vit-snacks

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

Loss: 0.2754
Accuracy: 0.9393

Model description

upload any image of your fave yummy snack

Intended uses & limitations

there are only 20 different varieties of snacks

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8724	0.33	100	0.9118	0.8670
0.5628	0.66	200	0.6873	0.8471
0.4421	0.99	300	0.4995	0.8691
0.2837	1.32	400	0.4008	0.9026
0.1645	1.65	500	0.3702	0.9058
0.1604	1.98	600	0.3981	0.8921
0.0498	2.31	700	0.3185	0.9204
0.0406	2.64	800	0.3427	0.9141
0.1049	2.97	900	0.3444	0.9173
0.0272	3.3	1000	0.3168	0.9246
0.0186	3.63	1100	0.3142	0.9288
0.0203	3.96	1200	0.2931	0.9298
0.007	4.29	1300	0.2754	0.9393
0.0072	4.62	1400	0.2778	0.9403
0.0073	4.95	1500	0.2782	0.9393

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1

Shivagowri
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vit-snacks