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: 1.0591
Accuracy: 0.9

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: 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	1.7966	0.1
No log	2.0	4	1.7835	0.2
No log	3.0	6	1.7547	0.2667
No log	4.0	8	1.7069	0.3667
1.7198	5.0	10	1.6416	0.3667
1.7198	6.0	12	1.5306	0.4
1.7198	7.0	14	1.4958	0.5333
1.7198	8.0	16	1.4440	0.5333
1.7198	9.0	18	1.3930	0.6
1.3635	10.0	20	1.2984	0.7333
1.3635	11.0	22	1.3484	0.7333
1.3635	12.0	24	1.2727	0.8333
1.3635	13.0	26	1.1674	0.8333
1.3635	14.0	28	1.1443	0.8667
1.0916	15.0	30	1.1607	0.9
1.0916	16.0	32	1.1076	0.8667
1.0916	17.0	34	1.0670	0.9667
1.0916	18.0	36	1.0694	0.9333
1.0916	19.0	38	1.0874	0.9
0.9397	20.0	40	1.0591	0.9

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Netnoy17/Chess_images_classifier

Chess_images_classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Netnoy17/
Chess_images_classifier