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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Vit-Base-30VN
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: vuongnhathien/30VNFoods
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8920634920634921
---
<!-- 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-30VN
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5335
- Accuracy: 0.8921
## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6059 | 1.0 | 275 | 0.5290 | 0.8425 |
| 0.284 | 2.0 | 550 | 0.5239 | 0.8569 |
| 0.1336 | 3.0 | 825 | 0.6038 | 0.8469 |
| 0.0807 | 4.0 | 1100 | 0.5934 | 0.8628 |
| 0.0357 | 5.0 | 1375 | 0.6220 | 0.8588 |
| 0.0206 | 6.0 | 1650 | 0.5674 | 0.8803 |
| 0.0105 | 7.0 | 1925 | 0.5276 | 0.8907 |
| 0.005 | 8.0 | 2200 | 0.5096 | 0.8922 |
| 0.0018 | 9.0 | 2475 | 0.5064 | 0.8926 |
| 0.0035 | 10.0 | 2750 | 0.5055 | 0.8974 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|