exper_batch_8_e4

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3353
• Accuracy: 0.9183

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4
• mixed_precision_training: Apex, opt level O1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.2251	0.08	100	4.1508	0.1203
3.4942	0.16	200	3.5566	0.2082
3.2871	0.23	300	3.0942	0.3092
2.7273	0.31	400	2.8338	0.3308
2.4984	0.39	500	2.4860	0.4341
2.3423	0.47	600	2.2201	0.4796
1.8785	0.55	700	2.1890	0.4653
1.8012	0.63	800	1.9901	0.4865
1.7236	0.7	900	1.6821	0.5736
1.4949	0.78	1000	1.5422	0.6083
1.5573	0.86	1100	1.5436	0.6110
1.3241	0.94	1200	1.4077	0.6207
1.0773	1.02	1300	1.1417	0.6916
0.7935	1.1	1400	1.1194	0.6931
0.7677	1.17	1500	1.0727	0.7167
0.9468	1.25	1600	1.0707	0.7136
0.7563	1.33	1700	0.9427	0.7390
0.8471	1.41	1800	0.8906	0.7571
0.9998	1.49	1900	0.8098	0.7845
0.6039	1.57	2000	0.7244	0.8034
0.7052	1.64	2100	0.7881	0.7953
0.6753	1.72	2200	0.7458	0.7926
0.3758	1.8	2300	0.6987	0.8022
0.4985	1.88	2400	0.6286	0.8265
0.4122	1.96	2500	0.5949	0.8358
0.1286	2.04	2600	0.5691	0.8385
0.1989	2.11	2700	0.5535	0.8389
0.3304	2.19	2800	0.5261	0.8520
0.3415	2.27	2900	0.5504	0.8477
0.4066	2.35	3000	0.5418	0.8497
0.1208	2.43	3100	0.5156	0.8612
0.1668	2.51	3200	0.5655	0.8539
0.0727	2.58	3300	0.4971	0.8658
0.0929	2.66	3400	0.4962	0.8635
0.0678	2.74	3500	0.4903	0.8670
0.1212	2.82	3600	0.4357	0.8867
0.1579	2.9	3700	0.4642	0.8739
0.2625	2.98	3800	0.3994	0.8951
0.024	3.05	3900	0.3953	0.8971
0.0696	3.13	4000	0.3883	0.9056
0.0169	3.21	4100	0.3755	0.9086
0.023	3.29	4200	0.3685	0.9109
0.0337	3.37	4300	0.3623	0.9109
0.0123	3.45	4400	0.3647	0.9067
0.0159	3.52	4500	0.3630	0.9082
0.0154	3.6	4600	0.3522	0.9094
0.0112	3.68	4700	0.3439	0.9163
0.0219	3.76	4800	0.3404	0.9194
0.0183	3.84	4900	0.3371	0.9183
0.0103	3.92	5000	0.3362	0.9183
0.0357	3.99	5100	0.3353	0.9183

Framework versions

• Transformers 4.19.4
• Pytorch 1.5.1
• Datasets 2.3.2
• Tokenizers 0.12.1