vit-tiny_tobacco3482_simkd__tNone_gNone__logits

This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0388
Accuracy: 0.82
Brier Loss: 0.7227
Nll: 2.6079
F1 Micro: 0.82
F1 Macro: 0.7960
Ece: 0.6182
Aurc: 0.0575

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	100	0.0492	0.03	0.9002	15.7630	0.03	0.0130	0.1184	0.9407
No log	2.0	200	0.0479	0.045	0.8991	12.1839	0.045	0.0140	0.1350	0.9268
No log	3.0	300	0.0472	0.305	0.8968	12.2323	0.305	0.1551	0.2930	0.5596
No log	4.0	400	0.0463	0.305	0.8938	10.0711	0.305	0.1809	0.3077	0.5037
0.0541	5.0	500	0.0453	0.325	0.8898	9.3778	0.325	0.2046	0.3193	0.4698
0.0541	6.0	600	0.0442	0.4	0.8787	9.6363	0.4000	0.2403	0.3713	0.3759
0.0541	7.0	700	0.0433	0.4	0.8639	9.3483	0.4000	0.2510	0.3685	0.3742
0.0541	8.0	800	0.0428	0.475	0.8521	9.0007	0.4750	0.2666	0.4177	0.2978
0.0541	9.0	900	0.0422	0.515	0.8438	7.6934	0.515	0.3644	0.4480	0.2431
0.0451	10.0	1000	0.0417	0.515	0.8210	7.6243	0.515	0.3743	0.4387	0.2146
0.0451	11.0	1100	0.0411	0.68	0.8190	3.6435	0.68	0.5226	0.5677	0.1272
0.0451	12.0	1200	0.0403	0.655	0.7803	5.4537	0.655	0.5379	0.5202	0.1352
0.0451	13.0	1300	0.0398	0.75	0.7745	4.1150	0.75	0.6543	0.5945	0.0749
0.0451	14.0	1400	0.0390	0.77	0.7561	2.8538	0.7700	0.6703	0.6085	0.0753
0.0406	15.0	1500	0.0392	0.745	0.7637	3.7047	0.745	0.6673	0.5796	0.1060
0.0406	16.0	1600	0.0398	0.73	0.7603	4.3010	0.7300	0.6681	0.5693	0.0949
0.0406	17.0	1700	0.0399	0.705	0.7610	3.6375	0.705	0.6564	0.5590	0.0923
0.0406	18.0	1800	0.0397	0.705	0.7536	4.2628	0.705	0.6170	0.5472	0.1332
0.0406	19.0	1900	0.0390	0.745	0.7400	2.8861	0.745	0.6686	0.5644	0.1070
0.0379	20.0	2000	0.0394	0.785	0.7238	3.3111	0.785	0.7255	0.5995	0.0695
0.0379	21.0	2100	0.0396	0.76	0.7419	3.1935	0.76	0.7463	0.5885	0.0793
0.0379	22.0	2200	0.0396	0.785	0.7423	3.7954	0.785	0.7737	0.6043	0.0873
0.0379	23.0	2300	0.0395	0.78	0.7321	3.8067	0.78	0.7491	0.5885	0.0908
0.0379	24.0	2400	0.0387	0.8	0.7228	2.9339	0.8000	0.7758	0.6038	0.0609
0.037	25.0	2500	0.0387	0.795	0.7222	2.6252	0.795	0.7601	0.6094	0.0606
0.037	26.0	2600	0.0387	0.8	0.7241	2.6253	0.8000	0.7628	0.6110	0.0607
0.037	27.0	2700	0.0387	0.795	0.7235	2.4818	0.795	0.7616	0.6093	0.0629
0.037	28.0	2800	0.0387	0.795	0.7245	2.6226	0.795	0.7586	0.6032	0.0604
0.037	29.0	2900	0.0387	0.805	0.7253	2.7588	0.805	0.7725	0.6144	0.0609
0.0364	30.0	3000	0.0387	0.805	0.7233	2.4956	0.805	0.7701	0.6204	0.0594
0.0364	31.0	3100	0.0387	0.81	0.7241	2.7695	0.81	0.7797	0.6188	0.0602
