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: 0.6555
Accuracy: 0.8421

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: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8073	0.9787	23	1.4742	0.5211
0.9801	2.0	47	1.2410	0.5526
0.5808	2.9787	70	0.9728	0.7053
0.3797	4.0	94	0.7751	0.7632
0.2559	4.9787	117	0.8020	0.7684
0.1131	6.0	141	0.7116	0.8105
0.1207	6.9787	164	0.7258	0.8105
0.1068	8.0	188	0.6817	0.8316
0.0559	8.9787	211	0.6589	0.8368
0.0529	9.7872	230	0.6555	0.8421

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1

pk3388/vit-base-patch16-224-high-vit

vit-base-patch16-224-high-vit

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

pk3388/
vit-base-patch16-224-high-vit