Modelpublic

sudo-s/exper_batch_8_e8

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face

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Model Card

experbatch8_e8

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: 0.4608
—Accuracy: 0.9052

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
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 8
—mixedprecisiontraining: Apex, opt level O1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.2202	0.08	100	4.1245	0.1237
3.467	0.16	200	3.5622	0.2143
3.3469	0.23	300	3.1688	0.2675
2.8086	0.31	400	2.8965	0.3034
2.6291	0.39	500	2.5858	0.4025
2.2382	0.47	600	2.2908	0.4133
1.9259	0.55	700	2.2007	0.4676
1.8088	0.63	800	2.0419	0.4742
1.9462	0.7	900	1.6793	0.5578
1.5392	0.78	1000	1.5460	0.6079
1.561	0.86	1100	1.5793	0.5690
1.2135	0.94	1200	1.4663	0.5929
1.0725	1.02	1300	1.2974	0.6534
0.8696	1.1	1400	1.2406	0.6569
0.8758	1.17	1500	1.2127	0.6623
1.1737	1.25	1600	1.2243	0.6550
0.8242	1.33	1700	1.1371	0.6735
1.0141	1.41	1800	1.0536	0.7024
0.9855	1.49	1900	0.9885	0.7205
0.805	1.57	2000	0.9048	0.7479
0.7207	1.64	2100	0.8842	0.7490
0.7101	1.72	2200	0.8954	0.7436
0.5946	1.8	2300	0.9174	0.7386
0.6937	1.88	2400	0.7818	0.7760
0.5593	1.96	2500	0.7449	0.7934
0.4139	2.04	2600	0.7787	0.7830
0.2929	2.11	2700	0.7122	0.7945
0.4159	2.19	2800	0.7446	0.7907
0.4079	2.27	2900	0.7354	0.7938
0.516	2.35	3000	0.7499	0.8007
0.2728	2.43	3100	0.6851	0.8061
0.4159	2.51	3200	0.7258	0.7999
0.3396	2.58	3300	0.7455	0.7972
0.1918	2.66	3400	0.6793	0.8119
0.1228	2.74	3500	0.6696	0.8134
0.2671	2.82	3600	0.6306	0.8285
0.4986	2.9	3700	0.6111	0.8296
0.3699	2.98	3800	0.5600	0.8508
0.0444	3.05	3900	0.6021	0.8331
0.1489	3.13	4000	0.5599	0.8516
0.15	3.21	4100	0.6377	0.8365
0.2535	3.29	4200	0.5752	0.8543
0.2679	3.37	4300	0.5677	0.8608
0.0989	3.45	4400	0.6325	0.8396
0.0825	3.52	4500	0.5979	0.8524
0.0427	3.6	4600	0.5903	0.8516
0.1806	3.68	4700	0.5323	0.8628
0.2672	3.76	4800	0.5688	0.8604
0.2674	3.84	4900	0.5369	0.8635
0.2185	3.92	5000	0.4743	0.8820
0.2195	3.99	5100	0.5340	0.8709
0.0049	4.07	5200	0.5883	0.8608
0.0204	4.15	5300	0.6102	0.8539
0.0652	4.23	5400	0.5659	0.8670
0.028	4.31	5500	0.4916	0.8840
0.0423	4.39	5600	0.5706	0.8736
0.0087	4.46	5700	0.5653	0.8697
0.0964	4.54	5800	0.5423	0.8755
0.0841	4.62	5900	0.5160	0.8743
0.0945	4.7	6000	0.5532	0.8697
0.0311	4.78	6100	0.4947	0.8867
0.0423	4.86	6200	0.5063	0.8843
0.1348	4.93	6300	0.5619	0.8743
0.049	5.01	6400	0.5800	0.8732
0.0053	5.09	6500	0.5499	0.8770
0.0234	5.17	6600	0.5102	0.8874
0.0192	5.25	6700	0.5447	0.8836
0.0029	5.32	6800	0.4787	0.8936
0.0249	5.4	6900	0.5232	0.8870
0.0671	5.48	7000	0.4766	0.8975
0.0056	5.56	7100	0.5136	0.8894
0.003	5.64	7200	0.5085	0.8882
0.0015	5.72	7300	0.4832	0.8971
0.0014	5.79	7400	0.4648	0.8998
0.0065	5.87	7500	0.4739	0.8978
0.0011	5.95	7600	0.5349	0.8867
0.0021	6.03	7700	0.5460	0.8847
0.0012	6.11	7800	0.5309	0.8890
0.0011	6.19	7900	0.4852	0.8998
0.0093	6.26	8000	0.4751	0.8998
0.003	6.34	8100	0.4934	0.8963
0.0027	6.42	8200	0.4882	0.9029
0.0009	6.5	8300	0.4806	0.9021
0.0009	6.58	8400	0.4974	0.9029
0.0009	6.66	8500	0.4748	0.9075
0.0008	6.73	8600	0.4723	0.9094
0.001	6.81	8700	0.4692	0.9098
0.0007	6.89	8800	0.4726	0.9075
0.0011	6.97	8900	0.4686	0.9067
0.0006	7.05	9000	0.4653	0.9056
0.0006	7.13	9100	0.4755	0.9029
0.0007	7.2	9200	0.4633	0.9036
0.0067	7.28	9300	0.4611	0.9036
0.0007	7.36	9400	0.4608	0.9052
0.0007	7.44	9500	0.4623	0.9044
0.0005	7.52	9600	0.4621	0.9056
0.0005	7.6	9700	0.4615	0.9056
0.0005	7.67	9800	0.4612	0.9059
0.0005	7.75	9900	0.4626	0.9075
0.0004	7.83	10000	0.4626	0.9075
0.0005	7.91	10100	0.4626	0.9075
0.0006	7.99	10200	0.4626	0.9079

Framework versions

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