300-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: 0.3298
Accuracy: 0.79
Brier Loss: 0.3334
Nll: 1.0051
F1 Micro: 0.79
F1 Macro: 0.7591
Ece: 0.2152
Aurc: 0.0601

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	1.7587	0.225	0.8896	7.6699	0.225	0.1499	0.2894	0.7588
No log	2.0	26	1.1905	0.4	0.7901	3.7933	0.4000	0.2961	0.3234	0.4395
No log	3.0	39	0.9530	0.53	0.6670	2.9907	0.53	0.4066	0.3137	0.2821
No log	4.0	52	0.8046	0.615	0.5821	2.2124	0.615	0.4898	0.3097	0.1862
No log	5.0	65	0.7084	0.685	0.5084	2.1522	0.685	0.6027	0.3191	0.1389
No log	6.0	78	0.7243	0.68	0.4683	2.2673	0.68	0.5904	0.2695	0.1279
No log	7.0	91	0.6734	0.67	0.4675	2.2909	0.67	0.5844	0.2436	0.1510
No log	8.0	104	0.5780	0.7	0.4215	2.0061	0.7	0.6160	0.2418	0.1016
No log	9.0	117	0.6270	0.71	0.4402	1.8620	0.7100	0.6574	0.2485	0.1249
No log	10.0	130	0.5604	0.72	0.4074	1.5914	0.72	0.6430	0.2566	0.0935
No log	11.0	143	0.5814	0.705	0.4079	1.6933	0.705	0.6190	0.2350	0.1035
No log	12.0	156	0.5901	0.71	0.4176	1.7974	0.7100	0.6472	0.2225	0.1058
No log	13.0	169	0.5041	0.71	0.3918	1.8429	0.7100	0.6562	0.2336	0.0958
No log	14.0	182	0.5099	0.72	0.3982	1.6343	0.72	0.6550	0.2202	0.1021
No log	15.0	195	0.4843	0.745	0.3951	1.3599	0.745	0.6719	0.2680	0.0884
No log	16.0	208	0.4529	0.74	0.3776	1.3838	0.74	0.6951	0.2112	0.0839
No log	17.0	221	0.4420	0.745	0.3782	1.4403	0.745	0.6982	0.2285	0.0800
No log	18.0	234	0.4428	0.755	0.3710	1.3696	0.755	0.7298	0.2170	0.0825
No log	19.0	247	0.4306	0.75	0.3794	1.4095	0.75	0.7235	0.2470	0.0862
No log	20.0	260	0.4166	0.74	0.3648	1.2893	0.74	0.6776	0.2312	0.0835
No log	21.0	273	0.3830	0.77	0.3524	1.1764	0.7700	0.7256	0.2535	0.0730
No log	22.0	286	0.3918	0.77	0.3564	1.2293	0.7700	0.7067	0.2372	0.0710
No log	23.0	299	0.4125	0.75	0.3656	1.1419	0.75	0.7109	0.2357	0.0766
No log	24.0	312	0.3771	0.785	0.3543	1.0960	0.785	0.7583	0.2345	0.0712
No log	25.0	325	0.3846	0.745	0.3613	1.0616	0.745	0.7061	0.2060	0.0766
No log	26.0	338	0.3660	0.77	0.3547	1.3094	0.7700	0.7196	0.2515	0.0724
No log	27.0	351	0.3634	0.78	0.3476	1.0645	0.78	0.7479	0.2401	0.0677
No log	28.0	364	0.3715	0.755	0.3522	1.1981	0.755	0.6984	0.2257	0.0709
No log	29.0	377	0.3701	0.765	0.3597	1.1645	0.765	0.7239	0.2631	0.0747
No log	30.0	390	0.3562	0.775	0.3465	1.1094	0.775	0.7140	0.2428	0.0659
No log	31.0	403	0.3811	0.775	0.3499	1.2515	0.775	0.7368	0.2214	0.0694
No log	32.0	416	0.3555	0.77	0.3439	1.1715	0.7700	0.7053	0.2532	0.0705
