ai_art_exp1_vit_final

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

• Accuracy: {'accuracy': 0.9946666666666667}
• Overall Accuracy: 0.9947
• Loss: 0.0231
• Human Accuracy: 0.99
• Ld Accuracy: 0.998
• Sd Accuracy: 0.996

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	Accuracy	Overall Accuracy	Validation Loss	Human Accuracy	Ld Accuracy	Sd Accuracy
0.198	0.992	93	{'accuracy': 0.9506666666666667}	0.9507	0.1906	0.8548	0.9981	0.9959
0.0647	1.9947	187	{'accuracy': 0.9793333333333333}	0.9793	0.0811	0.9489	0.9923	0.9959
0.0395	2.9973	281	{'accuracy': 0.988}	0.988	0.0567	0.9734	0.9904	1.0
0.069	4.0	375	{'accuracy': 0.9933333333333333}	0.9933	0.0399	0.9816	1.0	0.9980
0.0456	4.992	468	{'accuracy': 0.9946666666666667}	0.9947	0.0309	0.9877	1.0	0.9959
0.0324	5.9947	562	{'accuracy': 0.9906666666666667}	0.9907	0.0444	0.9734	1.0	0.9980
0.0136	6.9973	656	{'accuracy': 0.996}	0.996	0.0234	0.9939	1.0	0.9939
0.0137	8.0	750	{'accuracy': 0.9953333333333333}	0.9953	0.0218	0.9898	0.9962	1.0
0.0105	8.992	843	{'accuracy': 0.9953333333333333}	0.9953	0.0222	0.9877	1.0	0.9980
0.0111	9.92	930	{'accuracy': 0.9986666666666667}	0.9987	0.0122	0.9980	0.9981	1.0

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1