dit-base_tobacco-tiny_tobacco3482_kd_MSE

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: 1.0108
Accuracy: 0.815
Brier Loss: 0.2593
Nll: 1.1011
F1 Micro: 0.815
F1 Macro: 0.8014
Ece: 0.1462
Aurc: 0.0442

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: 128
eval_batch_size: 128
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	7	7.1562	0.195	0.9158	7.6908	0.195	0.1043	0.2991	0.7927
No log	2.0	14	6.3485	0.245	0.8600	4.2261	0.245	0.1794	0.2960	0.7445
No log	3.0	21	5.2488	0.41	0.7090	3.6293	0.41	0.3494	0.2826	0.3786
No log	4.0	28	4.0484	0.565	0.5698	1.7703	0.565	0.5209	0.2622	0.2372
No log	5.0	35	3.0368	0.655	0.4710	1.5921	0.655	0.6368	0.2222	0.1598
No log	6.0	42	2.6191	0.695	0.4219	1.6919	0.695	0.6535	0.2041	0.1200
No log	7.0	49	2.0941	0.725	0.3913	1.3852	0.7250	0.6844	0.2046	0.0966
No log	8.0	56	2.0668	0.725	0.4119	1.3829	0.7250	0.6811	0.1890	0.1045
No log	9.0	63	1.7456	0.79	0.3138	1.5258	0.79	0.7539	0.1521	0.0651
No log	10.0	70	1.5815	0.77	0.3391	1.2461	0.7700	0.7323	0.1593	0.0725
No log	11.0	77	1.5720	0.785	0.2895	1.3282	0.785	0.7659	0.1408	0.0522
No log	12.0	84	1.8886	0.78	0.3692	1.6238	0.78	0.7717	0.2015	0.0917
No log	13.0	91	1.6164	0.785	0.2918	1.6303	0.785	0.7925	0.1545	0.0564
No log	14.0	98	1.4318	0.785	0.3220	1.3070	0.785	0.7606	0.1430	0.0639
No log	15.0	105	1.2774	0.81	0.2807	1.2877	0.81	0.7939	0.1532	0.0595
No log	16.0	112	1.3797	0.8	0.2993	1.2409	0.8000	0.7759	0.1565	0.0700
No log	17.0	119	1.3629	0.795	0.3091	1.1781	0.795	0.7670	0.1712	0.0567
No log	18.0	126	1.5101	0.8	0.3192	1.3586	0.8000	0.7878	0.1919	0.0665
No log	19.0	133	1.3897	0.805	0.2857	1.4983	0.805	0.7851	0.1356	0.0516
No log	20.0	140	1.3821	0.795	0.3204	1.0916	0.795	0.7745	0.1678	0.0651
No log	21.0	147	1.2852	0.83	0.2621	1.5182	0.83	0.8246	0.1483	0.0486
No log	22.0	154	1.2080	0.815	0.2744	1.1921	0.815	0.7957	0.1319	0.0500
No log	23.0	161	1.4016	0.805	0.3165	1.3364	0.805	0.7844	0.1534	0.0624
No log	24.0	168	1.2883	0.825	0.2592	1.4946	0.825	0.8119	0.1549	0.0481
No log	25.0	175	1.1715	0.815	0.2676	1.3363	0.815	0.8054	0.1464	0.0494
No log	26.0	182	1.1844	0.825	0.2585	1.4938	0.825	0.8045	0.1572	0.0469
No log	27.0	189	1.1739	0.81	0.2959	1.0692	0.81	0.7909	0.1625	0.0550
No log	28.0	196	1.1944	0.815	0.2891	1.1811	0.815	0.7971	0.1430	0.0572
No log	29.0	203	1.2115	0.83	0.2597	1.4809	0.83	0.8101	0.1289	0.0469
No log	30.0	210	1.1622	0.81	0.2825	1.1104	0.81	0.7931	0.1463	0.0511
No log	31.0	217	1.2591	0.8	0.3096	1.2310	0.8000	0.7789	0.1719	0.0591
No log	32.0	224	1.1752	0.82	0.2687	1.4091	0.82	0.7959	0.1581	0.0504
