metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: results
    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: 0.575

results

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: 1.3098
Accuracy: 0.575

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	10	1.8622	0.2875
1.7517	2.0	20	1.6548	0.45
1.7517	3.0	30	1.4987	0.4688
0.8128	4.0	40	1.3997	0.5125
0.8128	5.0	50	1.3707	0.5125
0.2863	6.0	60	1.3209	0.525
0.2863	7.0	70	1.3131	0.55
0.0776	8.0	80	1.2887	0.5563
0.0776	9.0	90	1.2996	0.5687
0.0267	10.0	100	1.3032	0.5563
0.0267	11.0	110	1.3003	0.5625
0.0156	12.0	120	1.3069	0.5625
0.0156	13.0	130	1.3039	0.5687
0.0117	14.0	140	1.3037	0.5687
0.0117	15.0	150	1.3059	0.5687
0.0098	16.0	160	1.3098	0.575
0.0098	17.0	170	1.3095	0.5625
0.0088	18.0	180	1.3107	0.5625
0.0088	19.0	190	1.3112	0.5687
0.0083	20.0	200	1.3112	0.5687

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1