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

Loss: 0.2058
Accuracy: 0.9178

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7057	0.04	10	0.7027	0.4644
0.6808	0.09	20	0.6615	0.6590
0.6278	0.13	30	0.5969	0.7441
0.5674	0.17	40	0.5134	0.8183
0.4761	0.21	50	0.4146	0.875
0.3777	0.26	60	0.3362	0.8796
0.303	0.3	70	0.2906	0.8854
0.2385	0.34	80	0.2694	0.8937
0.2452	0.39	90	0.2515	0.9012
0.2771	0.43	100	0.2441	0.9050
0.2332	0.47	110	0.2510	0.8975
0.2495	0.51	120	0.2398	0.9052
0.2611	0.56	130	0.2384	0.9063
0.2292	0.6	140	0.2931	0.8865
0.2518	0.64	150	0.2537	0.8994
0.211	0.69	160	0.2619	0.8953
0.2514	0.73	170	0.2236	0.9090
0.2272	0.77	180	0.2254	0.9085
0.2263	0.81	190	0.2141	0.9181
0.2524	0.86	200	0.2038	0.9194
0.2024	0.9	210	0.2038	0.9165
0.2355	0.94	220	0.2215	0.9103
0.2431	0.99	230	0.2116	0.9178
0.1921	1.03	240	0.2105	0.9111
0.1845	1.07	250	0.2107	0.9117
0.1838	1.11	260	0.2070	0.9119
0.1824	1.16	270	0.2110	0.9130
0.1706	1.2	280	0.2177	0.9154
0.1826	1.24	290	0.2058	0.9160
0.1816	1.28	300	0.2081	0.9176
0.1901	1.33	310	0.2187	0.9149
0.2112	1.37	320	0.2107	0.9181
0.22	1.41	330	0.2065	0.9173
0.2105	1.46	340	0.2090	0.9170
0.2016	1.5	350	0.2044	0.9141
0.2055	1.54	360	0.2029	0.9173
0.1507	1.58	370	0.2103	0.9192
0.1705	1.63	380	0.1960	0.9184
0.1605	1.67	390	0.2070	0.9154
0.2011	1.71	400	0.2096	0.9160
0.1832	1.76	410	0.2023	0.9176
0.1756	1.8	420	0.2005	0.9189
0.1874	1.84	430	0.2050	0.9135
0.1497	1.88	440	0.1936	0.9240
0.1891	1.93	450	0.1991	0.9208
0.1595	1.97	460	0.2014	0.9194
0.2028	2.01	470	0.1994	0.9184
0.1794	2.06	480	0.2068	0.9146
0.1404	2.1	490	0.2046	0.9181
0.1615	2.14	500	0.1955	0.9243
0.1555	2.18	510	0.2027	0.9202
0.151	2.23	520	0.1893	0.9261
0.1676	2.27	530	0.2046	0.9192
0.1744	2.31	540	0.1967	0.9218
0.1644	2.36	550	0.1970	0.9226
0.2048	2.4	560	0.1930	0.9243
0.1649	2.44	570	0.1986	0.9218
0.1435	2.48	580	0.1956	0.9213
0.1598	2.53	590	0.1986	0.9197
0.1513	2.57	600	0.2020	0.9173
0.1769	2.61	610	0.2005	0.9170
0.1488	2.66	620	0.2033	0.9197
0.1636	2.7	630	0.1964	0.9216
0.1583	2.74	640	0.1985	0.9189
0.1294	2.78	650	0.2109	0.9151
0.1585	2.83	660	0.2000	0.9186
0.1531	2.87	670	0.2078	0.9178
0.1294	2.91	680	0.1891	0.9272
0.1612	2.96	690	0.2058	0.9178

Framework versions

Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

lngrid2020/hf_images_model1

hf_images_model1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lngrid2020/
hf_images_model1