No log	33.0	231	1.1114	0.815	0.2719	1.0945	0.815	0.7885	0.1492	0.0485
No log	34.0	238	1.1105	0.815	0.2727	1.1239	0.815	0.7962	0.1300	0.0479
No log	35.0	245	1.1662	0.825	0.2748	1.3396	0.825	0.8100	0.1571	0.0554
No log	36.0	252	1.1023	0.815	0.2757	1.1805	0.815	0.8031	0.1428	0.0504
No log	37.0	259	1.1060	0.84	0.2604	1.3305	0.8400	0.8319	0.1596	0.0487
No log	38.0	266	1.1123	0.81	0.2682	1.1122	0.81	0.7922	0.1310	0.0482
No log	39.0	273	1.0820	0.815	0.2669	1.1629	0.815	0.7955	0.1479	0.0490
No log	40.0	280	1.0972	0.805	0.2784	1.2442	0.805	0.7858	0.1576	0.0483
No log	41.0	287	1.0845	0.83	0.2705	1.1180	0.83	0.8221	0.1504	0.0468
No log	42.0	294	1.0769	0.82	0.2602	1.1173	0.82	0.8066	0.1458	0.0451
No log	43.0	301	1.1366	0.81	0.2939	1.0722	0.81	0.7958	0.1532	0.0526
No log	44.0	308	1.0716	0.82	0.2635	1.1839	0.82	0.8043	0.1403	0.0451
No log	45.0	315	1.0865	0.81	0.2770	1.3595	0.81	0.7929	0.1501	0.0528
No log	46.0	322	1.0768	0.82	0.2638	1.1161	0.82	0.8067	0.1462	0.0457
No log	47.0	329	1.0644	0.825	0.2552	1.2086	0.825	0.8098	0.1579	0.0439
No log	48.0	336	1.0511	0.815	0.2656	1.1019	0.815	0.8014	0.1518	0.0471
No log	49.0	343	1.0517	0.82	0.2717	1.0881	0.82	0.8044	0.1559	0.0473
No log	50.0	350	1.0824	0.81	0.2813	1.1022	0.81	0.7968	0.1538	0.0505
No log	51.0	357	1.1439	0.835	0.2634	1.3483	0.835	0.8206	0.1471	0.0496
No log	52.0	364	1.0444	0.83	0.2500	1.0999	0.83	0.8156	0.1310	0.0423
No log	53.0	371	1.0426	0.825	0.2644	1.1112	0.825	0.8053	0.1295	0.0474
No log	54.0	378	1.0341	0.825	0.2635	1.1053	0.825	0.8092	0.1467	0.0465
No log	55.0	385	1.0900	0.815	0.2762	1.1021	0.815	0.7990	0.1439	0.0480
No log	56.0	392	1.0423	0.845	0.2517	1.2594	0.845	0.8444	0.1497	0.0428
No log	57.0	399	1.0246	0.825	0.2634	1.0927	0.825	0.8130	0.1260	0.0454
No log	58.0	406	1.0365	0.835	0.2649	1.0825	0.835	0.8232	0.1291	0.0448
No log	59.0	413	1.0394	0.82	0.2668	1.0968	0.82	0.8045	0.1458	0.0460
No log	60.0	420	1.0261	0.815	0.2720	1.0883	0.815	0.8011	0.1409	0.0472
No log	61.0	427	1.0503	0.83	0.2543	1.3230	0.83	0.8132	0.1378	0.0455
No log	62.0	434	1.0400	0.82	0.2637	1.0958	0.82	0.8043	0.1397	0.0456
No log	63.0	441	1.0338	0.82	0.2629	1.0960	0.82	0.8042	0.1338	0.0435
No log	64.0	448	1.0373	0.84	0.2508	1.2817	0.8400	0.8260	0.1325	0.0433
No log	65.0	455	1.0266	0.83	0.2663	1.1057	0.83	0.8163	0.1383	0.0460
No log	66.0	462	1.0303	0.825	0.2549	1.1906	0.825	0.8098	0.1399	0.0450
No log	67.0	469	1.0224	0.82	0.2668	1.0920	0.82	0.8042	0.1252	0.0433
No log	68.0	476	1.0274	0.845	0.2526	1.1948	0.845	0.8368	0.1423	0.0442
