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: 1.4916
Accuracy: 0.4688

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	20	2.0695	0.1812
No log	2.0	40	2.0566	0.2062
No log	3.0	60	2.0300	0.2625
No log	4.0	80	1.9731	0.3125
No log	5.0	100	1.8858	0.3375
No log	6.0	120	1.7904	0.3438
No log	7.0	140	1.7051	0.3875
No log	8.0	160	1.6312	0.4
No log	9.0	180	1.5429	0.45
No log	10.0	200	1.4916	0.4688

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

FarizFirdaus/image_classification

image_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

FarizFirdaus/
image_classification