No log	33.0	429	0.3592	0.775	0.3449	1.1606	0.775	0.7364	0.2336	0.0729
No log	34.0	442	0.3555	0.78	0.3431	1.1054	0.78	0.7373	0.2143	0.0653
No log	35.0	455	0.3454	0.77	0.3415	1.0386	0.7700	0.7333	0.2463	0.0668
No log	36.0	468	0.3403	0.8	0.3394	1.1435	0.8000	0.7664	0.2674	0.0625
No log	37.0	481	0.3390	0.785	0.3379	1.1183	0.785	0.7552	0.2432	0.0633
No log	38.0	494	0.3413	0.79	0.3347	1.1538	0.79	0.7406	0.2239	0.0615
0.2994	39.0	507	0.3364	0.795	0.3362	0.9975	0.795	0.7650	0.2334	0.0639
0.2994	40.0	520	0.3340	0.79	0.3328	1.0045	0.79	0.7466	0.2711	0.0580
0.2994	41.0	533	0.3381	0.77	0.3391	0.9829	0.7700	0.7427	0.2147	0.0675
0.2994	42.0	546	0.3297	0.8	0.3319	1.0739	0.8000	0.7685	0.2613	0.0585
0.2994	43.0	559	0.3338	0.8	0.3373	1.1507	0.8000	0.7719	0.2491	0.0637
0.2994	44.0	572	0.3316	0.79	0.3359	1.1274	0.79	0.7539	0.2469	0.0620
0.2994	45.0	585	0.3283	0.79	0.3336	1.0644	0.79	0.7531	0.2636	0.0612
0.2994	46.0	598	0.3297	0.8	0.3344	1.1343	0.8000	0.7670	0.2317	0.0600
0.2994	47.0	611	0.3293	0.79	0.3318	1.0692	0.79	0.7542	0.2396	0.0616
0.2994	48.0	624	0.3339	0.79	0.3357	1.1225	0.79	0.7590	0.2508	0.0617
0.2994	49.0	637	0.3290	0.795	0.3343	1.0692	0.795	0.7618	0.2529	0.0604
0.2994	50.0	650	0.3298	0.79	0.3348	1.1343	0.79	0.7591	0.2330	0.0609
0.2994	51.0	663	0.3305	0.795	0.3330	1.0045	0.795	0.7618	0.2357	0.0607
0.2994	52.0	676	0.3299	0.79	0.3339	1.0722	0.79	0.7542	0.2562	0.0614
0.2994	53.0	689	0.3280	0.8	0.3325	1.0688	0.8000	0.7685	0.2500	0.0593
0.2994	54.0	702	0.3284	0.795	0.3323	1.0175	0.795	0.7618	0.2436	0.0598
0.2994	55.0	715	0.3287	0.79	0.3331	1.0750	0.79	0.7591	0.2497	0.0604
0.2994	56.0	728	0.3286	0.795	0.3335	1.0115	0.795	0.7618	0.2296	0.0602
0.2994	57.0	741	0.3285	0.79	0.3330	1.0648	0.79	0.7591	0.2446	0.0602
0.2994	58.0	754	0.3299	0.795	0.3339	1.0193	0.795	0.7618	0.2345	0.0608
0.2994	59.0	767	0.3294	0.79	0.3329	1.0139	0.79	0.7591	0.2369	0.0601
0.2994	60.0	780	0.3292	0.795	0.3332	1.0118	0.795	0.7618	0.2226	0.0601
0.2994	61.0	793	0.3293	0.795	0.3333	1.0716	0.795	0.7618	0.2282	0.0602
0.2994	62.0	806	0.3294	0.795	0.3331	1.0107	0.795	0.7618	0.2224	0.0601
0.2994	63.0	819	0.3295	0.795	0.3336	1.0144	0.795	0.7618	0.2294	0.0605
0.2994	64.0	832	0.3293	0.795	0.3332	1.0104	0.795	0.7618	0.2324	0.0603
0.2994	65.0	845	0.3298	0.795	0.3337	1.0114	0.795	0.7618	0.2478	0.0606
0.2994	66.0	858	0.3297	0.795	0.3333	1.0076	0.795	0.7618	0.2366	0.0601
0.2994	67.0	871	0.3298	0.79	0.3338	1.0120	0.79	0.7591	0.2513	0.0606
