Rice-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: 0.1649
• Accuracy: 0.9688
• F1: 0.9686

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
1.0399	1.0	115	0.6185	0.8910	0.8933
0.3392	2.0	230	0.2849	0.9502	0.9497
0.1633	3.0	345	0.2230	0.9439	0.9440
0.104	4.0	460	0.2022	0.9502	0.9495
0.0828	5.0	575	0.2081	0.9408	0.9406
0.0603	6.0	690	0.2301	0.9408	0.9403
0.0513	7.0	805	0.1704	0.9595	0.9593
0.042	8.0	920	0.1587	0.9626	0.9626
0.0356	9.0	1035	0.1606	0.9626	0.9625
0.0299	10.0	1150	0.1608	0.9657	0.9656
0.0262	11.0	1265	0.1553	0.9626	0.9625
0.0232	12.0	1380	0.1582	0.9657	0.9656
0.0207	13.0	1495	0.1588	0.9657	0.9656
0.0186	14.0	1610	0.1618	0.9657	0.9656
0.0168	15.0	1725	0.1618	0.9657	0.9656
0.0152	16.0	1840	0.1639	0.9657	0.9656
0.0139	17.0	1955	0.1649	0.9688	0.9686
0.0127	18.0	2070	0.1676	0.9657	0.9656
0.0117	19.0	2185	0.1688	0.9688	0.9686
0.0108	20.0	2300	0.1710	0.9626	0.9622
0.01	21.0	2415	0.1723	0.9657	0.9654
0.0093	22.0	2530	0.1739	0.9657	0.9654
0.0087	23.0	2645	0.1758	0.9626	0.9622
0.0081	24.0	2760	0.1776	0.9626	0.9622
0.0076	25.0	2875	0.1777	0.9657	0.9654
0.0071	26.0	2990	0.1792	0.9657	0.9654
0.0067	27.0	3105	0.1808	0.9657	0.9654
0.0063	28.0	3220	0.1822	0.9657	0.9654
0.006	29.0	3335	0.1834	0.9657	0.9654
0.0057	30.0	3450	0.1840	0.9657	0.9654
0.0054	31.0	3565	0.1855	0.9657	0.9654
0.0051	32.0	3680	0.1868	0.9657	0.9654
0.0049	33.0	3795	0.1877	0.9657	0.9654
0.0047	34.0	3910	0.1892	0.9657	0.9654
0.0045	35.0	4025	0.1900	0.9657	0.9654
0.0043	36.0	4140	0.1914	0.9657	0.9654
0.0042	37.0	4255	0.1919	0.9657	0.9654
0.004	38.0	4370	0.1929	0.9657	0.9654
0.0039	39.0	4485	0.1938	0.9657	0.9654
0.0037	40.0	4600	0.1953	0.9657	0.9654
0.0036	41.0	4715	0.1956	0.9657	0.9654
0.0035	42.0	4830	0.1965	0.9657	0.9654
0.0035	43.0	4945	0.1974	0.9657	0.9654
0.0034	44.0	5060	0.1981	0.9657	0.9654
0.0033	45.0	5175	0.1984	0.9657	0.9654
0.0032	46.0	5290	0.1986	0.9657	0.9654
0.0032	47.0	5405	0.1989	0.9657	0.9654
0.0032	48.0	5520	0.1993	0.9657	0.9654
0.0031	49.0	5635	0.1993	0.9657	0.9654
0.0031	50.0	5750	0.1993	0.9657	0.9654

Framework versions

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