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unreal-hug/
segformer-b3-finetuned-segments-outputs

Name: segformer-b3-finetuned-segments-outputs
Availability: InStock

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segformer-b3-finetuned-segments-outputs

This model is a fine-tuned version of nvidia/mit-b3 on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset. It achieves the following results on the evaluation set:

Loss: 0.3002
Mean Iou: 0.2829
Mean Accuracy: 0.3326
Overall Accuracy: 0.6026
Accuracy Unlabeled: nan
Accuracy Lv: 0.7852
Accuracy Rv: 0.5699
Accuracy Ra: 0.5380
Accuracy La: 0.6208
Accuracy Vs: 0.0
Accuracy As: 0.0
Accuracy Mk: 0.0004
Accuracy Tk: nan
Accuracy Asd: 0.1783
Accuracy Vsd: 0.1873
Accuracy Ak: 0.4458
Iou Unlabeled: 0.0
Iou Lv: 0.7310
Iou Rv: 0.5182
Iou Ra: 0.5178
Iou La: 0.5526
Iou Vs: 0.0
Iou As: 0.0
Iou Mk: 0.0004
Iou Tk: nan
Iou Asd: 0.1728
Iou Vsd: 0.1827
Iou Ak: 0.4361

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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Lv	Accuracy Rv	Accuracy Ra	Accuracy La	Accuracy Vs	Accuracy Mk	Accuracy Tk	Accuracy Asd	Accuracy Vsd	Accuracy Ak	Iou Lv	Iou Rv	Iou Ra	Iou La	Iou Vs	Iou Mk	Iou Tk	Iou Asd	Iou Vsd	Iou Ak
0.4253	0.62	100	0.4599	0.1588	0.2152	0.4965	nan	0.8879	0.0778	0.1004	0.5637	0.0	0.0	nan	0.0120	0.0509	0.4590	0.6799	0.0770	0.0985	0.3899	0.0	0.0	nan	0.0120	0.0509	0.4386
0.3839	1.25	200	0.3598	0.2325	0.2929	0.5740	nan	0.8720	0.4761	0.6272	0.2194	0.0	0.0	nan	0.0102	0.2038	0.5201	0.8020	0.4259	0.4142	0.2085	0.0	0.0	nan	0.0102	0.1964	0.4999
0.4634	1.88	300	0.3361	0.3031	0.3870	0.6197	nan	0.7362	0.7347	0.2986	0.7550	0.0	0.0	nan	0.3070	0.4629	0.5752	0.6984	0.5947	0.2894	0.5089	0.0	0.0	nan	0.2756	0.4265	0.5410
0.147	2.5	400	0.3123	0.3081	0.3772	0.5772	nan	0.6525	0.4740	0.6282	0.5966	0.0	0.0002	nan	0.2846	0.5934	0.5425	0.6202	0.4429	0.5296	0.5133	0.0	0.0002	nan	0.2597	0.5196	0.5033
0.2044	3.12	500	0.3104	0.2918	0.3459	0.5719	nan	0.7327	0.5989	0.5243	0.4087	0.0	0.0046	nan	0.0585	0.5632	0.5678	0.6887	0.5466	0.4931	0.3770	0.0	0.0045	nan	0.0583	0.4945	0.5471
0.3223	3.75	600	0.3078	0.3341	0.4038	0.6417	nan	0.6870	0.5831	0.7323	0.7609	0.0019	0.0267	nan	0.2290	0.4286	0.5887	0.6482	0.5377	0.6608	0.6435	0.0019	0.0255	nan	0.2199	0.3893	0.5488
0.275	4.38	700	0.3081	0.3007	0.3562	0.5801	nan	0.7267	0.3140	0.5325	0.6536	0.0024	0.0	nan	0.2228	0.5105	0.5992	0.6833	0.2982	0.5065	0.5827	0.0024	0.0	nan	0.2110	0.4492	0.5741
0.2679	5.0	800	0.3002	0.2829	0.3326	0.6026	nan	0.7852	0.5699	0.5380	0.6208	0.0	0.0004	nan	0.1783	0.1873	0.4458	0.7310	0.5182	0.5178	0.5526	0.0	0.0004	nan	0.1728	0.1827	0.4361
0.3721	5.62	900	0.3100	0.3449	0.4111	0.6774	nan	0.8066	0.6839	0.6907	0.6722	0.0004	0.0002	nan	0.2097	0.5078	0.5401	0.7558	0.6115	0.6389	0.6063	0.0004	0.0002	nan	0.2043	0.4613	0.5147
0.2418	6.25	1000	0.3161	0.3769	0.4608	0.7076	nan	0.7978	0.6939	0.6991	0.7553	0.1402	0.0	nan	0.2148	0.6464	0.6604	0.7465	0.6110	0.6455	0.6508	0.1308	0.0	nan	0.2046	0.5357	0.6210
