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: 0.7152
Accuracy: 0.552

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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	Validation Loss	Accuracy
0.6546	0.99	66	0.7185	0.52
0.6222	2.0	133	0.7152	0.552
0.5986	2.99	199	0.7344	0.542
0.5535	4.0	266	0.7782	0.514
0.5377	4.99	332	0.8329	0.514
0.5115	6.0	399	0.7596	0.528
0.5133	6.99	465	0.8151	0.512
0.511	8.0	532	0.7897	0.538
0.4712	8.99	598	0.8539	0.514
0.4626	9.92	660	0.8449	0.522

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

tommilyjones/vit-base-patch16-224-finetuned-hateful-meme-restructured

vit-base-patch16-224-finetuned-hateful-meme-restructured

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tommilyjones/
vit-base-patch16-224-finetuned-hateful-meme-restructured