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: 0.6281
Accuracy: 0.7059

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
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
No log	1.0	4	0.7020	0.5294
No log	2.0	8	0.6686	0.6471
0.7085	3.0	12	0.6509	0.5882
0.7085	4.0	16	0.6336	0.6471
0.6847	5.0	20	0.6281	0.7059
0.6847	6.0	24	0.6256	0.7059
0.6847	7.0	28	0.6229	0.7059
0.6814	8.0	32	0.6218	0.7059
0.6814	9.0	36	0.6214	0.7059
0.6717	10.0	40	0.6213	0.7059

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

jayanthspratap/vit-base-patch16-224-drfx-CT-classifier

vit-base-patch16-224-drfx-CT-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jayanthspratap/
vit-base-patch16-224-drfx-CT-classifier