This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.5014
Accuracy: 0.4333

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: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	2.4111	0.1333
No log	2.0	2	2.0318	0.1667
No log	3.0	3	2.0433	0.1667
No log	4.0	4	2.0298	0.1
No log	5.0	5	1.9400	0.2
No log	6.0	6	1.8599	0.2667
No log	7.0	7	1.7569	0.2333
No log	8.0	8	1.8134	0.1333
No log	9.0	9	1.6201	0.3667
1.971	10.0	10	1.6919	0.2667
1.971	11.0	11	1.6849	0.2667
1.971	12.0	12	1.6344	0.3333
1.971	13.0	13	1.6805	0.3
1.971	14.0	14	1.5784	0.4667
1.971	15.0	15	1.5104	0.4667
1.971	16.0	16	1.4978	0.4333
1.971	17.0	17	1.5169	0.4
1.971	18.0	18	1.4790	0.4
1.971	19.0	19	1.4223	0.6
1.5094	20.0	20	1.5014	0.4333

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

ricardoSLabs/jaffe_V2_20_1

jaffe_V2_20_1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ricardoSLabs/
jaffe_V2_20_1