vit-base-tour-augmentation-v5

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

• Loss: 2.4590
• Acc: {'accuracy': 0.42896389324960754}
• F1: {'f1': 0.4271599947085357}

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: 32
• 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
1.634	0.77	1000	2.4590	{'accuracy': 0.42896389324960754}	{'f1': 0.4271599947085357}
0.8261	1.53	2000	2.4812	{'accuracy': 0.4703689167974882}	{'f1': 0.4487847422578378}
0.3823	2.3	3000	2.6315	{'accuracy': 0.46683673469387754}	{'f1': 0.45668802662482005}
0.1652	3.07	4000	2.8592	{'accuracy': 0.46703296703296704}	{'f1': 0.4525782242029193}
0.0713	3.83	5000	3.0906	{'accuracy': 0.4430926216640502}	{'f1': 0.45342569349779865}
0.0354	4.6	6000	3.2511	{'accuracy': 0.45506279434850866}	{'f1': 0.44957410984221347}
0.0214	5.36	7000	3.3369	{'accuracy': 0.47370486656200944}	{'f1': 0.4603765751991713}
0.0129	6.13	8000	3.4611	{'accuracy': 0.4619309262166405}	{'f1': 0.4624748087985776}
0.0079	6.9	9000	3.5376	{'accuracy': 0.46251962323390894}	{'f1': 0.4584329789658534}
0.0058	7.66	10000	3.5842	{'accuracy': 0.4705651491365777}	{'f1': 0.46144792853832145}

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1+cu113
• Datasets 2.5.2
• Tokenizers 0.12.1