metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: turcoins-classifier
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: hsyntemiz--turcoins
          split: test
          args: hsyntemiz--turcoins
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9548611111111112

turcoins-classifier

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1763
Accuracy: 0.9549

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
1.9277	1.0	146	1.9660	0.7726
1.6627	2.0	292	1.7154	0.7917
1.4071	2.99	438	1.4120	0.8079
1.09	4.0	585	1.1225	0.8362
0.8086	5.0	731	0.8917	0.8675
0.7636	6.0	877	0.7596	0.8709
0.611	6.99	1023	0.6493	0.8883
0.4605	8.0	1170	0.5899	0.8872
0.37	9.0	1316	0.4978	0.9045
0.3882	10.0	1462	0.4424	0.9132
0.3139	10.99	1608	0.3969	0.9115
0.3178	12.0	1755	0.3525	0.9294
0.2796	13.0	1901	0.3552	0.9161
0.2571	14.0	2047	0.3189	0.9265
0.2481	14.99	2193	0.2945	0.9358
0.1875	16.0	2340	0.2647	0.9392
0.1861	17.0	2486	0.2404	0.9410
0.1839	18.0	2632	0.2556	0.9421
0.173	18.99	2778	0.2387	0.9462
0.1837	20.0	2925	0.2049	0.9485
0.1724	21.0	3071	0.2065	0.9525
0.1399	22.0	3217	0.2089	0.9404
0.1696	22.99	3363	0.1957	0.9497
0.1405	24.0	3510	0.1848	0.9554
0.1009	25.0	3656	0.1912	0.9520
0.1126	26.0	3802	0.1717	0.9560
0.1336	26.99	3948	0.1699	0.9589
0.1046	28.0	4095	0.1600	0.9601
0.126	29.0	4241	0.1839	0.9520
0.0882	29.95	4380	0.1763	0.9549

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.12.0
Tokenizers 0.13.3