dit-base_tobacco-tiny_tobacco3482_og_simkd

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: 318.4368
Accuracy: 0.805
Brier Loss: 0.3825
Nll: 1.1523
F1 Micro: 0.805
F1 Macro: 0.7673
Ece: 0.2987
Aurc: 0.0702

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	328.9614	0.155	0.8984	7.4608	0.155	0.0353	0.2035	0.8760
No log	2.0	14	328.8199	0.235	0.8940	6.4907	0.235	0.1148	0.2643	0.7444
No log	3.0	21	328.4224	0.38	0.8711	2.8184	0.38	0.3279	0.3440	0.4817
No log	4.0	28	327.5357	0.51	0.8072	2.0744	0.51	0.4221	0.4111	0.3319
No log	5.0	35	326.2037	0.53	0.6860	2.0669	0.53	0.4313	0.3619	0.2744
No log	6.0	42	324.8763	0.565	0.6008	1.9437	0.565	0.4477	0.3009	0.2469
No log	7.0	49	323.9205	0.6	0.5390	1.7694	0.6	0.4647	0.2365	0.1978
No log	8.0	56	323.2227	0.65	0.4632	1.7803	0.65	0.5195	0.2313	0.1422
No log	9.0	63	322.5265	0.74	0.4177	1.7538	0.74	0.6302	0.2442	0.1113
No log	10.0	70	322.1928	0.705	0.4013	1.5880	0.705	0.5864	0.2147	0.1118
No log	11.0	77	322.2687	0.795	0.4006	1.2854	0.795	0.7476	0.2719	0.0942
No log	12.0	84	321.6652	0.725	0.3754	1.3462	0.7250	0.6521	0.2238	0.0920
No log	13.0	91	322.3688	0.785	0.3951	1.3209	0.785	0.7260	0.2712	0.0805
No log	14.0	98	321.7083	0.72	0.3915	1.4854	0.72	0.6220	0.1963	0.0986
No log	15.0	105	321.6171	0.8	0.3614	1.3397	0.8000	0.7427	0.2531	0.0741
No log	16.0	112	321.0427	0.77	0.3502	1.1461	0.7700	0.7082	0.1976	0.0769
No log	17.0	119	321.1529	0.735	0.3827	1.5751	0.735	0.6769	0.1926	0.0973
No log	18.0	126	321.0808	0.78	0.3611	1.2529	0.78	0.7199	0.2242	0.0762
No log	19.0	133	321.6684	0.795	0.3835	1.1789	0.795	0.7506	0.2823	0.0712
No log	20.0	140	321.2322	0.78	0.3682	1.1715	0.78	0.7356	0.2532	0.0752
No log	21.0	147	320.4927	0.795	0.3458	1.3764	0.795	0.7504	0.2178	0.0710
No log	22.0	154	320.8896	0.8	0.3568	1.0908	0.8000	0.7536	0.2709	0.0677
No log	23.0	161	320.9060	0.785	0.3774	1.1571	0.785	0.7414	0.2712	0.0719
No log	24.0	168	320.9026	0.795	0.3718	1.0871	0.795	0.7465	0.2718	0.0690
No log	25.0	175	320.7932	0.805	0.3601	1.0998	0.805	0.7699	0.2620	0.0614
No log	26.0	182	321.2285	0.735	0.4164	1.8530	0.735	0.7051	0.2814	0.0889
No log	27.0	189	320.8364	0.775	0.4028	1.4063	0.775	0.7412	0.2687	0.0836
No log	28.0	196	320.0800	0.785	0.3548	1.2123	0.785	0.7394	0.2055	0.0740
No log	29.0	203	319.9995	0.79	0.3526	1.2296	0.79	0.7381	0.2363	0.0691
No log	30.0	210	320.0685	0.795	0.3588	1.2765	0.795	0.7447	0.2310	0.0725
No log	31.0	217	320.0981	0.805	0.3699	1.0128	0.805	0.7690	0.2868	0.0701
No log	32.0	224	320.5063	0.8	0.3900	1.1437	0.8000	0.7650	0.3141	0.0679
