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

Loss: 0.0001
Accuracy: 1.0

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6146	0.16	100	0.0402	1.0
0.6062	0.31	200	0.2393	1.0
0.4847	0.47	300	0.2164	1.0
0.5282	0.63	400	0.0427	1.0
0.4153	0.79	500	1.0996	0.0
0.3295	0.94	600	0.0499	1.0
0.3541	1.1	700	0.0009	1.0
0.4617	1.26	800	0.0106	1.0
0.3014	1.42	900	0.0045	1.0
0.3558	1.57	1000	0.0038	1.0
0.2357	1.73	1100	0.0140	1.0
0.3055	1.89	1200	0.0809	1.0
0.3278	2.04	1300	0.0077	1.0
0.3075	2.2	1400	0.0059	1.0
0.3462	2.36	1500	0.0377	1.0
0.2968	2.52	1600	0.0082	1.0
0.3392	2.67	1700	0.8628	0.0
0.2155	2.83	1800	0.0022	1.0
0.3521	2.99	1900	0.0671	1.0
0.3968	3.14	2000	0.0014	1.0
0.32	3.3	2100	0.0075	1.0
0.1787	3.46	2200	0.0015	1.0
0.2598	3.62	2300	0.0086	1.0
0.3424	3.77	2400	0.0008	1.0
0.2371	3.93	2500	0.0054	1.0
0.2773	4.09	2600	0.0028	1.0
0.3192	4.25	2700	0.0088	1.0
0.2173	4.4	2800	0.1174	1.0
0.2181	4.56	2900	0.0056	1.0
0.2476	4.72	3000	0.0006	1.0
0.2417	4.87	3100	0.0005	1.0
0.1915	5.03	3200	0.0002	1.0
0.149	5.19	3300	0.0004	1.0
0.1618	5.35	3400	0.1542	1.0
0.1752	5.5	3500	0.0001	1.0
0.1094	5.66	3600	0.0045	1.0
0.2532	5.82	3700	0.0016	1.0
0.1606	5.97	3800	0.0004	1.0
0.1781	6.13	3900	0.0007	1.0
0.1459	6.29	4000	0.0003	1.0
0.2357	6.45	4100	2.6113	0.0
0.2524	6.6	4200	0.0003	1.0
0.1708	6.76	4300	0.0006	1.0
0.1875	6.92	4400	0.0011	1.0
0.1462	7.08	4500	0.0004	1.0
0.1534	7.23	4600	0.0002	1.0
0.2834	7.39	4700	0.0003	1.0
0.2264	7.55	4800	0.0001	1.0
0.1007	7.7	4900	0.0001	1.0
0.2376	7.86	5000	0.0006	1.0
0.2233	8.02	5100	0.0002	1.0
0.1804	8.18	5200	0.0034	1.0
0.185	8.33	5300	0.0002	1.0
0.1149	8.49	5400	0.0007	1.0
0.2048	8.65	5500	0.0009	1.0
0.0786	8.81	5600	0.9478	0.0
0.2222	8.96	5700	0.0007	1.0
0.1289	9.12	5800	0.0009	1.0
0.2248	9.28	5900	0.0005	1.0
0.0987	9.43	6000	0.0002	1.0
0.2897	9.59	6100	0.0002	1.0
0.2023	9.75	6200	0.0042	1.0
0.1481	9.91	6300	0.0003	1.0
0.1224	10.06	6400	0.0009	1.0
0.1353	10.22	6500	0.0080	1.0
0.0659	10.38	6600	0.0006	1.0
0.1692	10.53	6700	0.0005	1.0
0.1713	10.69	6800	0.0006	1.0
0.1131	10.85	6900	0.0012	1.0
0.2325	11.01	7000	0.0003	1.0
0.0817	11.16	7100	0.0003	1.0
0.1854	11.32	7200	0.0001	1.0
0.0956	11.48	7300	0.0002	1.0
0.0758	11.64	7400	0.0127	1.0
0.0928	11.79	7500	0.0002	1.0
0.1563	11.95	7600	0.0004	1.0
0.0596	12.11	7700	0.0003	1.0
0.1266	12.26	7800	0.0031	1.0
0.1788	12.42	7900	0.0002	1.0
0.1663	12.58	8000	0.0071	1.0
0.064	12.74	8100	0.0003	1.0
0.1459	12.89	8200	0.0005	1.0
0.1237	13.05	8300	0.0001	1.0
0.1334	13.21	8400	0.0001	1.0
0.0802	13.36	8500	0.0001	1.0
0.1418	13.52	8600	0.0000	1.0
0.048	13.68	8700	0.0001	1.0
0.1267	13.84	8800	0.0121	1.0
0.1298	13.99	8900	0.0001	1.0
0.16	14.15	9000	0.0001	1.0
0.1295	14.31	9100	0.0001	1.0
0.1714	14.47	9200	0.0001	1.0
0.1377	14.62	9300	0.0001	1.0
0.1336	14.78	9400	0.0001	1.0
0.1293	14.94	9500	0.0001	1.0
0.111	15.09	9600	0.0001	1.0
0.0818	15.25	9700	0.0000	1.0
0.1884	15.41	9800	0.0001	1.0
0.1004	15.57	9900	0.0002	1.0
0.1029	15.72	10000	0.0000	1.0
0.0772	15.88	10100	0.0000	1.0
0.1573	16.04	10200	0.0001	1.0
0.0748	16.19	10300	0.0001	1.0
0.088	16.35	10400	0.0001	1.0
0.1062	16.51	10500	0.0001	1.0
0.0237	16.67	10600	0.0001	1.0
0.0729	16.82	10700	0.0000	1.0
0.1028	16.98	10800	0.0001	1.0
0.0423	17.14	10900	0.0000	1.0
0.0922	17.3	11000	0.0002	1.0
0.0788	17.45	11100	0.0001	1.0
0.0357	17.61	11200	0.0001	1.0
0.0519	17.77	11300	0.0000	1.0
0.108	17.92	11400	0.0001	1.0
0.1746	18.08	11500	0.1221	1.0
0.1	18.24	11600	0.0006	1.0
0.0798	18.4	11700	0.0001	1.0
0.0118	18.55	11800	0.0001	1.0
0.1151	18.71	11900	0.0001	1.0
0.0617	18.87	12000	0.0001	1.0
0.1577	19.03	12100	0.0001	1.0
0.1928	19.18	12200	0.0001	1.0
0.0462	19.34	12300	0.0001	1.0
0.0461	19.5	12400	0.3145	1.0
0.0454	19.65	12500	0.0001	1.0
0.0637	19.81	12600	0.0001	1.0
0.0733	19.97	12700	0.0001	1.0

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

carolinetfls/plant-seedlings-vit-freeze-0-6-aug-3-whole-data-train

plant-seedlings-freeze-0-6-aug-3-whole-data-train

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

carolinetfls/
plant-seedlings-vit-freeze-0-6-aug-3-whole-data-train