0.0364	32.0	3200	0.0386	0.81	0.7239	2.6185	0.81	0.7797	0.6190	0.0580
0.0364	33.0	3300	0.0387	0.805	0.7238	2.9106	0.805	0.7717	0.6182	0.0586
0.0364	34.0	3400	0.0386	0.805	0.7231	2.9062	0.805	0.7725	0.6133	0.0590
0.0364	35.0	3500	0.0387	0.805	0.7247	2.7645	0.805	0.7717	0.6141	0.0590
0.0364	36.0	3600	0.0386	0.805	0.7238	2.9152	0.805	0.7717	0.6104	0.0578
0.0364	37.0	3700	0.0387	0.805	0.7229	2.9094	0.805	0.7717	0.6142	0.0588
0.0364	38.0	3800	0.0386	0.805	0.7237	2.9185	0.805	0.7717	0.6173	0.0565
0.0364	39.0	3900	0.0386	0.805	0.7230	2.9178	0.805	0.7717	0.6131	0.0578
0.0364	40.0	4000	0.0386	0.805	0.7233	2.9155	0.805	0.7717	0.6131	0.0561
0.0364	41.0	4100	0.0386	0.805	0.7235	2.9142	0.805	0.7717	0.6173	0.0574
0.0364	42.0	4200	0.0387	0.805	0.7225	2.9162	0.805	0.7717	0.6196	0.0572
0.0364	43.0	4300	0.0387	0.805	0.7231	3.0596	0.805	0.7717	0.6139	0.0560
0.0364	44.0	4400	0.0387	0.805	0.7229	3.0584	0.805	0.7717	0.6140	0.0558
0.0364	45.0	4500	0.0386	0.815	0.7231	2.9107	0.815	0.7856	0.6224	0.0551
0.0364	46.0	4600	0.0386	0.8	0.7228	3.0609	0.8000	0.7683	0.6154	0.0570
0.0364	47.0	4700	0.0386	0.8	0.7229	3.0539	0.8000	0.7683	0.6141	0.0564
0.0364	48.0	4800	0.0387	0.805	0.7228	2.9149	0.805	0.7753	0.6164	0.0559
0.0364	49.0	4900	0.0387	0.805	0.7239	3.0631	0.805	0.7729	0.6144	0.0569
0.0364	50.0	5000	0.0387	0.8	0.7231	3.0551	0.8000	0.7683	0.6094	0.0562
0.0364	51.0	5100	0.0387	0.815	0.7232	3.0662	0.815	0.7868	0.6212	0.0569
0.0364	52.0	5200	0.0386	0.805	0.7226	2.9067	0.805	0.7753	0.6241	0.0550
0.0364	53.0	5300	0.0387	0.81	0.7236	2.9086	0.81	0.7761	0.6209	0.0558
0.0364	54.0	5400	0.0387	0.805	0.7240	2.9108	0.805	0.7729	0.6072	0.0577
0.0364	55.0	5500	0.0387	0.815	0.7228	2.9235	0.815	0.7832	0.6202	0.0556
0.0364	56.0	5600	0.0387	0.82	0.7229	2.9335	0.82	0.7898	0.6273	0.0544
0.0364	57.0	5700	0.0387	0.82	0.7230	2.9210	0.82	0.7900	0.6292	0.0561
0.0364	58.0	5800	0.0387	0.82	0.7227	2.9211	0.82	0.7898	0.6325	0.0560
0.0364	59.0	5900	0.0386	0.82	0.7238	3.0664	0.82	0.7898	0.6249	0.0548
0.0364	60.0	6000	0.0387	0.82	0.7218	2.9137	0.82	0.7900	0.6397	0.0545
0.0364	61.0	6100	0.0387	0.82	0.7233	3.0756	0.82	0.7900	0.6225	0.0563
0.0364	62.0	6200	0.0387	0.815	0.7231	3.0725	0.815	0.7878	0.6190	0.0558
0.0364	63.0	6300	0.0387	0.82	0.7218	3.0589	0.82	0.7900	0.6223	0.0554
0.0364	64.0	6400	0.0387	0.82	0.7231	3.0632	0.82	0.7900	0.6289	0.0551
0.0363	65.0	6500	0.0387	0.82	0.7227	3.0595	0.82	0.7900	0.6339	0.0560
