learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
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

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	185	3.0723	0.3166	0.2902	0.3034	0.3166
No log	2.0	370	1.1438	0.8850	0.8847	0.8924	0.8850
2.2334	3.0	555	0.3604	0.9229	0.9230	0.9266	0.9229
2.2334	4.0	740	0.2413	0.9350	0.9349	0.9383	0.9350
2.2334	5.0	925	0.2297	0.9323	0.9315	0.9349	0.9323
0.1102	6.0	1110	0.2163	0.9391	0.9390	0.9417	0.9391
0.1102	7.0	1295	0.2179	0.9364	0.9359	0.9395	0.9364
0.1102	8.0	1480	0.2164	0.9378	0.9373	0.9404	0.9378
0.0078	9.0	1665	0.2169	0.9364	0.9359	0.9392	0.9364
0.0078	10.0	1850	0.2214	0.9350	0.9345	0.9382	0.9350
0.003	11.0	2035	0.2225	0.9364	0.9359	0.9395	0.9364
0.003	12.0	2220	0.2252	0.9364	0.9359	0.9394	0.9364
0.003	13.0	2405	0.2265	0.9364	0.9359	0.9394	0.9364
0.0018	14.0	2590	0.2276	0.9350	0.9345	0.9383	0.9350
0.0018	15.0	2775	0.2308	0.9350	0.9345	0.9383	0.9350
0.0018	16.0	2960	0.2320	0.9364	0.9359	0.9394	0.9364
0.0012	17.0	3145	0.2327	0.9350	0.9345	0.9383	0.9350
0.0012	18.0	3330	0.2351	0.9350	0.9345	0.9383	0.9350
0.0009	19.0	3515	0.2364	0.9350	0.9345	0.9383	0.9350
0.0009	20.0	3700	0.2374	0.9350	0.9345	0.9383	0.9350
0.0009	21.0	3885	0.2386	0.9350	0.9345	0.9383	0.9350
0.0007	22.0	4070	0.2396	0.9350	0.9345	0.9383	0.9350
0.0007	23.0	4255	0.2412	0.9350	0.9345	0.9383	0.9350
0.0007	24.0	4440	0.2422	0.9350	0.9345	0.9383	0.9350
0.0006	25.0	4625	0.2429	0.9350	0.9345	0.9383	0.9350
0.0006	26.0	4810	0.2433	0.9350	0.9345	0.9383	0.9350
0.0006	27.0	4995	0.2440	0.9350	0.9345	0.9383	0.9350
0.0005	28.0	5180	0.2446	0.9350	0.9345	0.9383	0.9350
0.0005	29.0	5365	0.2449	0.9350	0.9345	0.9383	0.9350
0.0005	30.0	5550	0.2450	0.9350	0.9345	0.9383	0.9350

Shedinja/vit-pet-classification

resultados