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: 1.5032
Accuracy: 0.5375

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: 0.0003
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0207	1.0	10	1.3284	0.4875
0.9015	2.0	20	1.2407	0.5687
0.7775	3.0	30	1.3203	0.55
0.649	4.0	40	1.2159	0.5312
0.5012	5.0	50	1.4273	0.5
0.546	6.0	60	1.3676	0.5
0.4387	7.0	70	1.4644	0.5062
0.3772	8.0	80	1.3247	0.5563
0.3367	9.0	90	1.5443	0.5125
0.3075	10.0	100	1.3807	0.575
0.3138	11.0	110	1.4366	0.5188
0.2298	12.0	120	1.5350	0.4938
0.2454	13.0	130	1.4485	0.5312
0.2273	14.0	140	1.5771	0.475
0.187	15.0	150	1.4757	0.5312

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0

Josh8T/image_classification

image_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Josh8T/
image_classification