0.0363	66.0	6600	0.0387	0.82	0.7229	2.9000	0.82	0.7900	0.6320	0.0551
0.0363	67.0	6700	0.0387	0.82	0.7222	2.9092	0.82	0.7900	0.6292	0.0548
0.0363	68.0	6800	0.0387	0.82	0.7233	2.7662	0.82	0.7900	0.6299	0.0564
0.0363	69.0	6900	0.0387	0.82	0.7230	2.9095	0.82	0.7900	0.6239	0.0555
0.0363	70.0	7000	0.0387	0.82	0.7226	2.9175	0.82	0.7908	0.6274	0.0549
0.0363	71.0	7100	0.0387	0.82	0.7237	3.0548	0.82	0.7900	0.6337	0.0550
0.0363	72.0	7200	0.0387	0.815	0.7229	2.9144	0.815	0.7841	0.6207	0.0570
0.0363	73.0	7300	0.0387	0.82	0.7235	2.9041	0.82	0.7900	0.6310	0.0564
0.0363	74.0	7400	0.0387	0.82	0.7227	2.9094	0.82	0.7908	0.6291	0.0558
0.0363	75.0	7500	0.0387	0.825	0.7236	2.9105	0.825	0.7983	0.6319	0.0543
0.0363	76.0	7600	0.0387	0.82	0.7225	2.9172	0.82	0.7908	0.6260	0.0550
0.0363	77.0	7700	0.0387	0.815	0.7227	2.9050	0.815	0.7841	0.6325	0.0557
0.0363	78.0	7800	0.0387	0.825	0.7236	2.9242	0.825	0.7983	0.6264	0.0575
0.0363	79.0	7900	0.0387	0.82	0.7231	2.9167	0.82	0.7900	0.6263	0.0572
0.0363	80.0	8000	0.0387	0.825	0.7229	2.7707	0.825	0.8004	0.6311	0.0569
0.0363	81.0	8100	0.0387	0.81	0.7230	2.9083	0.81	0.7812	0.6295	0.0573
0.0363	82.0	8200	0.0387	0.82	0.7227	2.7600	0.82	0.7927	0.6263	0.0576
0.0363	83.0	8300	0.0387	0.815	0.7226	2.7902	0.815	0.7930	0.6169	0.0576
0.0363	84.0	8400	0.0387	0.815	0.7226	2.7666	0.815	0.7841	0.6254	0.0571
0.0363	85.0	8500	0.0387	0.82	0.7228	2.7730	0.82	0.7960	0.6226	0.0566
0.0363	86.0	8600	0.0387	0.815	0.7228	2.6311	0.815	0.7878	0.6186	0.0572
0.0363	87.0	8700	0.0388	0.82	0.7230	2.6272	0.82	0.7924	0.6321	0.0575
0.0363	88.0	8800	0.0387	0.82	0.7227	2.7579	0.82	0.7924	0.6318	0.0568
0.0363	89.0	8900	0.0388	0.82	0.7227	2.7688	0.82	0.7960	0.6238	0.0575
0.0363	90.0	9000	0.0387	0.82	0.7232	2.6100	0.82	0.7986	0.6290	0.0569
0.0363	91.0	9100	0.0387	0.82	0.7228	2.6088	0.82	0.7960	0.6258	0.0574
0.0363	92.0	9200	0.0387	0.825	0.7228	2.6134	0.825	0.8038	0.6239	0.0575
0.0363	93.0	9300	0.0387	0.825	0.7229	2.6136	0.825	0.7990	0.6283	0.0576
0.0363	94.0	9400	0.0387	0.82	0.7228	2.6112	0.82	0.7924	0.6235	0.0580
0.0363	95.0	9500	0.0388	0.82	0.7228	2.6077	0.82	0.7960	0.6185	0.0575
0.0363	96.0	9600	0.0388	0.825	0.7229	2.6073	0.825	0.8038	0.6206	0.0572
0.0363	97.0	9700	0.0388	0.82	0.7227	2.6077	0.82	0.7960	0.6213	0.0572
0.0363	98.0	9800	0.0388	0.815	0.7228	2.6094	0.815	0.7893	0.6161	0.0579
0.0363	99.0	9900	0.0388	0.815	0.7228	2.6086	0.815	0.7893	0.6160	0.0579
0.0363	100.0	10000	0.0388	0.82	0.7227	2.6079	0.82	0.7960	0.6182	0.0575

