This model is a fine-tuned version of google/vit-base-patch16-224 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set:

Loss: 0.1710
Accuracy: 0.9459

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: 0.005
train_batch_size: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
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
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.94	1.0	12	0.2062	0.9323
0.1495	2.0	24	0.1771	0.9405
0.0944	3.0	36	0.1685	0.9459
0.0395	4.0	48	0.1703	0.9452
0.0257	4.5957	55	0.1710	0.9459

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

JSlin/vit-base-patch16-224-finetuned-lora-oxford-pets

vit-base-patch16-224-finetuned-lora-oxford-pets

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

JSlin/
vit-base-patch16-224-finetuned-lora-oxford-pets