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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: 0.8566666666666667
- name: Precision
type: precision
value: 0.8522571872571872
- name: Recall
type: recall
value: 0.8566666666666667
---
<!-- 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: 0.4410
- Accuracy: 0.8567
- Precision: 0.8523
- Recall: 0.8567
- F1 Score: 0.8517
## 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 | 0.5841 | 0.7333 | 0.6770 | 0.7333 | 0.6479 |
| No log | 2.0 | 8 | 0.5727 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 3.0 | 12 | 0.6089 | 0.7208 | 0.7222 | 0.7208 | 0.7215 |
| No log | 4.0 | 16 | 0.5332 | 0.7458 | 0.7205 | 0.7458 | 0.6727 |
| No log | 5.0 | 20 | 0.5314 | 0.7625 | 0.7410 | 0.7625 | 0.7416 |
| No log | 6.0 | 24 | 0.5284 | 0.7583 | 0.7486 | 0.7583 | 0.6959 |
| No log | 7.0 | 28 | 0.5220 | 0.775 | 0.7700 | 0.775 | 0.7286 |
| 0.5564 | 8.0 | 32 | 0.5204 | 0.7833 | 0.7740 | 0.7833 | 0.7481 |
| 0.5564 | 9.0 | 36 | 0.5044 | 0.7708 | 0.7616 | 0.7708 | 0.7650 |
| 0.5564 | 10.0 | 40 | 0.4845 | 0.8125 | 0.8051 | 0.8125 | 0.7941 |
| 0.5564 | 11.0 | 44 | 0.4921 | 0.7833 | 0.7726 | 0.7833 | 0.7757 |
| 0.5564 | 12.0 | 48 | 0.4792 | 0.8167 | 0.8098 | 0.8167 | 0.7996 |
| 0.5564 | 13.0 | 52 | 0.4825 | 0.8 | 0.7889 | 0.8 | 0.7901 |
| 0.5564 | 14.0 | 56 | 0.4987 | 0.8083 | 0.7989 | 0.8083 | 0.8002 |
| 0.3176 | 15.0 | 60 | 0.4970 | 0.8208 | 0.8144 | 0.8208 | 0.8050 |
| 0.3176 | 16.0 | 64 | 0.5076 | 0.8083 | 0.7983 | 0.8083 | 0.7923 |
| 0.3176 | 17.0 | 68 | 0.5227 | 0.8083 | 0.7979 | 0.8083 | 0.7941 |
| 0.3176 | 18.0 | 72 | 0.5132 | 0.8042 | 0.7928 | 0.8042 | 0.7905 |
| 0.3176 | 19.0 | 76 | 0.5081 | 0.8167 | 0.8087 | 0.8167 | 0.8014 |
| 0.3176 | 20.0 | 80 | 0.5140 | 0.8292 | 0.8220 | 0.8292 | 0.8187 |
| 0.3176 | 21.0 | 84 | 0.5392 | 0.8125 | 0.8032 | 0.8125 | 0.7977 |
| 0.3176 | 22.0 | 88 | 0.5175 | 0.7958 | 0.7829 | 0.7958 | 0.7815 |
| 0.1778 | 23.0 | 92 | 0.5109 | 0.8125 | 0.8032 | 0.8125 | 0.7977 |
| 0.1778 | 24.0 | 96 | 0.4961 | 0.8292 | 0.8217 | 0.8292 | 0.8213 |
| 0.1778 | 25.0 | 100 | 0.5251 | 0.8083 | 0.7979 | 0.8083 | 0.7941 |
| 0.1778 | 26.0 | 104 | 0.5192 | 0.8167 | 0.8075 | 0.8167 | 0.8046 |
| 0.1778 | 27.0 | 108 | 0.5030 | 0.8333 | 0.8274 | 0.8333 | 0.8286 |
| 0.1778 | 28.0 | 112 | 0.5031 | 0.8375 | 0.8310 | 0.8375 | 0.8300 |
| 0.1778 | 29.0 | 116 | 0.5164 | 0.8208 | 0.8127 | 0.8208 | 0.8083 |
| 0.1109 | 30.0 | 120 | 0.5192 | 0.8208 | 0.8127 | 0.8208 | 0.8083 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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