Framework versions

Transformers 4.28.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.12.0
Tokenizers 0.12.1

加载中…

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001

train_batch_size: 8

eval_batch_size: 8

seed: 42

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: linear

lr_scheduler_warmup_ratio: 0.1

num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	100	0.0492	0.03	0.9002	15.7630	0.03	0.0130	0.1184	0.9407
No log	2.0	200	0.0479	0.045	0.8991	12.1839	0.045	0.0140	0.1350	0.9268
No log	3.0	300	0.0472	0.305	0.8968	12.2323	0.305	0.1551	0.2930	0.5596
No log	4.0	400	0.0463	0.305	0.8938	10.0711	0.305	0.1809	0.3077	0.5037
0.0541	5.0	500	0.0453	0.325	0.8898	9.3778	0.325	0.2046	0.3193	0.4698
0.0541	6.0	600	0.0442	0.4	0.8787	9.6363	0.4000	0.2403	0.3713	0.3759
0.0541	7.0	700	0.0433	0.4	0.8639	9.3483	0.4000	0.2510	0.3685	0.3742
0.0541	8.0	800	0.0428	0.475	0.8521	9.0007	0.4750	0.2666	0.4177	0.2978
0.0541	9.0	900	0.0422	0.515	0.8438	7.6934	0.515	0.3644	0.4480	0.2431
0.0451	10.0	1000	0.0417	0.515	0.8210	7.6243	0.515	0.3743	0.4387	0.2146
0.0451	11.0	1100	0.0411	0.68	0.8190	3.6435	0.68	0.5226	0.5677	0.1272
0.0451	12.0	1200	0.0403	0.655	0.7803	5.4537	0.655	0.5379	0.5202	0.1352
0.0451	13.0	1300	0.0398	0.75	0.7745	4.1150	0.75	0.6543	0.5945	0.0749
0.0451	14.0	1400	0.0390	0.77	0.7561	2.8538	0.7700	0.6703	0.6085	0.0753
0.0406	15.0	1500	0.0392	0.745	0.7637	3.7047	0.745	0.6673	0.5796	0.1060
0.0406	16.0	1600	0.0398	0.73	0.7603	4.3010	0.7300	0.6681	0.5693	0.0949
0.0406	17.0	1700	0.0399	0.705	0.7610	3.6375	0.705	0.6564	0.5590	0.0923
0.0406	18.0	1800	0.0397	0.705	0.7536	4.2628	0.705	0.6170	0.5472	0.1332
0.0406	19.0	1900	0.0390	0.745	0.7400	2.8861	0.745	0.6686	0.5644	0.1070
0.0379	20.0	2000	0.0394	0.785	0.7238	3.3111	0.785	0.7255	0.5995	0.0695
0.0379	21.0	2100	0.0396	0.76	0.7419	3.1935	0.76	0.7463	0.5885	0.0793
0.0379	22.0	2200	0.0396	0.785	0.7423	3.7954	0.785	0.7737	0.6043	0.0873
0.0379	23.0	2300	0.0395	0.78	0.7321	3.8067	0.78	0.7491	0.5885	0.0908
0.0379	24.0	2400	0.0387	0.8	0.7228	2.9339	0.8000	0.7758	0.6038	0.0609
0.037	25.0	2500	0.0387	0.795	0.7222	2.6252	0.795	0.7601	0.6094	0.0606
0.037	26.0	2600	0.0387	0.8	0.7241	2.6253	0.8000	0.7628	0.6110	0.0607
0.037	27.0	2700	0.0387	0.795	0.7235	2.4818	0.795	0.7616	0.6093	0.0629
0.037	28.0	2800	0.0387	0.795	0.7245	2.6226	0.795	0.7586	0.6032	0.0604
0.037	29.0	2900	0.0387	0.805	0.7253	2.7588	0.805	0.7725	0.6144	0.0609
0.0364	30.0	3000	0.0387	0.805	0.7233	2.4956	0.805	0.7701	0.6204	0.0594
0.0364	31.0	3100	0.0387	0.81	0.7241	2.7695	0.81	0.7797	0.6188	0.0602
0.0364	32.0	3200	0.0386	0.81	0.7239	2.6185	0.81	0.7797	0.6190	0.0580