0.2994	68.0	884	0.3297	0.795	0.3337	1.0110	0.795	0.7618	0.2376	0.0605
0.2994	69.0	897	0.3297	0.795	0.3335	1.0115	0.795	0.7618	0.2228	0.0602
0.2994	70.0	910	0.3292	0.795	0.3333	1.0089	0.795	0.7618	0.2215	0.0602
0.2994	71.0	923	0.3297	0.795	0.3334	1.0083	0.795	0.7618	0.2226	0.0600
0.2994	72.0	936	0.3297	0.79	0.3335	1.0072	0.79	0.7591	0.2257	0.0604
0.2994	73.0	949	0.3297	0.795	0.3332	1.0060	0.795	0.7618	0.2381	0.0600
0.2994	74.0	962	0.3295	0.795	0.3335	1.0082	0.795	0.7618	0.2366	0.0603
0.2994	75.0	975	0.3296	0.79	0.3334	1.0089	0.79	0.7591	0.2373	0.0601
0.2994	76.0	988	0.3298	0.795	0.3334	1.0098	0.795	0.7618	0.2310	0.0602
0.0006	77.0	1001	0.3297	0.79	0.3334	1.0084	0.79	0.7591	0.2228	0.0603
0.0006	78.0	1014	0.3297	0.79	0.3333	1.0071	0.79	0.7591	0.2148	0.0600
0.0006	79.0	1027	0.3298	0.795	0.3334	1.0059	0.795	0.7618	0.2309	0.0602
0.0006	80.0	1040	0.3298	0.795	0.3334	1.0046	0.795	0.7618	0.2309	0.0602
0.0006	81.0	1053	0.3298	0.79	0.3335	1.0073	0.79	0.7591	0.2239	0.0602
0.0006	82.0	1066	0.3298	0.795	0.3336	1.0072	0.795	0.7618	0.2317	0.0603
0.0006	83.0	1079	0.3297	0.795	0.3334	1.0055	0.795	0.7618	0.2224	0.0601
0.0006	84.0	1092	0.3298	0.79	0.3335	1.0061	0.79	0.7591	0.2240	0.0601
0.0006	85.0	1105	0.3297	0.79	0.3334	1.0052	0.79	0.7591	0.2322	0.0601
0.0006	86.0	1118	0.3298	0.79	0.3335	1.0059	0.79	0.7591	0.2323	0.0602
0.0006	87.0	1131	0.3298	0.79	0.3335	1.0065	0.79	0.7591	0.2152	0.0602
0.0006	88.0	1144	0.3298	0.79	0.3335	1.0056	0.79	0.7591	0.2235	0.0603
0.0006	89.0	1157	0.3297	0.79	0.3334	1.0050	0.79	0.7591	0.2152	0.0602
0.0006	90.0	1170	0.3297	0.79	0.3334	1.0049	0.79	0.7591	0.2153	0.0602
0.0006	91.0	1183	0.3297	0.79	0.3334	1.0059	0.79	0.7591	0.2234	0.0601
0.0006	92.0	1196	0.3298	0.79	0.3334	1.0049	0.79	0.7591	0.2152	0.0602
0.0006	93.0	1209	0.3299	0.79	0.3335	1.0056	0.79	0.7591	0.2152	0.0601
0.0006	94.0	1222	0.3298	0.79	0.3335	1.0049	0.79	0.7591	0.2152	0.0602
0.0006	95.0	1235	0.3298	0.79	0.3334	1.0048	0.79	0.7591	0.2152	0.0602
0.0006	96.0	1248	0.3298	0.79	0.3334	1.0050	0.79	0.7591	0.2152	0.0601
0.0006	97.0	1261	0.3298	0.79	0.3335	1.0053	0.79	0.7591	0.2152	0.0602
0.0006	98.0	1274	0.3298	0.79	0.3334	1.0051	0.79	0.7591	0.2152	0.0602
0.0006	99.0	1287	0.3298	0.79	0.3334	1.0052	0.79	0.7591	0.2152	0.0601
0.0006	100.0	1300	0.3298	0.79	0.3334	1.0051	0.79	0.7591	0.2152	0.0601

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2

jordyvl/300-tiny_tobacco3482_kd

300-tiny_tobacco3482_kd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions