0.4772
- Accuracy: 0.7833
- Precision: 0.7702
- Recall: 0.7833
- F1 Score: 0.7559
## 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 | 0.6010 | 0.7333 | 0.6725 | 0.7333 | 0.6280 |
| No log | 2.0 | 8 | 0.5552 | 0.7375 | 0.8067 | 0.7375 | 0.6302 |
| No log | 3.0 | 12 | 0.5450 | 0.7542 | 0.7598 | 0.7542 | 0.6782 |
| 0.576 | 4.0 | 16 | 0.5325 | 0.75 | 0.7707 | 0.75 | 0.6641 |
| 0.576 | 5.0 | 20 | 0.5234 | 0.75 | 0.7232 | 0.75 | 0.6900 |
| 0.576 | 6.0 | 24 | 0.5112 | 0.7625 | 0.7506 | 0.7625 | 0.7076 |
| 0.576 | 7.0 | 28 | 0.5082 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
| 0.4876 | 8.0 | 32 | 0.5067 | 0.7667 | 0.7466 | 0.7667 | 0.7288 |
| 0.4876 | 9.0 | 36 | 0.5091 | 0.7792 | 0.7623 | 0.7792 | 0.7528 |
| 0.4876 | 10.0 | 40 | 0.5023 | 0.7583 | 0.7393 | 0.7583 | 0.7045 |
| 0.4876 | 11.0 | 44 | 0.4911 | 0.7708 | 0.7507 | 0.7708 | 0.7435 |
| 0.4379 | 12.0 | 48 | 0.4921 | 0.7667 | 0.7487 | 0.7667 | 0.7513 |
| 0.4379 | 13.0 | 52 | 0.4906 | 0.7917 | 0.7792 | 0.7917 | 0.7680 |
| 0.4379 | 14.0 | 56 | 0.4919 | 0.7875 | 0.7731 | 0.7875 | 0.7645 |
| 0.4003 | 15.0 | 60 | 0.4929 | 0.7833 | 0.7678 | 0.7833 | 0.7587 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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