No log	69.0	483	1.0145	0.82	0.2647	1.0884	0.82	0.8070	0.1345	0.0449
No log	70.0	490	1.0194	0.815	0.2606	1.1076	0.815	0.8014	0.1529	0.0446
No log	71.0	497	1.0153	0.825	0.2572	1.2484	0.825	0.8142	0.1425	0.0445
0.6377	72.0	504	1.0265	0.815	0.2607	1.1109	0.815	0.8039	0.1457	0.0445
0.6377	73.0	511	1.0081	0.82	0.2567	1.1031	0.82	0.8040	0.1321	0.0440
0.6377	74.0	518	1.0135	0.825	0.2600	1.1036	0.825	0.8074	0.1477	0.0450
0.6377	75.0	525	1.0053	0.82	0.2616	1.1012	0.82	0.8044	0.1542	0.0442
0.6377	76.0	532	1.0187	0.82	0.2598	1.1115	0.82	0.8069	0.1566	0.0445
0.6377	77.0	539	1.0127	0.82	0.2610	1.1024	0.82	0.8097	0.1489	0.0443
0.6377	78.0	546	1.0079	0.82	0.2581	1.1034	0.82	0.8069	0.1463	0.0434
0.6377	79.0	553	1.0097	0.815	0.2592	1.1030	0.815	0.8014	0.1478	0.0438
0.6377	80.0	560	1.0131	0.835	0.2556	1.1048	0.835	0.8281	0.1508	0.0441
0.6377	81.0	567	1.0183	0.82	0.2602	1.1057	0.82	0.8044	0.1417	0.0446
0.6377	82.0	574	1.0190	0.815	0.2665	1.0966	0.815	0.7987	0.1370	0.0462
0.6377	83.0	581	1.0117	0.815	0.2619	1.0974	0.815	0.8014	0.1614	0.0442
0.6377	84.0	588	1.0099	0.82	0.2557	1.1070	0.82	0.8044	0.1327	0.0436
0.6377	85.0	595	1.0088	0.82	0.2569	1.1037	0.82	0.8044	0.1446	0.0437
0.6377	86.0	602	1.0110	0.82	0.2596	1.0945	0.82	0.8043	0.1505	0.0442
0.6377	87.0	609	1.0151	0.815	0.2606	1.1046	0.815	0.8014	0.1416	0.0451
0.6377	88.0	616	1.0101	0.815	0.2587	1.1025	0.815	0.8014	0.1435	0.0440
0.6377	89.0	623	1.0106	0.815	0.2613	1.0976	0.815	0.8014	0.1489	0.0443
0.6377	90.0	630	1.0097	0.815	0.2590	1.0993	0.815	0.8014	0.1490	0.0439
0.6377	91.0	637	1.0098	0.815	0.2593	1.1024	0.815	0.8014	0.1510	0.0440
0.6377	92.0	644	1.0116	0.815	0.2600	1.1004	0.815	0.8014	0.1465	0.0442
0.6377	93.0	651	1.0107	0.815	0.2596	1.1005	0.815	0.8014	0.1548	0.0442
0.6377	94.0	658	1.0110	0.815	0.2599	1.0993	0.815	0.8014	0.1463	0.0440
0.6377	95.0	665	1.0106	0.815	0.2593	1.1011	0.815	0.8014	0.1409	0.0441
0.6377	96.0	672	1.0106	0.815	0.2596	1.1011	0.815	0.8014	0.1496	0.0442
0.6377	97.0	679	1.0109	0.815	0.2595	1.1007	0.815	0.8014	0.1462	0.0442
0.6377	98.0	686	1.0107	0.815	0.2593	1.1013	0.815	0.8014	0.1409	0.0441
0.6377	99.0	693	1.0107	0.815	0.2594	1.1009	0.815	0.8014	0.1462	0.0441
0.6377	100.0	700	1.0108	0.815	0.2593	1.1011	0.815	0.8014	0.1462	0.0442

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.2.0.dev20231112+cu118
Datasets 2.14.5
Tokenizers 0.14.1

jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_MSE

dit-base_tobacco-tiny_tobacco3482_kd_MSE

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions