Cinnamon-Plant-50-Epochs-Model

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

• Loss: 0.3989
• Accuracy: 0.8958
• F1: 0.8960

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.0428	1.0	18	0.2528	0.9167	0.9167
0.0218	2.0	36	0.3322	0.875	0.8763
0.0149	3.0	54	0.2954	0.875	0.8763
0.0121	4.0	72	0.2941	0.8958	0.8965
0.0106	5.0	90	0.3093	0.875	0.8763
0.0096	6.0	108	0.3130	0.8958	0.8965
0.0088	7.0	126	0.3227	0.875	0.8763
0.0082	8.0	144	0.3197	0.9167	0.9170
0.0077	9.0	162	0.3323	0.8958	0.8965
0.0073	10.0	180	0.3310	0.9167	0.9170
0.0069	11.0	198	0.3378	0.9167	0.9170
0.0066	12.0	216	0.3427	0.8958	0.8965
0.0064	13.0	234	0.3437	0.9167	0.9170
0.0061	14.0	252	0.3483	0.9167	0.9170
0.0059	15.0	270	0.3504	0.9167	0.9170
0.0057	16.0	288	0.3539	0.9167	0.9170
0.0055	17.0	306	0.3597	0.8958	0.8965
0.0054	18.0	324	0.3623	0.8958	0.8965
0.0052	19.0	342	0.3638	0.8958	0.8965
0.0051	20.0	360	0.3670	0.8958	0.8965
0.0049	21.0	378	0.3672	0.9167	0.9170
0.0048	22.0	396	0.3690	0.9167	0.9170
0.0047	23.0	414	0.3704	0.9167	0.9170
0.0046	24.0	432	0.3735	0.9167	0.9170
0.0045	25.0	450	0.3748	0.8958	0.8960
0.0044	26.0	468	0.3775	0.9167	0.9170
0.0044	27.0	486	0.3779	0.8958	0.8960
0.0043	28.0	504	0.3797	0.8958	0.8960
0.0042	29.0	522	0.3818	0.8958	0.8960
0.0041	30.0	540	0.3840	0.8958	0.8960
0.0041	31.0	558	0.3845	0.8958	0.8960
0.004	32.0	576	0.3861	0.8958	0.8960
0.004	33.0	594	0.3877	0.8958	0.8960
0.0039	34.0	612	0.3892	0.8958	0.8960
0.0039	35.0	630	0.3901	0.8958	0.8960
0.0038	36.0	648	0.3912	0.8958	0.8960
0.0038	37.0	666	0.3921	0.8958	0.8960
0.0038	38.0	684	0.3929	0.8958	0.8960
0.0037	39.0	702	0.3935	0.8958	0.8960
0.0037	40.0	720	0.3940	0.8958	0.8960
0.0037	41.0	738	0.3951	0.8958	0.8960
0.0036	42.0	756	0.3958	0.8958	0.8960
0.0036	43.0	774	0.3964	0.8958	0.8960
0.0036	44.0	792	0.3973	0.8958	0.8960
0.0036	45.0	810	0.3978	0.8958	0.8960
0.0036	46.0	828	0.3982	0.8958	0.8960
0.0036	47.0	846	0.3985	0.8958	0.8960
0.0036	48.0	864	0.3987	0.8958	0.8960
0.0035	49.0	882	0.3989	0.8958	0.8960
0.0035	50.0	900	0.3989	0.8958	0.8960

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1