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: 0.4274
Accuracy: 0.8243

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: 32
eval_batch_size: 32
seed: 69
gradient_accumulation_steps: 16
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6782	1.78	15	0.5922	0.7008
0.5096	3.56	30	0.5153	0.7552
0.4434	5.33	45	0.4520	0.7762
0.3844	7.11	60	0.4381	0.8013
0.3642	8.89	75	0.4359	0.8054
0.322	10.67	90	0.4086	0.8138
0.2845	12.44	105	0.4111	0.8201
0.2588	14.22	120	0.4100	0.8159
0.2516	16.0	135	0.4122	0.8389
0.2375	17.78	150	0.4085	0.8243
0.2309	19.56	165	0.4149	0.8117
0.2175	21.33	180	0.4274	0.8243

Framework versions

Transformers 4.37.2
Pytorch 2.0.1+cu117
Datasets 2.15.0
Tokenizers 0.15.0

ongkn/attraction-classifier

attraction-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ongkn/
attraction-classifier