225-tiny_tobacco3482_kd

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: 0.2991
Accuracy: 0.775
Brier Loss: 0.3491
Nll: 1.2196
F1 Micro: 0.775
F1 Macro: 0.7302
Ece: 0.2602
Aurc: 0.0644

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: 64
eval_batch_size: 64
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	13	1.6344	0.23	0.8900	7.7885	0.23	0.1633	0.2747	0.7585
No log	2.0	26	1.0824	0.385	0.7943	4.1668	0.3850	0.2795	0.3160	0.4560
No log	3.0	39	0.8639	0.535	0.6762	3.0145	0.535	0.4086	0.3233	0.2913
No log	4.0	52	0.7309	0.595	0.5956	2.2236	0.595	0.4646	0.3075	0.1944
No log	5.0	65	0.6374	0.67	0.5211	2.1759	0.67	0.5737	0.2898	0.1450
No log	6.0	78	0.6720	0.685	0.4833	2.2861	0.685	0.5860	0.2904	0.1331
No log	7.0	91	0.6097	0.675	0.4767	2.3133	0.675	0.5733	0.2622	0.1519
No log	8.0	104	0.5206	0.705	0.4301	1.8228	0.705	0.6164	0.2603	0.1038
No log	9.0	117	0.5486	0.715	0.4414	1.8451	0.715	0.6444	0.2583	0.1063
No log	10.0	130	0.5067	0.7	0.4171	1.7759	0.7	0.6325	0.2611	0.1071
No log	11.0	143	0.4612	0.745	0.4017	1.4919	0.745	0.6635	0.2840	0.0838
No log	12.0	156	0.4785	0.745	0.4204	1.8579	0.745	0.6750	0.2542	0.0979
No log	13.0	169	0.4518	0.715	0.4036	1.5697	0.715	0.6496	0.2744	0.1002
No log	14.0	182	0.5081	0.7	0.4294	1.9850	0.7	0.6514	0.2364	0.1225
No log	15.0	195	0.4415	0.705	0.3994	1.7828	0.705	0.6301	0.2380	0.0992
No log	16.0	208	0.3859	0.73	0.3832	1.3431	0.7300	0.6516	0.2548	0.0817
No log	17.0	221	0.3869	0.75	0.3832	1.2075	0.75	0.6651	0.2622	0.0758
No log	18.0	234	0.3637	0.755	0.3770	1.2290	0.755	0.7108	0.2569	0.0687
No log	19.0	247	0.3933	0.745	0.3700	1.4931	0.745	0.6812	0.2434	0.0799
No log	20.0	260	0.3540	0.745	0.3721	1.1910	0.745	0.6702	0.2208	0.0760
No log	21.0	273	0.3560	0.77	0.3718	1.1248	0.7700	0.7142	0.2731	0.0743
No log	22.0	286	0.3530	0.74	0.3758	1.4213	0.74	0.6902	0.2326	0.0768
No log	23.0	299	0.3419	0.745	0.3699	1.2528	0.745	0.6714	0.2324	0.0765
No log	24.0	312	0.3302	0.775	0.3595	1.3338	0.775	0.7120	0.2521	0.0665
No log	25.0	325	0.3533	0.775	0.3672	1.4609	0.775	0.7167	0.2482	0.0740
No log	26.0	338	0.3416	0.775	0.3684	1.1575	0.775	0.7124	0.2601	0.0732
No log	27.0	351	0.3463	0.75	0.3714	1.1053	0.75	0.6868	0.2512	0.0808
No log	28.0	364	0.3298	0.775	0.3605	1.2108	0.775	0.6986	0.2537	0.0668
No log	29.0	377	0.3278	0.77	0.3645	1.1893	0.7700	0.7013	0.2447	0.0765
No log	30.0	390	0.3165	0.78	0.3608	1.1615	0.78	0.7285	0.2472	0.0712
No log	31.0	403	0.3212	0.765	0.3571	1.1317	0.765	0.6999	0.2497	0.0725
No log	32.0	416	0.3119	0.765	0.3581	1.0644	0.765	0.6881	0.2285	0.0675
