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: 0.7292
Accuracy: 0.6538

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	3	1.0620	0.5385
No log	1.85	6	0.9886	0.5962
No log	2.77	9	0.9286	0.7115
0.9947	4.0	13	0.8659	0.6731
0.9947	4.92	16	0.8310	0.6731
0.9947	5.85	19	0.7778	0.6731
0.7638	6.77	22	0.7388	0.7115
0.7638	8.0	26	0.7570	0.6731
0.7638	8.92	29	0.7214	0.6923
0.6277	9.23	30	0.7292	0.6538

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Tapashh/Chess

Chess

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Tapashh/
Chess