0.5517	6.88	1100	0.3622	0.1738	0.2011	0.3603	nan	0.5002	0.2451	0.4020	0.3224	0.0287	0.0143	nan	0.1725	0.1302	0.1956	0.4829	0.2368	0.3610	0.3027	0.0279	0.0139	nan	0.1660	0.1262	0.1944
0.2611	7.5	1200	0.3240	0.3572	0.4346	0.6530	nan	0.7703	0.6570	0.6721	0.5853	0.1717	0.0561	nan	0.3176	0.5672	0.5490	0.7190	0.5879	0.5838	0.5265	0.1576	0.0520	nan	0.2832	0.4852	0.5341
0.2422	8.12	1300	0.3206	0.3382	0.4095	0.6283	nan	0.7598	0.5413	0.6799	0.5747	0.1393	0.1071	nan	0.2918	0.4583	0.5432	0.7139	0.4894	0.5792	0.5128	0.1306	0.0900	nan	0.2601	0.4134	0.5313
0.2	8.75	1400	0.3110	0.3299	0.3976	0.5977	nan	0.6984	0.4791	0.6668	0.6132	0.2240	0.0000	nan	0.3035	0.4994	0.4917	0.6626	0.4281	0.5904	0.5516	0.2026	0.0000	nan	0.2767	0.4409	0.4754
0.1095	9.38	1500	0.3375	0.2732	0.3235	0.5205	nan	0.5957	0.4483	0.5939	0.5724	0.1094	0.0005	nan	0.2502	0.2124	0.4518	0.5689	0.4004	0.5432	0.5122	0.1038	0.0005	nan	0.2293	0.2068	0.4398
0.2373	10.0	1600	0.3453	0.3066	0.3658	0.5723	nan	0.6940	0.5507	0.5989	0.5415	0.2547	0.0	nan	0.1549	0.4018	0.4611	0.6560	0.5007	0.5402	0.4888	0.2231	0.0	nan	0.1519	0.3680	0.4442
0.0756	10.62	1700	0.3413	0.3699	0.4457	0.6868	nan	0.7934	0.6758	0.6577	0.7146	0.2091	0.0075	nan	0.2043	0.5427	0.6520	0.7465	0.6060	0.6120	0.6207	0.1863	0.0071	nan	0.1908	0.4923	0.6071
0.1072	11.25	1800	0.3736	0.2889	0.3434	0.5518	nan	0.6798	0.5118	0.6135	0.5297	0.1772	0.0195	nan	0.1954	0.3432	0.3636	0.6444	0.4854	0.5561	0.4786	0.1539	0.0183	nan	0.1788	0.3106	0.3523
0.1216	11.88	1900	0.3648	0.3248	0.3879	0.6056	nan	0.7039	0.5606	0.6138	0.6566	0.1644	0.0080	nan	0.2637	0.3991	0.5087	0.6665	0.5153	0.5725	0.5704	0.1453	0.0074	nan	0.2402	0.3677	0.4877
0.1401	12.5	2000	0.3436	0.3537	0.4292	0.6524	nan	0.7521	0.6339	0.6030	0.7209	0.1334	0.0988	nan	0.3603	0.4304	0.5592	0.7059	0.5504	0.5546	0.6319	0.1235	0.0846	nan	0.3103	0.3901	0.5391
0.1436	13.12	2100	0.3869	0.3156	0.3744	0.5828	nan	0.7025	0.4233	0.5510	0.6780	0.1886	0.0510	nan	0.2666	0.3543	0.5291	0.6640	0.3923	0.5214	0.5994	0.1688	0.0440	nan	0.2386	0.3359	0.5075
0.0907	13.75	2200	0.3739	0.3237	0.3853	0.6046	nan	0.7534	0.5218	0.6138	0.5515	0.2576	0.0377	nan	0.2211	0.3392	0.5574	0.7090	0.4937	0.5742	0.4980	0.2077	0.0343	nan	0.2079	0.3158	0.5206
0.147	14.38	2300	0.3751	0.3667	0.4460	0.6265	nan	0.6614	0.6418	0.5923	0.7208	0.2728	0.0884	nan	0.2801	0.5884	0.6142	0.6267	0.5779	0.5584	0.6181	0.2302	0.0739	nan	0.2553	0.5113	0.5816
0.0612	15.0	2400	0.3993	0.3152	0.3777	0.5802	nan	0.6818	0.5538	0.6054	0.5973	0.1225	0.0486	nan	0.3157	0.4393	0.4124	0.6439	0.5163	0.5435	0.5331	0.1134	0.0438	nan	0.2698	0.3983	0.4056
0.0854	15.62	2500	0.4168	0.3039	0.3621	0.5569	nan	0.6689	0.4421	0.5384	0.5871	0.1719	0.0233	nan	0.2696	0.3985	0.5212	0.6317	0.4223	0.4957	0.5199	0.1508	0.0213	nan	0.2457	0.3702	0.4855
0.0806	16.25	2600	0.4017	0.3460	0.4169	0.6201	nan	0.7083	0.6388	0.6258	0.5904	0.1749	0.1096	nan	0.2301	0.4998	0.5915	0.6659	0.5768	0.5736	0.5358	0.1552	0.0903	nan	0.2099	0.4466	0.5522
