vit-base-patch16-224

This model is a fine-tuned version of motheecreator/vit-Facial-Expression-Recognition on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.3337
• Accuracy: 0.8884

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6159	0.1393	100	0.3897	0.8657
0.6396	0.2786	200	0.3721	0.8774
0.6264	0.4178	300	0.3983	0.8621
0.6294	0.5571	400	0.3873	0.8708
0.6526	0.6964	500	0.3771	0.8716
0.6253	0.8357	600	0.3828	0.8682
0.6808	0.9749	700	0.3555	0.8798
0.4981	1.1142	800	0.3877	0.8640
0.5193	1.2535	900	0.3770	0.8730
0.5093	1.3928	1000	0.3648	0.8788
0.4901	1.5320	1100	0.3370	0.8851
0.5428	1.6713	1200	0.3456	0.8823
0.4994	1.8106	1300	0.3449	0.8826
0.4499	1.9499	1400	0.3400	0.8849
0.4512	2.0891	1500	0.3337	0.8884
0.3978	2.2284	1600	0.3237	0.8901
0.4247	2.3677	1700	0.3226	0.8924
0.4017	2.5070	1800	0.3187	0.8950
0.4164	2.6462	1900	0.3149	0.8948
0.3754	2.7855	2000	0.3142	0.8910
0.3889	2.9248	2100	0.3119	0.8945

Framework versions

• Transformers 4.56.0
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0