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: 0.0636
Accuracy: 0.9870
Precision: 0.9875
Recall: 0.9870
F1: 0.9870
Confusion Matrix: [[62, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 410, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 356, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 123, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 13, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 11, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]
Cohen Kappa: 0.9830
Matthews Corrcoef: 0.9831

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: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Confusion Matrix	Cohen Kappa	Matthews Corrcoef
0.2225	0.6211	100	0.2418	0.9510	0.9485	0.9510	0.9472	[[59, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 276, 3, 0, 0, 0, 0, 0, 0, 0], [1, 0, 399, 8, 2, 0, 0, 0, 0, 0], [0, 0, 8, 345, 6, 0, 0, 0, 0, 0], [0, 0, 0, 0, 124, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 6, 0, 3], [0, 0, 0, 0, 0, 0, 13, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 3, 0, 11], [2, 0, 0, 0, 0, 0, 0, 2, 3, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 20]]	0.9362	0.9364
0.106	1.2422	200	0.1557	0.9694	0.9684	0.9694	0.9675	[[60, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 406, 2, 0, 0, 0, 0, 0, 0], [0, 0, 7, 351, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 122, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 6, 1, 2], [0, 0, 0, 0, 0, 0, 14, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 8, 2, 4], [1, 0, 0, 0, 0, 1, 0, 1, 6, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9601	0.9602
0.1198	1.8634	300	0.1496	0.9640	0.9679	0.9640	0.9624	[[60, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 266, 13, 0, 0, 0, 0, 0, 0, 0], [0, 0, 406, 4, 0, 0, 0, 0, 0, 0], [0, 0, 7, 352, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 124, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 8, 0, 1], [0, 0, 0, 0, 0, 0, 14, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 9, 0, 4], [0, 0, 0, 0, 0, 0, 0, 1, 8, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9530	0.9533
0.0901	2.4845	400	0.1208	0.9686	0.9709	0.9686	0.9665	[[63, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [8, 1, 401, 0, 0, 0, 0, 0, 0, 0], [0, 0, 11, 348, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 121, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 6, 0, 2], [0, 0, 0, 0, 0, 0, 14, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 9, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 9, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 20]]	0.9591	0.9592
0.0133	3.1056	500	0.0862	0.9801	0.9820	0.9801	0.9797	[[61, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 408, 1, 0, 0, 0, 0, 0, 0], [0, 0, 4, 355, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 121, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 14, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 11, 0, 2], [0, 0, 0, 0, 0, 0, 0, 1, 8, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9740	0.9741
0.0078	3.7267	600	0.0498	0.9908	0.9915	0.9908	0.9906	[[63, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 410, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 356, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 124, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 14, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 13, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 9, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 20]]	0.9880	0.9881
0.0045	4.3478	700	0.0532	0.9877	0.9879	0.9877	0.9877	[[62, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 409, 1, 0, 0, 0, 0, 0, 0], [0, 0, 2, 357, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 122, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 14, 0, 0, 1], [0, 0, 0, 0, 0, 1, 1, 11, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9840	0.9840
0.0039	4.9689	800	0.0547	0.9877	0.9885	0.9877	0.9878	[[63, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 410, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 356, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 123, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 13, 0, 0, 2], [0, 0, 0, 0, 0, 1, 0, 11, 0, 2], [0, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9840	0.9841
0.0032	5.5901	900	0.0767	0.9862	0.9868	0.9862	0.9862	[[61, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 408, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 357, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 123, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 13, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 12, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9820	0.9821
0.0021	6.2112	1000	0.0678	0.9862	0.9867	0.9862	0.9862	[[62, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 409, 1, 0, 0, 0, 0, 0, 0], [0, 0, 3, 356, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 123, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 13, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 11, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9820	0.9821
0.0017	6.8323	1100	0.0617	0.9877	0.9882	0.9877	0.9877	[[62, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 410, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 357, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 123, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 13, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 11, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9840	0.9841
0.0012	7.4534	1200	0.0636	0.9870	0.9875	0.9870	0.9870	[[62, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 279, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 410, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 356, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 123, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 13, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 11, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 19]]	0.9830	0.9831

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

darthraider/vit-4-veggies-2

vit-4-veggies-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

darthraider/
vit-4-veggies-2