0.137	16.88	2700	0.4268	0.2834	0.3348	0.5474	nan	0.6984	0.4311	0.5213	0.5677	0.0688	0.0219	nan	0.1758	0.4672	0.3961	0.6564	0.4077	0.4842	0.5061	0.0638	0.0208	nan	0.1653	0.4276	0.3855
0.0375	17.5	2800	0.4117	0.2816	0.3339	0.5291	nan	0.6131	0.4906	0.6136	0.5158	0.0881	0.0292	nan	0.2010	0.3391	0.4484	0.5803	0.4398	0.5575	0.4677	0.0809	0.0272	nan	0.1899	0.3179	0.4369
0.0654	18.12	2900	0.4334	0.3470	0.4190	0.6392	nan	0.7536	0.6040	0.6625	0.6205	0.1722	0.1006	nan	0.3133	0.4067	0.5566	0.7052	0.5599	0.5923	0.5496	0.1515	0.0809	nan	0.2670	0.3782	0.5320
0.0759	18.75	3000	0.4226	0.3140	0.3770	0.5661	nan	0.6390	0.5546	0.6119	0.5289	0.1559	0.0158	nan	0.2478	0.4546	0.5611	0.6063	0.5000	0.5449	0.4774	0.1370	0.0148	nan	0.2238	0.4182	0.5319
0.1047	19.38	3100	0.4350	0.3058	0.3639	0.5608	nan	0.6803	0.5207	0.5750	0.5243	0.1947	0.0335	nan	0.2706	0.3744	0.4656	0.6424	0.4914	0.5193	0.4712	0.1700	0.0307	nan	0.2409	0.3498	0.4479
0.146	20.0	3200	0.4320	0.3138	0.3796	0.5634	nan	0.6526	0.4673	0.5958	0.5859	0.2021	0.0287	nan	0.2886	0.4822	0.4933	0.6188	0.4135	0.5438	0.5232	0.1710	0.0244	nan	0.2502	0.4367	0.4706
0.1012	20.62	3300	0.4231	0.3294	0.3967	0.5944	nan	0.6824	0.5358	0.6163	0.5851	0.1819	0.0243	nan	0.3027	0.4514	0.5866	0.6449	0.4899	0.5645	0.5214	0.1589	0.0213	nan	0.2605	0.4193	0.5423
0.1004	21.25	3400	0.4312	0.3369	0.4078	0.6181	nan	0.7167	0.5900	0.6539	0.5973	0.1753	0.0330	nan	0.2538	0.5161	0.5419	0.6767	0.5234	0.5867	0.5300	0.1515	0.0276	nan	0.2273	0.4649	0.5176
0.0837	21.88	3500	0.4385	0.3202	0.3844	0.5932	nan	0.6960	0.5322	0.6045	0.5847	0.1779	0.0238	nan	0.2458	0.3876	0.5910	0.6549	0.4828	0.5517	0.5181	0.1554	0.0210	nan	0.2195	0.3639	0.5549
0.1212	22.5	3600	0.4473	0.3209	0.3857	0.5969	nan	0.7202	0.5315	0.5947	0.5830	0.1908	0.0382	nan	0.2426	0.4183	0.5379	0.6752	0.4757	0.5356	0.5134	0.1673	0.0335	nan	0.2203	0.3885	0.5200
0.0698	23.12	3700	0.4587	0.3033	0.3629	0.5581	nan	0.6604	0.5113	0.5777	0.5497	0.1981	0.0128	nan	0.2450	0.3808	0.4930	0.6252	0.4590	0.5288	0.4903	0.1688	0.0121	nan	0.2188	0.3569	0.4760
0.1282	23.75	3800	0.4509	0.3262	0.3922	0.5981	nan	0.6936	0.5414	0.6098	0.6114	0.1966	0.0250	nan	0.2791	0.3966	0.5680	0.6536	0.4908	0.5585	0.5406	0.1666	0.0219	nan	0.2447	0.3730	0.5383
0.0473	24.38	3900	0.4496	0.3334	0.4008	0.6063	nan	0.7051	0.5613	0.6237	0.5998	0.1989	0.0330	nan	0.2805	0.4479	0.5579	0.6636	0.5091	0.5670	0.5331	0.1698	0.0286	nan	0.2470	0.4166	0.5329
0.069	25.0	4000	0.4442	0.3404	0.4109	0.6170	nan	0.7116	0.5806	0.6320	0.6083	0.2084	0.0391	nan	0.2758	0.4698	0.5837	0.6682	0.5204	0.5734	0.5400	0.1764	0.0335	nan	0.2434	0.4342	0.5547

Framework versions

Transformers 4.37.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

unreal-hug/segformer-b3-finetuned-segments-outputs

segformer-b3-finetuned-segments-outputs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

unreal-hug/
segformer-b3-finetuned-segments-outputs