No log	33.0	429	0.3229	0.765	0.3523	1.2937	0.765	0.7138	0.2517	0.0658
No log	34.0	442	0.3193	0.78	0.3660	1.1849	0.78	0.7329	0.2686	0.0700
No log	35.0	455	0.3088	0.775	0.3556	1.1613	0.775	0.7071	0.2640	0.0680
No log	36.0	468	0.3113	0.785	0.3508	1.1715	0.785	0.7501	0.2443	0.0656
No log	37.0	481	0.3113	0.79	0.3526	1.2334	0.79	0.7388	0.2580	0.0639
No log	38.0	494	0.3077	0.755	0.3528	1.1152	0.755	0.6973	0.2401	0.0692
0.2783	39.0	507	0.3064	0.775	0.3567	1.2289	0.775	0.7370	0.2417	0.0696
0.2783	40.0	520	0.3063	0.77	0.3521	1.2437	0.7700	0.7232	0.2396	0.0688
0.2783	41.0	533	0.3042	0.77	0.3541	1.2490	0.7700	0.7234	0.2470	0.0682
0.2783	42.0	546	0.2999	0.77	0.3486	1.1626	0.7700	0.7082	0.2491	0.0638
0.2783	43.0	559	0.3020	0.77	0.3515	1.2141	0.7700	0.7312	0.2570	0.0687
0.2783	44.0	572	0.3024	0.775	0.3502	1.2184	0.775	0.7168	0.2568	0.0648
0.2783	45.0	585	0.3002	0.78	0.3517	1.2189	0.78	0.7364	0.2673	0.0644
0.2783	46.0	598	0.3022	0.775	0.3511	1.1594	0.775	0.7266	0.2538	0.0661
0.2783	47.0	611	0.2974	0.775	0.3464	1.2157	0.775	0.7238	0.2630	0.0627
0.2783	48.0	624	0.3003	0.78	0.3519	1.1584	0.78	0.7318	0.2413	0.0666
0.2783	49.0	637	0.2990	0.77	0.3492	1.2187	0.7700	0.7136	0.2401	0.0643
0.2783	50.0	650	0.3019	0.765	0.3516	1.2254	0.765	0.7180	0.2409	0.0673
0.2783	51.0	663	0.2991	0.77	0.3499	1.2186	0.7700	0.7145	0.2566	0.0646
0.2783	52.0	676	0.2990	0.77	0.3507	1.2204	0.7700	0.7207	0.2360	0.0651
0.2783	53.0	689	0.2982	0.765	0.3488	1.1663	0.765	0.7042	0.2338	0.0643
0.2783	54.0	702	0.2969	0.775	0.3485	1.1667	0.775	0.7302	0.2586	0.0642
0.2783	55.0	715	0.2989	0.775	0.3487	1.2181	0.775	0.7302	0.2670	0.0647
0.2783	56.0	728	0.2991	0.77	0.3499	1.2208	0.7700	0.7136	0.2339	0.0650
0.2783	57.0	741	0.2986	0.775	0.3487	1.2162	0.775	0.7302	0.2415	0.0639
0.2783	58.0	754	0.2985	0.77	0.3490	1.2183	0.7700	0.7207	0.2547	0.0647
0.2783	59.0	767	0.2993	0.77	0.3494	1.2218	0.7700	0.7136	0.2417	0.0649
0.2783	60.0	780	0.2983	0.77	0.3487	1.2185	0.7700	0.7207	0.2555	0.0646
0.2783	61.0	793	0.2989	0.775	0.3492	1.2182	0.775	0.7302	0.2444	0.0645
0.2783	62.0	806	0.2987	0.775	0.3487	1.2174	0.775	0.7302	0.2438	0.0642
0.2783	63.0	819	0.2987	0.775	0.3490	1.2198	0.775	0.7302	0.2508	0.0646
0.2783	64.0	832	0.2989	0.775	0.3494	1.2195	0.775	0.7302	0.2609	0.0646
0.2783	65.0	845	0.2990	0.775	0.3492	1.2177	0.775	0.7302	0.2528	0.0644
0.2783	66.0	858	0.2992	0.775	0.3493	1.2193	0.775	0.7302	0.2537	0.0646
0.2783	67.0	871	0.2990	0.775	0.3493	1.2199	0.775	0.7302	0.2510	0.0647