No log	33.0	231	319.8609	0.795	0.3549	1.2051	0.795	0.7526	0.2485	0.0697
No log	34.0	238	319.6974	0.81	0.3600	1.0124	0.81	0.7724	0.2671	0.0672
No log	35.0	245	319.5988	0.795	0.3513	1.1480	0.795	0.7540	0.2425	0.0679
No log	36.0	252	319.6317	0.8	0.3544	1.2190	0.8000	0.7607	0.2449	0.0674
No log	37.0	259	319.6821	0.81	0.3531	1.0714	0.81	0.7672	0.2590	0.0662
No log	38.0	266	319.7618	0.805	0.3754	1.0421	0.805	0.7625	0.2973	0.0701
No log	39.0	273	319.9920	0.775	0.3843	1.0821	0.775	0.7374	0.2801	0.0723
No log	40.0	280	319.3407	0.765	0.3633	1.2213	0.765	0.7041	0.2274	0.0767
No log	41.0	287	319.2732	0.765	0.3696	1.2638	0.765	0.7184	0.2315	0.0835
No log	42.0	294	319.5948	0.805	0.3685	1.0782	0.805	0.7625	0.2678	0.0661
No log	43.0	301	319.7181	0.8	0.3776	1.0004	0.8000	0.7507	0.2598	0.0672
No log	44.0	308	319.1170	0.77	0.3619	1.2129	0.7700	0.7159	0.2557	0.0787
No log	45.0	315	319.5949	0.8	0.3809	1.1448	0.8000	0.7670	0.2868	0.0688
No log	46.0	322	319.0327	0.79	0.3675	1.2386	0.79	0.7315	0.2546	0.0790
No log	47.0	329	319.3806	0.805	0.3665	1.1368	0.805	0.7620	0.2737	0.0700
No log	48.0	336	319.4999	0.795	0.3836	1.0256	0.795	0.7550	0.2800	0.0748
No log	49.0	343	319.2553	0.8	0.3660	1.2011	0.8000	0.7573	0.2698	0.0679
No log	50.0	350	319.3495	0.805	0.3836	1.1055	0.805	0.7634	0.3004	0.0671
No log	51.0	357	319.1643	0.8	0.3660	1.1980	0.8000	0.7497	0.2641	0.0709
No log	52.0	364	319.1483	0.795	0.3651	1.0776	0.795	0.7561	0.2856	0.0683
No log	53.0	371	319.0104	0.79	0.3724	1.1653	0.79	0.7422	0.2512	0.0724
No log	54.0	378	319.1622	0.795	0.3814	1.2807	0.795	0.7456	0.2644	0.0759
No log	55.0	385	319.1554	0.8	0.3694	1.2710	0.8000	0.7570	0.2877	0.0667
No log	56.0	392	319.2158	0.79	0.3795	1.1678	0.79	0.7509	0.2942	0.0692
No log	57.0	399	319.1813	0.795	0.3839	1.1243	0.795	0.7529	0.2835	0.0733
No log	58.0	406	318.7599	0.81	0.3632	1.1484	0.81	0.7738	0.3030	0.0691
No log	59.0	413	319.0827	0.805	0.3792	1.2070	0.805	0.7685	0.2901	0.0674
No log	60.0	420	318.6928	0.805	0.3661	1.1517	0.805	0.7534	0.2492	0.0719
No log	61.0	427	318.8309	0.805	0.3714	1.2785	0.805	0.7517	0.2674	0.0699
No log	62.0	434	318.9468	0.8	0.3794	1.1549	0.8000	0.7566	0.2862	0.0707
No log	63.0	441	318.8059	0.785	0.3774	1.2460	0.785	0.7487	0.2721	0.0752
No log	64.0	448	318.7155	0.81	0.3659	1.1963	0.81	0.7660	0.2676	0.0680
No log	65.0	455	318.8439	0.795	0.3799	1.0230	0.795	0.7464	0.2797	0.0700
No log	66.0	462	318.7784	0.79	0.3783	1.3168	0.79	0.7503	0.2618	0.0804
No log	67.0	469	318.9019	0.795	0.3802	1.2003	0.795	0.7503	0.2934	0.0702
No log	68.0	476	318.6647	0.8	0.3728	1.1395	0.8000	0.7590	0.2718	0.0699