0.0364	33.0	3300	0.0387	0.805	0.7238	2.9106	0.805	0.7717	0.6182	0.0586
0.0364	34.0	3400	0.0386	0.805	0.7231	2.9062	0.805	0.7725	0.6133	0.0590
0.0364	35.0	3500	0.0387	0.805	0.7247	2.7645	0.805	0.7717	0.6141	0.0590
0.0364	36.0	3600	0.0386	0.805	0.7238	2.9152	0.805	0.7717	0.6104	0.0578
0.0364	37.0	3700	0.0387	0.805	0.7229	2.9094	0.805	0.7717	0.6142	0.0588
0.0364	38.0	3800	0.0386	0.805	0.7237	2.9185	0.805	0.7717	0.6173	0.0565
0.0364	39.0	3900	0.0386	0.805	0.7230	2.9178	0.805	0.7717	0.6131	0.0578
0.0364	40.0	4000	0.0386	0.805	0.7233	2.9155	0.805	0.7717	0.6131	0.0561
0.0364	41.0	4100	0.0386	0.805	0.7235	2.9142	0.805	0.7717	0.6173	0.0574
0.0364	42.0	4200	0.0387	0.805	0.7225	2.9162	0.805	0.7717	0.6196	0.0572
0.0364	43.0	4300	0.0387	0.805	0.7231	3.0596	0.805	0.7717	0.6139	0.0560
0.0364	44.0	4400	0.0387	0.805	0.7229	3.0584	0.805	0.7717	0.6140	0.0558
0.0364	45.0	4500	0.0386	0.815	0.7231	2.9107	0.815	0.7856	0.6224	0.0551
0.0364	46.0	4600	0.0386	0.8	0.7228	3.0609	0.8000	0.7683	0.6154	0.0570
0.0364	47.0	4700	0.0386	0.8	0.7229	3.0539	0.8000	0.7683	0.6141	0.0564
0.0364	48.0	4800	0.0387	0.805	0.7228	2.9149	0.805	0.7753	0.6164	0.0559
0.0364	49.0	4900	0.0387	0.805	0.7239	3.0631	0.805	0.7729	0.6144	0.0569
0.0364	50.0	5000	0.0387	0.8	0.7231	3.0551	0.8000	0.7683	0.6094	0.0562
0.0364	51.0	5100	0.0387	0.815	0.7232	3.0662	0.815	0.7868	0.6212	0.0569
0.0364	52.0	5200	0.0386	0.805	0.7226	2.9067	0.805	0.7753	0.6241	0.0550
0.0364	53.0	5300	0.0387	0.81	0.7236	2.9086	0.81	0.7761	0.6209	0.0558
0.0364	54.0	5400	0.0387	0.805	0.7240	2.9108	0.805	0.7729	0.6072	0.0577
0.0364	55.0	5500	0.0387	0.815	0.7228	2.9235	0.815	0.7832	0.6202	0.0556
0.0364	56.0	5600	0.0387	0.82	0.7229	2.9335	0.82	0.7898	0.6273	0.0544
0.0364	57.0	5700	0.0387	0.82	0.7230	2.9210	0.82	0.7900	0.6292	0.0561
0.0364	58.0	5800	0.0387	0.82	0.7227	2.9211	0.82	0.7898	0.6325	0.0560
0.0364	59.0	5900	0.0386	0.82	0.7238	3.0664	0.82	0.7898	0.6249	0.0548
0.0364	60.0	6000	0.0387	0.82	0.7218	2.9137	0.82	0.7900	0.6397	0.0545
0.0364	61.0	6100	0.0387	0.82	0.7233	3.0756	0.82	0.7900	0.6225	0.0563
0.0364	62.0	6200	0.0387	0.815	0.7231	3.0725	0.815	0.7878	0.6190	0.0558
0.0364	63.0	6300	0.0387	0.82	0.7218	3.0589	0.82	0.7900	0.6223	0.0554
0.0364	64.0	6400	0.0387	0.82	0.7231	3.0632	0.82	0.7900	0.6289	0.0551
0.0363	65.0	6500	0.0387	0.82	0.7227	3.0595	0.82	0.7900	0.6339	0.0560
0.0363	66.0	6600	0.0387	0.82	0.7229	2.9000	0.82	0.7900	0.6320	0.0551
0.0363	67.0	6700	0.0387	0.82	0.7222	2.9092	0.82	0.7900	0.6292	0.0548
0.0363	68.0	6800	0.0387	0.82	0.7233	2.7662	0.82	0.7900	0.6299	0.0564