0.2783	68.0	884	0.2991	0.775	0.3495	1.2199	0.775	0.7302	0.2476	0.0646
0.2783	69.0	897	0.2989	0.775	0.3491	1.2187	0.775	0.7302	0.2606	0.0646
0.2783	70.0	910	0.2987	0.775	0.3490	1.2187	0.775	0.7302	0.2436	0.0642
0.2783	71.0	923	0.2990	0.775	0.3491	1.2190	0.775	0.7302	0.2510	0.0646
0.2783	72.0	936	0.2990	0.775	0.3492	1.2191	0.775	0.7302	0.2541	0.0646
0.2783	73.0	949	0.2990	0.775	0.3491	1.2176	0.775	0.7302	0.2509	0.0647
0.2783	74.0	962	0.2990	0.775	0.3493	1.2203	0.775	0.7302	0.2600	0.0643
0.2783	75.0	975	0.2989	0.775	0.3492	1.2203	0.775	0.7302	0.2665	0.0643
0.2783	76.0	988	0.2991	0.775	0.3492	1.2193	0.775	0.7302	0.2601	0.0643
0.0005	77.0	1001	0.2991	0.775	0.3491	1.2201	0.775	0.7302	0.2598	0.0645
0.0005	78.0	1014	0.2991	0.775	0.3490	1.2198	0.775	0.7302	0.2441	0.0645
0.0005	79.0	1027	0.2991	0.775	0.3492	1.2182	0.775	0.7302	0.2513	0.0645
0.0005	80.0	1040	0.2992	0.775	0.3491	1.2183	0.775	0.7302	0.2514	0.0645
0.0005	81.0	1053	0.2992	0.775	0.3492	1.2196	0.775	0.7302	0.2584	0.0646
0.0005	82.0	1066	0.2992	0.775	0.3493	1.2199	0.775	0.7302	0.2520	0.0646
0.0005	83.0	1079	0.2991	0.775	0.3491	1.2191	0.775	0.7302	0.2514	0.0643
0.0005	84.0	1092	0.2991	0.775	0.3491	1.2194	0.775	0.7302	0.2516	0.0645
0.0005	85.0	1105	0.2990	0.775	0.3491	1.2188	0.775	0.7302	0.2585	0.0645
0.0005	86.0	1118	0.2991	0.775	0.3492	1.2193	0.775	0.7302	0.2584	0.0645
0.0005	87.0	1131	0.2991	0.775	0.3491	1.2201	0.775	0.7302	0.2667	0.0643
0.0005	88.0	1144	0.2991	0.775	0.3492	1.2199	0.775	0.7302	0.2516	0.0645
0.0005	89.0	1157	0.2990	0.775	0.3491	1.2193	0.775	0.7302	0.2603	0.0644
0.0005	90.0	1170	0.2990	0.775	0.3492	1.2197	0.775	0.7302	0.2536	0.0645
0.0005	91.0	1183	0.2990	0.775	0.3491	1.2201	0.775	0.7302	0.2668	0.0644
0.0005	92.0	1196	0.2991	0.775	0.3491	1.2190	0.775	0.7302	0.2533	0.0644
0.0005	93.0	1209	0.2991	0.775	0.3492	1.2192	0.775	0.7302	0.2602	0.0645
0.0005	94.0	1222	0.2991	0.775	0.3492	1.2193	0.775	0.7302	0.2533	0.0645
0.0005	95.0	1235	0.2991	0.775	0.3491	1.2192	0.775	0.7302	0.2533	0.0644
0.0005	96.0	1248	0.2991	0.775	0.3491	1.2196	0.775	0.7302	0.2668	0.0644
0.0005	97.0	1261	0.2991	0.775	0.3492	1.2196	0.775	0.7302	0.2602	0.0644
0.0005	98.0	1274	0.2991	0.775	0.3491	1.2194	0.775	0.7302	0.2533	0.0644
0.0005	99.0	1287	0.2991	0.775	0.3491	1.2195	0.775	0.7302	0.2602	0.0644
0.0005	100.0	1300	0.2991	0.775	0.3491	1.2196	0.775	0.7302	0.2602	0.0644

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2

jordyvl/225-tiny_tobacco3482_kd

225-tiny_tobacco3482_kd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions