This model is a fine-tuned version of timm/mobilenetv3_large_100.miil_in21k on the davanstrien/zenodo-presentations-open-labels dataset. It achieves the following results on the evaluation set:

Loss: 0.4897
Accuracy: 0.7913

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: 2e-05
train_batch_size: 128
eval_batch_size: 128
seed: 1337
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 200.0

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.6794	1.0	23	0.6063	0.6560
0.6215	2.0	46	0.7362	0.5833
0.5784	3.0	69	0.7598	0.5490
0.5347	4.0	92	0.7638	0.5306
0.5307	5.0	115	0.7638	0.5235
0.5391	6.0	138	0.7677	0.5090
0.48	7.0	161	0.7717	0.5108
0.473	8.0	184	0.7756	0.5028
0.5014	9.0	207	0.7717	0.5054
0.496	10.0	230	0.7717	0.5040
0.4688	11.0	253	0.7677	0.4972
0.4943	12.0	276	0.7638	0.4977
0.5012	13.0	299	0.7717	0.5057
0.4639	14.0	322	0.7717	0.5010
0.4709	15.0	345	0.7795	0.4949
0.4888	16.0	368	0.7835	0.4955
0.4594	17.0	391	0.7717	0.4986
0.4745	18.0	414	0.7677	0.5011
0.4667	19.0	437	0.7756	0.4928
0.4551	20.0	460	0.7795	0.5055
0.4657	21.0	483	0.7756	0.4928
0.4818	22.0	506	0.7756	0.5002
0.4633	23.0	529	0.7835	0.4946
0.4779	24.0	552	0.7795	0.4942
0.4718	25.0	575	0.7835	0.4963
0.4511	26.0	598	0.7717	0.5011
0.4798	27.0	621	0.7874	0.4904
0.4868	28.0	644	0.7835	0.4982
0.4653	29.0	667	0.7874	0.4988
0.4613	30.0	690	0.7795	0.4985
0.4675	31.0	713	0.7717	0.5060
0.4587	32.0	736	0.7717	0.5059
0.464	33.0	759	0.7795	0.5042
0.4374	34.0	782	0.7677	0.5063
0.4864	35.0	805	0.7677	0.5040
0.4354	36.0	828	0.7717	0.5109
0.4655	37.0	851	0.7717	0.5107
0.4691	38.0	874	0.7677	0.5093
0.4826	39.0	897	0.7717	0.5044
0.4577	40.0	920	0.7795	0.5000
0.4636	41.0	943	0.7717	0.4963
0.4361	42.0	966	0.7717	0.4958
0.4534	43.0	989	0.7795	0.5008
0.4559	44.0	1012	0.7795	0.5025
0.4189	45.0	1035	0.7756	0.5014
0.4861	46.0	1058	0.7677	0.5004
0.4709	47.0	1081	0.7795	0.5005
0.4726	48.0	1104	0.7717	0.5008
0.4441	49.0	1127	0.7756	0.4988
0.4579	50.0	1150	0.7756	0.5000
0.4366	51.0	1173	0.4980	0.7756
0.4467	52.0	1196	0.4947	0.7795
0.4797	53.0	1219	0.4950	0.7756
0.4544	54.0	1242	0.4998	0.7717
0.4466	55.0	1265	0.4980	0.7795
0.4599	56.0	1288	0.4963	0.7835
0.4458	57.0	1311	0.4956	0.7874
0.4296	58.0	1334	0.4994	0.7874
0.4415	59.0	1357	0.4998	0.7835
0.4036	60.0	1380	0.4996	0.7795
0.4406	61.0	1403	0.5022	0.7913
0.4235	62.0	1426	0.5018	0.7913
0.4492	63.0	1449	0.4964	0.8031
0.4065	64.0	1472	0.4953	0.7874
0.4474	65.0	1495	0.4897	0.7913
0.4605	66.0	1518	0.5039	0.7795
0.436	67.0	1541	0.5024	0.7756
0.4746	68.0	1564	0.5007	0.7874
0.4555	69.0	1587	0.5054	0.7874
0.433	70.0	1610	0.4974	0.7874
0.4503	71.0	1633	0.5096	0.7795
0.4424	72.0	1656	0.5040	0.7756
0.4331	73.0	1679	0.5056	0.7913
0.4263	74.0	1702	0.5026	0.7874
0.4305	75.0	1725	0.5033	0.7835
0.4271	76.0	1748	0.5015	0.7874
0.4635	77.0	1771	0.4988	0.7913
0.4212	78.0	1794	0.4994	0.7913
0.4154	79.0	1817	0.5044	0.7874
0.4288	80.0	1840	0.5033	0.7913
0.4211	81.0	1863	0.5050	0.7835
0.4022	82.0	1886	0.5021	0.7835
0.4477	83.0	1909	0.5096	0.7756
0.4091	84.0	1932	0.5017	0.7913
0.4284	85.0	1955	0.5094	0.7795
0.4317	86.0	1978	0.5056	0.7874
0.4011	87.0	2001	0.4992	0.7953
0.4043	88.0	2024	0.5106	0.7874
0.4233	89.0	2047	0.5083	0.7835
0.4383	90.0	2070	0.5016	0.7913
0.4328	91.0	2093	0.5062	0.7874
0.3978	92.0	2116	0.5026	0.7874
0.4052	93.0	2139	0.4964	0.7913
0.3938	94.0	2162	0.5036	0.7874
0.393	95.0	2185	0.5102	0.7835
0.4294	96.0	2208	0.5003	0.7874
0.4122	97.0	2231	0.5013	0.7913
0.4207	98.0	2254	0.5076	0.7874
0.4127	99.0	2277	0.5040	0.7835
0.441	100.0	2300	0.5022	0.7835
0.3938	101.0	2323	0.4975	0.7992
0.4109	102.0	2346	0.5019	0.7913
0.4299	103.0	2369	0.5060	0.7874
0.4148	104.0	2392	0.5038	0.7874
0.4179	105.0	2415	0.5064	0.7835
0.4352	106.0	2438	0.5059	0.7874
0.4027	107.0	2461	0.5025	0.7953
0.4002	108.0	2484	0.5020	0.7874
0.3988	109.0	2507	0.5063	0.7874
0.4095	110.0	2530	0.5034	0.7913
0.4001	111.0	2553	0.5054	0.7874
0.4201	112.0	2576	0.5076	0.7992
0.4134	113.0	2599	0.5070	0.7953
0.3614	114.0	2622	0.5033	0.7835
0.3928	115.0	2645	0.5043	0.7874
0.435	116.0	2668	0.4999	0.7874
0.4162	117.0	2691	0.5132	0.7874
0.4078	118.0	2714	0.5088	0.7795
0.4025	119.0	2737	0.5075	0.7835
0.4096	120.0	2760	0.5023	0.7835
0.3879	121.0	2783	0.5063	0.7835
0.4033	122.0	2806	0.5001	0.7874
0.3927	123.0	2829	0.5087	0.7795
0.3803	124.0	2852	0.5150	0.7913
0.4248	125.0	2875	0.5150	0.7835
0.3874	126.0	2898	0.5158	0.7874
0.3646	127.0	2921	0.4980	0.8031
0.4115	128.0	2944	0.5077	0.7913
0.385	129.0	2967	0.5153	0.7913
0.4064	130.0	2990	0.5114	0.7953
0.4168	131.0	3013	0.5057	0.7992
0.4319	132.0	3036	0.5041	0.7953
0.4234	133.0	3059	0.5119	0.7992
0.3721	134.0	3082	0.5118	0.7874
0.3709	135.0	3105	0.5078	0.7913
0.4149	136.0	3128	0.5164	0.7795
0.416	137.0	3151	0.5123	0.7835
0.406	138.0	3174	0.5116	0.7913
0.3613	139.0	3197	0.5170	0.7913
0.3786	140.0	3220	0.5099	0.8031
0.3976	141.0	3243	0.5111	0.7913
0.371	142.0	3266	0.5081	0.7953
0.4056	143.0	3289	0.5098	0.7913
0.4214	144.0	3312	0.5085	0.7953
0.3832	145.0	3335	0.5084	0.7953
0.3762	146.0	3358	0.5061	0.7913
0.4118	147.0	3381	0.5111	0.7992
0.3866	148.0	3404	0.5092	0.8071
0.3869	149.0	3427	0.5122	0.7953
0.3734	150.0	3450	0.5117	0.7953
0.4061	151.0	3473	0.5095	0.7913
0.3705	152.0	3496	0.5171	0.7953
0.3873	153.0	3519	0.5179	0.7953
0.3927	154.0	3542	0.5117	0.7992
0.3807	155.0	3565	0.5133	0.7953
0.3761	156.0	3588	0.5140	0.7913
0.3964	157.0	3611	0.5118	0.7953
0.39	158.0	3634	0.5122	0.8031
0.3943	159.0	3657	0.5126	0.8031
0.3417	160.0	3680	0.5097	0.7992
0.3996	161.0	3703	0.5048	0.7913
0.4	162.0	3726	0.5148	0.7953
0.4051	163.0	3749	0.5150	0.7874
0.3973	164.0	3772	0.5037	0.8031
0.3963	165.0	3795	0.5048	0.7953
0.3568	166.0	3818	0.5168	0.7913
0.3995	167.0	3841	0.5096	0.7913
0.3628	168.0	3864	0.5102	0.7953
0.3836	169.0	3887	0.5133	0.7953
0.3646	170.0	3910	0.5099	0.8031
0.3789	171.0	3933	0.5151	0.7874
0.3832	172.0	3956	0.5149	0.8031
0.3476	173.0	3979	0.5178	0.7835
0.3806	174.0	4002	0.5081	0.7992
0.4053	175.0	4025	0.5100	0.7874
0.3986	176.0	4048	0.5189	0.7992
0.3827	177.0	4071	0.5129	0.7992
0.3892	178.0	4094	0.5099	0.7874
0.3955	179.0	4117	0.5212	0.7992
0.4077	180.0	4140	0.5102	0.7953
0.3579	181.0	4163	0.5100	0.7953
0.3666	182.0	4186	0.5248	0.7835
0.3746	183.0	4209	0.5220	0.7874
0.3867	184.0	4232	0.5173	0.7913
0.4024	185.0	4255	0.5248	0.7874
0.4014	186.0	4278	0.5085	0.7913
0.3445	187.0	4301	0.5137	0.8031
0.382	188.0	4324	0.5213	0.7913
0.3673	189.0	4347	0.5242	0.7913
0.3631	190.0	4370	0.5146	0.7913
0.393	191.0	4393	0.5098	0.7835
0.3806	192.0	4416	0.5134	0.7992
0.3789	193.0	4439	0.5127	0.7992
0.3717	194.0	4462	0.5184	0.7913
0.361	195.0	4485	0.5186	0.7835
0.3722	196.0	4508	0.5107	0.7953
0.3551	197.0	4531	0.5175	0.7953
0.3649	198.0	4554	0.5136	0.7992
0.3749	199.0	4577	0.5193	0.7913
0.3782	200.0	4600	0.5182	0.7992

Framework versions

Transformers 4.46.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1

davanstrien/test-timm

test-timm

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

davanstrien/
test-timm