0.0363	69.0	6900	0.0387	0.82	0.7230	2.9095	0.82	0.7900	0.6239	0.0555
0.0363	70.0	7000	0.0387	0.82	0.7226	2.9175	0.82	0.7908	0.6274	0.0549
0.0363	71.0	7100	0.0387	0.82	0.7237	3.0548	0.82	0.7900	0.6337	0.0550
0.0363	72.0	7200	0.0387	0.815	0.7229	2.9144	0.815	0.7841	0.6207	0.0570
0.0363	73.0	7300	0.0387	0.82	0.7235	2.9041	0.82	0.7900	0.6310	0.0564
0.0363	74.0	7400	0.0387	0.82	0.7227	2.9094	0.82	0.7908	0.6291	0.0558
0.0363	75.0	7500	0.0387	0.825	0.7236	2.9105	0.825	0.7983	0.6319	0.0543
0.0363	76.0	7600	0.0387	0.82	0.7225	2.9172	0.82	0.7908	0.6260	0.0550
0.0363	77.0	7700	0.0387	0.815	0.7227	2.9050	0.815	0.7841	0.6325	0.0557
0.0363	78.0	7800	0.0387	0.825	0.7236	2.9242	0.825	0.7983	0.6264	0.0575
0.0363	79.0	7900	0.0387	0.82	0.7231	2.9167	0.82	0.7900	0.6263	0.0572
0.0363	80.0	8000	0.0387	0.825	0.7229	2.7707	0.825	0.8004	0.6311	0.0569
0.0363	81.0	8100	0.0387	0.81	0.7230	2.9083	0.81	0.7812	0.6295	0.0573
0.0363	82.0	8200	0.0387	0.82	0.7227	2.7600	0.82	0.7927	0.6263	0.0576
0.0363	83.0	8300	0.0387	0.815	0.7226	2.7902	0.815	0.7930	0.6169	0.0576
0.0363	84.0	8400	0.0387	0.815	0.7226	2.7666	0.815	0.7841	0.6254	0.0571
0.0363	85.0	8500	0.0387	0.82	0.7228	2.7730	0.82	0.7960	0.6226	0.0566
0.0363	86.0	8600	0.0387	0.815	0.7228	2.6311	0.815	0.7878	0.6186	0.0572
0.0363	87.0	8700	0.0388	0.82	0.7230	2.6272	0.82	0.7924	0.6321	0.0575
0.0363	88.0	8800	0.0387	0.82	0.7227	2.7579	0.82	0.7924	0.6318	0.0568
0.0363	89.0	8900	0.0388	0.82	0.7227	2.7688	0.82	0.7960	0.6238	0.0575
0.0363	90.0	9000	0.0387	0.82	0.7232	2.6100	0.82	0.7986	0.6290	0.0569
0.0363	91.0	9100	0.0387	0.82	0.7228	2.6088	0.82	0.7960	0.6258	0.0574
0.0363	92.0	9200	0.0387	0.825	0.7228	2.6134	0.825	0.8038	0.6239	0.0575
0.0363	93.0	9300	0.0387	0.825	0.7229	2.6136	0.825	0.7990	0.6283	0.0576
0.0363	94.0	9400	0.0387	0.82	0.7228	2.6112	0.82	0.7924	0.6235	0.0580
0.0363	95.0	9500	0.0388	0.82	0.7228	2.6077	0.82	0.7960	0.6185	0.0575
0.0363	96.0	9600	0.0388	0.825	0.7229	2.6073	0.825	0.8038	0.6206	0.0572
0.0363	97.0	9700	0.0388	0.82	0.7227	2.6077	0.82	0.7960	0.6213	0.0572
0.0363	98.0	9800	0.0388	0.815	0.7228	2.6094	0.815	0.7893	0.6161	0.0579
0.0363	99.0	9900	0.0388	0.815	0.7228	2.6086	0.815	0.7893	0.6160	0.0579
0.0363	100.0	10000	0.0388	0.82	0.7227	2.6079	0.82	0.7960	0.6182	0.0575

Framework versions

Transformers 4.28.0.dev0

Pytorch 1.12.1+cu113

Datasets 2.12.0

Tokenizers 0.12.1

jordyvl/vit-tiny_tobacco3482_simkd__tNone_gNone__logits

vit-tiny_tobacco3482_simkd__tNone_gNone__logits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vit-tiny_tobacco3482_simkd__tNone_gNone__logits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions