metadata

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-25ep
    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: 0.8486111111111111

vit-base-25ep

This model is a fine-tuned version of google/vit-base-patch16-224 on the vuongnhathien/30VNFoods dataset. It achieves the following results on the evaluation set:

Loss: 0.5506
Accuracy: 0.8486

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: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6167	1.0	275	0.5712	0.8354
0.3183	2.0	550	0.5564	0.8406
0.1729	3.0	825	0.5955	0.8433
0.139	4.0	1100	0.6453	0.8406
0.0775	5.0	1375	0.6044	0.8517
0.0784	6.0	1650	0.7265	0.8414
0.0502	7.0	1925	0.6977	0.8533
0.0525	8.0	2200	0.7100	0.8549
0.0311	9.0	2475	0.7423	0.8525
0.026	10.0	2750	0.7901	0.8461
0.0183	11.0	3025	0.7261	0.8592
0.0218	12.0	3300	0.8014	0.8485
0.0135	13.0	3575	0.7391	0.8584
0.0066	14.0	3850	0.6938	0.8740
0.0047	15.0	4125	0.6765	0.8815
0.0052	16.0	4400	0.6611	0.8839
0.0033	17.0	4675	0.6794	0.8803
0.0037	18.0	4950	0.6724	0.8811
0.0026	19.0	5225	0.6759	0.8875
0.0031	20.0	5500	0.6699	0.8855
0.0028	21.0	5775	0.6720	0.8847
0.0029	22.0	6050	0.6746	0.8843
0.0016	23.0	6325	0.6731	0.8859
0.0016	24.0	6600	0.6759	0.8859
0.0019	25.0	6875	0.6767	0.8847

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2