No log	69.0	483	318.3780	0.8	0.3688	1.2812	0.8000	0.7602	0.2690	0.0728
No log	70.0	490	318.8004	0.8	0.3779	1.0682	0.8000	0.7607	0.2887	0.0682
No log	71.0	497	318.7021	0.8	0.3748	1.1101	0.8000	0.7545	0.2977	0.0691
322.4844	72.0	504	318.3595	0.79	0.3779	1.2333	0.79	0.7386	0.2617	0.0843
322.4844	73.0	511	318.5725	0.805	0.3740	1.2108	0.805	0.7674	0.2762	0.0677
322.4844	74.0	518	318.7131	0.81	0.3822	1.2048	0.81	0.7660	0.2971	0.0696
322.4844	75.0	525	318.6258	0.775	0.3806	1.1511	0.775	0.7228	0.2824	0.0743
322.4844	76.0	532	318.5414	0.8	0.3746	1.2136	0.8000	0.7563	0.2872	0.0708
322.4844	77.0	539	318.5404	0.795	0.3765	1.1414	0.795	0.7551	0.2905	0.0707
322.4844	78.0	546	318.5820	0.8	0.3806	1.1653	0.8000	0.7573	0.2888	0.0707
322.4844	79.0	553	318.5909	0.8	0.3838	1.2343	0.8000	0.7563	0.2778	0.0754
322.4844	80.0	560	318.6398	0.795	0.3874	1.1097	0.795	0.7520	0.3045	0.0727
322.4844	81.0	567	318.6250	0.795	0.3860	1.1612	0.795	0.7542	0.3079	0.0727
322.4844	82.0	574	318.5269	0.795	0.3825	1.2812	0.795	0.7451	0.2723	0.0737
322.4844	83.0	581	318.5790	0.795	0.3846	1.1575	0.795	0.7455	0.2984	0.0723
322.4844	84.0	588	318.4343	0.795	0.3826	1.2088	0.795	0.7532	0.2852	0.0746
322.4844	85.0	595	318.3853	0.795	0.3792	1.2784	0.795	0.7456	0.3003	0.0729
322.4844	86.0	602	318.5143	0.805	0.3854	1.1745	0.805	0.7636	0.3071	0.0705
322.4844	87.0	609	318.3533	0.805	0.3763	1.1579	0.805	0.7679	0.2805	0.0694
322.4844	88.0	616	318.4745	0.795	0.3860	1.0964	0.795	0.7539	0.2952	0.0712
322.4844	89.0	623	318.4909	0.805	0.3829	1.1544	0.805	0.7673	0.3035	0.0700
322.4844	90.0	630	318.4910	0.8	0.3828	1.1537	0.8000	0.7497	0.2730	0.0717
322.4844	91.0	637	318.5176	0.8	0.3855	1.1613	0.8000	0.7552	0.2815	0.0718
322.4844	92.0	644	318.4100	0.795	0.3810	1.2215	0.795	0.7532	0.2696	0.0731
322.4844	93.0	651	318.3500	0.805	0.3765	1.2181	0.805	0.7702	0.2790	0.0705
322.4844	94.0	658	318.3257	0.805	0.3785	1.2218	0.805	0.7678	0.3114	0.0704
322.4844	95.0	665	318.3990	0.8	0.3823	1.1485	0.8000	0.7585	0.2901	0.0710
322.4844	96.0	672	318.5006	0.81	0.3862	1.1518	0.81	0.7724	0.2925	0.0698
322.4844	97.0	679	318.3142	0.8	0.3780	1.1608	0.8000	0.7557	0.2916	0.0716
322.4844	98.0	686	318.3767	0.795	0.3819	1.2208	0.795	0.7526	0.2764	0.0731
322.4844	99.0	693	318.4233	0.8	0.3810	1.1532	0.8000	0.7557	0.2786	0.0706
322.4844	100.0	700	318.4368	0.805	0.3825	1.1523	0.805	0.7673	0.2987	0.0702

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_og_simkd

dit-base_tobacco-tiny_tobacco3482_og_simkd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions