learning_rate: 0.00025
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6069	0.6410	100	0.5681	0.7146	0.5533	0.4191	0.8137
0.4385	1.2821	200	0.4052	0.8384	0.6334	0.6241	0.6430
0.3415	1.9231	300	0.2995	0.8891	0.7233	0.7893	0.6674
0.3761	2.5641	400	0.2871	0.8809	0.6934	0.7863	0.6201
0.3066	3.2051	500	0.2877	0.8841	0.7072	0.7835	0.6445
0.3236	3.8462	600	0.2608	0.8937	0.7398	0.7901	0.6955
0.336	4.4872	700	0.2619	0.8926	0.7301	0.8037	0.6689
0.3003	5.1282	800	0.2736	0.8865	0.7160	0.7843	0.6585
0.2756	5.7692	900	0.2584	0.8945	0.7443	0.7862	0.7066
0.2566	6.4103	1000	0.2574	0.8928	0.7319	0.8007	0.6741
0.2609	7.0513	1100	0.2506	0.8966	0.75	0.7899	0.7140
0.2721	7.6923	1200	0.2282	0.9024	0.7599	0.8159	0.7110
0.2317	8.3333	1300	0.2425	0.9029	0.7613	0.8164	0.7132
0.2953	8.9744	1400	0.2284	0.9077	0.7758	0.8210	0.7354
0.2485	9.6154	1500	0.2320	0.9042	0.7669	0.8129	0.7258
0.2387	10.2564	1600	0.2352	0.9034	0.7672	0.8045	0.7332
0.2288	10.8974	1700	0.2178	0.9087	0.7816	0.8131	0.7524
0.1979	11.5385	1800	0.2283	0.9100	0.7881	0.8060	0.7709
0.194	12.1795	1900	0.2298	0.9024	0.7704	0.7876	0.7539
0.2011	12.8205	2000	0.2204	0.9104	0.7882	0.8103	0.7672
0.2033	13.4615	2100	0.2149	0.9133	0.7951	0.8168	0.7746
0.1795	14.1026	2200	0.2278	0.9069	0.7815	0.7971	0.7664
0.2153	14.7436	2300	0.2177	0.9100	0.7853	0.8143	0.7583
0.1814	15.3846	2400	0.2169	0.9144	0.7991	0.8154	0.7834
0.1605	16.0256	2500	0.2127	0.9141	0.8	0.8094	0.7908
0.172	16.6667	2600	0.2147	0.9116	0.7942	0.8029	0.7857
0.1622	17.3077	2700	0.2259	0.9071	0.7837	0.7923	0.7753
0.1676	17.9487	2800	0.2165	0.9117	0.7915	0.8125	0.7716
0.1581	18.5897	2900	0.2204	0.9109	0.7919	0.8037	0.7805
0.1725	19.2308	3000	0.2196	0.9108	0.7919	0.8021	0.7820
0.1306	19.8718	3100	0.2161	0.9125	0.7936	0.8137	0.7746
0.1304	20.5128	3200	0.2252	0.9061	0.7813	0.7905	0.7724
0.1248	21.1538	3300	0.2302	0.9112	0.7928	0.8040	0.7820
0.1214	21.7949	3400	0.2315	0.9085	0.7856	0.8	0.7716
0.0979	22.4359	3500	0.2298	0.9109	0.7911	0.8060	0.7768
0.1157	23.0769	3600	0.2284	0.9128	0.7964	0.8082	0.7849
0.1279	23.7179	3700	0.2327	0.9125	0.7933	0.8146	0.7731
0.1032	24.3590	3800	0.2316	0.9120	0.7932	0.8103	0.7768
0.0958	25.0	3900	0.2244	0.9156	0.8023	0.8164	0.7886
0.1156	25.6410	4000	0.2356	0.9127	0.7938	0.8148	0.7738
0.106	26.2821	4100	0.2334	0.9100	0.7912	0.7969	0.7857
0.0966	26.9231	4200	0.2334	0.9132	0.7975	0.8080	0.7871
0.0746	27.5641	4300	0.2340	0.9117	0.7939	0.8053	0.7827
0.0905	28.2051	4400	0.2323	0.9130	0.7973	0.8070	0.7879
0.0899	28.8462	4500	0.2340	0.9138	0.7987	0.8105	0.7871
0.0804	29.4872	4600	0.2318	0.9143	0.8	0.8109	0.7894

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.1
Tokenizers 0.21.0

liamxostrander/vit-base-patch16-224-in21k-v2025-2-20

vit-base-patch16-224-in21k-v2025-2-20

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

liamxostrander/
vit-base-patch16-224-in21k-v2025-2-20