vit-base-DogSick

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:

• Loss: 1.3041
• Acc: {'accuracy': 0.6102564102564103}
• F1: {'f1': 0.5980148081337936}

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: 4e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	F1
2.4055	0.61	50	2.2086	{'accuracy': 0.41794871794871796}	{'f1': 0.3246788612052483}
2.0379	1.22	100	1.9233	{'accuracy': 0.4846153846153846}	{'f1': 0.4386383497855148}
1.7287	1.83	150	1.7200	{'accuracy': 0.5256410256410257}	{'f1': 0.4806042289317683}
1.4667	2.44	200	1.6021	{'accuracy': 0.5692307692307692}	{'f1': 0.533374137436958}
1.3444	3.05	250	1.5410	{'accuracy': 0.5333333333333333}	{'f1': 0.4846134797922835}
1.1334	3.66	300	1.4674	{'accuracy': 0.5743589743589743}	{'f1': 0.5533432367508125}
1.007	4.27	350	1.4096	{'accuracy': 0.5923076923076923}	{'f1': 0.5801847507206119}
0.897	4.88	400	1.3674	{'accuracy': 0.6}	{'f1': 0.5903283954748092}
0.7326	5.49	450	1.3359	{'accuracy': 0.5923076923076923}	{'f1': 0.5793036546532927}
0.7105	6.1	500	1.3259	{'accuracy': 0.6153846153846154}	{'f1': 0.6064330281486513}
0.6164	6.71	550	1.3183	{'accuracy': 0.6102564102564103}	{'f1': 0.6014695572651212}
0.5804	7.32	600	1.3103	{'accuracy': 0.6025641025641025}	{'f1': 0.5965366941171513}
0.5313	7.93	650	1.3041	{'accuracy': 0.6102564102564103}	{'f1': 0.5980148081337936}

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1