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-ve-U13b-80RX
    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: 0.8478260869565217

vit-base-patch16-224-ve-U13b-80RX

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: 0.6099
Accuracy: 0.8478

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 6
total_train_batch_size: 48
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
1.3857	0.99	17	1.3703	0.5652
1.3134	1.98	34	1.2235	0.4565
1.0384	2.97	51	1.0173	0.5435
0.908	3.96	68	0.8346	0.7826
0.75	4.95	85	0.7343	0.7826
0.5131	6.0	103	0.6099	0.8478
0.395	6.99	120	0.5932	0.7826
0.355	7.98	137	0.7209	0.7391
0.2658	8.97	154	0.5652	0.8043
0.248	9.96	171	0.7103	0.7826
0.2086	10.95	188	0.6788	0.7609
0.1532	12.0	206	0.5725	0.7826
0.147	12.99	223	0.6130	0.8043
0.1145	13.98	240	0.6563	0.8043
0.1053	14.97	257	0.5993	0.8043
0.0971	15.96	274	0.8840	0.7391
0.0947	16.95	291	0.6256	0.8043
0.1055	18.0	309	0.8406	0.7609
0.0974	18.99	326	0.6355	0.8478
0.1215	19.98	343	0.6651	0.8043
0.108	20.97	360	0.8301	0.7826
0.0784	21.96	377	0.8837	0.7609
0.0919	22.95	394	0.6985	0.8043
0.064	24.0	412	0.6426	0.8043
0.0669	24.99	429	0.8102	0.7826
0.0878	25.98	446	0.7863	0.7391
0.0875	26.97	463	0.8777	0.7609
0.0441	27.96	480	0.7324	0.8043
0.088	28.95	497	0.8099	0.7826
0.0739	30.0	515	0.7776	0.8043
0.0598	30.99	532	0.8188	0.7826
0.0443	31.98	549	0.8549	0.8043
0.0376	32.97	566	0.8049	0.7826
0.0375	33.96	583	0.8037	0.8043
0.0346	34.95	600	0.8255	0.8261
0.0471	36.0	618	0.8239	0.8043
0.0669	36.99	635	0.8188	0.8043
0.0438	37.98	652	0.8443	0.8043
0.0549	38.97	669	0.8551	0.8043
0.0622	39.61	680	0.8551	0.8043

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0