metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: image_classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.53125

image_classification

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: 1.2727
Accuracy: 0.5312

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0804	1.0	10	2.0714	0.1625
2.0428	2.0	20	2.0324	0.2313
1.9463	3.0	30	1.8978	0.3438
1.7768	4.0	40	1.7234	0.375
1.6163	5.0	50	1.6029	0.4188
1.509	6.0	60	1.5122	0.5
1.4118	7.0	70	1.4839	0.4375
1.3381	8.0	80	1.4268	0.475
1.2653	9.0	90	1.4095	0.4813
1.1979	10.0	100	1.3504	0.5375
1.1219	11.0	110	1.3293	0.4875
1.0858	12.0	120	1.3023	0.4875
1.0214	13.0	130	1.3063	0.5188
1.0085	14.0	140	1.3306	0.5312
0.9615	15.0	150	1.2838	0.5
0.9277	16.0	160	1.3073	0.5125
0.898	17.0	170	1.2606	0.5437
0.8747	18.0	180	1.3116	0.5437
0.8657	19.0	190	1.3171	0.5375
0.8462	20.0	200	1.2619	0.525

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3