vit-base-patch16-224-FV2-finetuned-memes

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

• Loss: 0.5458
• Accuracy: 0.8648
• Precision: 0.8651
• Recall: 0.8648
• F1: 0.8646

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: 0.00012
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.994	0.99	20	0.7937	0.7257	0.7148	0.7257	0.7025
0.509	1.99	40	0.4634	0.8346	0.8461	0.8346	0.8303
0.2698	2.99	60	0.3851	0.8594	0.8619	0.8594	0.8586
0.1381	3.99	80	0.4186	0.8624	0.8716	0.8624	0.8634
0.0899	4.99	100	0.4038	0.8586	0.8624	0.8586	0.8594
0.0708	5.99	120	0.4170	0.8563	0.8612	0.8563	0.8580
0.0629	6.99	140	0.4414	0.8594	0.8599	0.8594	0.8585
0.0554	7.99	160	0.4617	0.8539	0.8563	0.8539	0.8550
0.0582	8.99	180	0.4712	0.8648	0.8667	0.8648	0.8651
0.0582	9.99	200	0.4753	0.8632	0.8647	0.8632	0.8636
0.0535	10.99	220	0.4653	0.8694	0.8690	0.8694	0.8684
0.0516	11.99	240	0.4937	0.8679	0.8692	0.8679	0.8681
0.0478	12.99	260	0.5109	0.8725	0.8741	0.8725	0.8718
0.0484	13.99	280	0.5144	0.8640	0.8660	0.8640	0.8647
0.0472	14.99	300	0.5249	0.8679	0.8688	0.8679	0.8678
0.043	15.99	320	0.5324	0.8709	0.8711	0.8709	0.8704
0.0473	16.99	340	0.5352	0.8648	0.8660	0.8648	0.8647
0.0502	17.99	360	0.5389	0.8694	0.8692	0.8694	0.8687
0.0489	18.99	380	0.5564	0.8648	0.8666	0.8648	0.8651
0.04	19.99	400	0.5458	0.8648	0.8651	0.8648	0.8646

Framework versions

• Transformers 4.24.0.dev0
• Pytorch 1.11.0+cu102
• Datasets 2.6.1.dev0
• Tokenizers 0.13.1