metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-U8-40
    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: 0.8666666666666667

vit-base-patch16-224-U8-40

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

Loss: 0.5495
Accuracy: 0.8667

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: 5.5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3457	1.0	20	1.3128	0.45
1.1498	2.0	40	1.1047	0.5667
0.8312	3.0	60	0.8231	0.65
0.5334	4.0	80	0.5719	0.8167
0.3582	5.0	100	0.5495	0.8667
0.2389	6.0	120	0.5801	0.8333
0.2055	7.0	140	0.6727	0.8167
0.1738	8.0	160	0.7238	0.8
0.1556	9.0	180	0.7665	0.75
0.1461	10.0	200	0.8229	0.7667
0.1401	11.0	220	0.8102	0.75
0.08	12.0	240	0.6609	0.8333
0.0989	13.0	260	0.6703	0.8333
0.0773	14.0	280	0.7303	0.8167
0.089	15.0	300	0.7757	0.7833
0.11	16.0	320	0.7279	0.8
0.086	17.0	340	0.8491	0.7833
0.0671	18.0	360	0.7950	0.8
0.0775	19.0	380	0.6753	0.85
0.0636	20.0	400	0.7881	0.8333
0.0737	21.0	420	0.7450	0.8333
0.0583	22.0	440	0.8295	0.8
0.0646	23.0	460	0.8227	0.8333
0.0637	24.0	480	0.9030	0.7833
0.0647	25.0	500	0.8656	0.8
0.0477	26.0	520	0.8362	0.8
0.0481	27.0	540	0.8389	0.8
0.0355	28.0	560	0.9424	0.8
0.0352	29.0	580	0.8963	0.8
0.0335	30.0	600	0.8560	0.8333
0.0372	31.0	620	0.7250	0.8333
0.0389	32.0	640	0.7846	0.8167
0.0425	33.0	660	0.8532	0.8333
0.0404	34.0	680	0.8169	0.8333
0.0359	35.0	700	0.8682	0.8167
0.0231	36.0	720	0.9362	0.8167
0.027	37.0	740	0.9139	0.8167
0.0214	38.0	760	0.8782	0.8167
0.0191	39.0	780	0.8794	0.8167
0.0293	40.0	800	0.8929	0.8167

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0