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peldrak/
segformer-finetuned-coastal

Name: segformer-finetuned-coastal
Availability: InStock

generated_from_trainer

nvidia/segformer-b0-finetuned-ade-512-512

finetune

other

endpoints_compatible

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segformer-finetuned-coastal

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on the peldrak/coastal3 dataset. It achieves the following results on the evaluation set:

Loss: 1.7456
Mean Iou: 0.0841
Mean Accuracy: 0.1629
Overall Accuracy: 0.3309
Accuracy Water: 0.2728
Accuracy Whitewater: 0.0
Accuracy Sediment: 0.0194
Accuracy Other Natural Terrain: 0.0
Accuracy Vegetation: 0.7325
Accuracy Development: 0.0055
Accuracy Unknown: 0.1102
Iou Water: 0.1767
Iou Whitewater: 0.0
Iou Sediment: 0.0157
Iou Other Natural Terrain: 0.0
Iou Vegetation: 0.3002
Iou Development: 0.0052
Iou Unknown: 0.0908

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: 6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Water	Accuracy Whitewater	Accuracy Sediment	Accuracy Other Natural Terrain	Accuracy Vegetation	Accuracy Development	Accuracy Unknown	Iou Water	Iou Whitewater	Iou Sediment	Iou Other Natural Terrain	Iou Vegetation	Iou Development	Iou Unknown
1.8429	0.12	20	1.8742	0.0623	0.1432	0.2198	0.5172	0.0044	0.3137	0.0050	0.1165	0.0019	0.0435	0.2514	0.0037	0.0521	0.0034	0.0889	0.0019	0.0348
1.6623	0.25	40	1.8297	0.0731	0.1572	0.2536	0.4349	0.0007	0.3336	0.0010	0.3158	0.0011	0.0135	0.2462	0.0006	0.0569	0.0009	0.1940	0.0011	0.0120
1.6173	0.37	60	1.7856	0.0812	0.1585	0.3080	0.3151	0.0007	0.1179	0.0005	0.6339	0.0012	0.0405	0.2056	0.0007	0.0357	0.0005	0.2859	0.0012	0.0388
1.5078	0.49	80	1.7805	0.0764	0.1537	0.3038	0.2792	0.0	0.1036	0.0000	0.6639	0.0003	0.0290	0.1839	0.0	0.0338	0.0000	0.2895	0.0003	0.0274
1.3927	0.62	100	1.7549	0.0725	0.1521	0.3227	0.2589	0.0	0.0222	0.0001	0.7643	0.0005	0.0187	0.1703	0.0	0.0133	0.0001	0.3055	0.0005	0.0179
1.6024	0.74	120	1.7348	0.0835	0.1598	0.3271	0.3514	0.0	0.0204	0.0001	0.6538	0.0005	0.0926	0.2021	0.0	0.0147	0.0001	0.2873	0.0005	0.0796
1.3287	0.86	140	1.7352	0.0776	0.1556	0.3267	0.3309	0.0	0.0083	0.0	0.6910	0.0000	0.0591	0.1935	0.0	0.0076	0.0	0.2849	0.0000	0.0573
1.4153	0.99	160	1.7024	0.0746	0.1567	0.3356	0.2947	0.0	0.0011	0.0	0.7683	0.0006	0.0320	0.1879	0.0	0.0011	0.0	0.3021	0.0006	0.0309
1.3334	1.11	180	1.7262	0.0744	0.1567	0.3374	0.3130	0.0	0.0018	0.0	0.7620	0.0001	0.0203	0.1988	0.0	0.0017	0.0	0.3004	0.0001	0.0198
1.3956	1.23	200	1.7304	0.0858	0.1622	0.3326	0.4838	0.0	0.0127	0.0	0.5432	0.0015	0.0944	0.2373	0.0	0.0112	0.0	0.2731	0.0015	0.0777
1.5776	1.36	220	1.7300	0.0791	0.1622	0.3411	0.2581	0.0	0.0010	0.0	0.8012	0.0003	0.0748	0.1734	0.0	0.0010	0.0	0.3144	0.0003	0.0647
1.1656	1.48	240	1.7248	0.0831	0.1657	0.3440	0.2687	0.0	0.0026	0.0	0.7879	0.0014	0.0995	0.1775	0.0	0.0025	0.0	0.3183	0.0014	0.0822
1.4429	1.6	260	1.7308	0.0764	0.1616	0.3408	0.2091	0.0	0.0029	0.0	0.8518	0.0037	0.0637	0.1507	0.0	0.0028	0.0	0.3200	0.0036	0.0578
1.6649	1.73	280	1.7282	0.0743	0.1564	0.3372	0.3261	0.0	0.0033	0.0	0.7514	0.0015	0.0128	0.1994	0.0	0.0031	0.0	0.3037	0.0015	0.0125
1.3634	1.85	300	1.7216	0.0847	0.1653	0.3413	0.3196	0.0	0.0273	0.0	0.7402	0.0036	0.0665	0.2012	0.0	0.0204	0.0	0.3101	0.0035	0.0579
1.5224	1.98	320	1.7343	0.0822	0.1626	0.3410	0.3793	0.0	0.0277	0.0	0.6985	0.0017	0.0311	0.2236	0.0	0.0210	0.0	0.3000	0.0016	0.0293
1.2527	2.1	340	1.7149	0.0759	0.1559	0.3344	0.4108	0.0	0.0119	0.0	0.6629	0.0021	0.0034	0.2244	0.0	0.0105	0.0	0.2913	0.0021	0.0033
1.5931	2.22	360	1.7170	0.0838	0.1619	0.3365	0.4061	0.0	0.0098	0.0	0.6414	0.0028	0.0730	0.2228	0.0	0.0090	0.0	0.2882	0.0027	0.0641
1.2434	2.35	380	1.7437	0.0844	0.1619	0.3364	0.4734	0.0	0.0118	0.0	0.5785	0.0028	0.0667	0.2391	0.0	0.0105	0.0	0.2777	0.0027	0.0607
1.4071	2.47	400	1.7316	0.0823	0.1639	0.3433	0.3054	0.0	0.0053	0.0	0.7633	0.0057	0.0674	0.1933	0.0	0.0051	0.0	0.3125	0.0054	0.0596
1.2177	2.59	420	1.7195	0.0848	0.1657	0.3459	0.3442	0.0	0.0030	0.0	0.7315	0.0110	0.0705	0.2084	0.0	0.0029	0.0	0.3111	0.0102	0.0613
1.3724	2.72	440	1.7359	0.0843	0.1660	0.3455	0.3091	0.0	0.0089	0.0	0.7620	0.0057	0.0761	0.1946	0.0	0.0084	0.0	0.3147	0.0054	0.0669
1.3973	2.84	460	1.7469	0.0827	0.1617	0.3352	0.3153	0.0	0.0101	0.0	0.7231	0.0109	0.0724	0.1922	0.0	0.0088	0.0	0.3045	0.0099	0.0638
1.3098	2.96	480	1.7193	0.0852	0.1658	0.3447	0.3412	0.0	0.0032	0.0	0.7240	0.0038	0.0887	0.2039	0.0	0.0031	0.0	0.3076	0.0037	0.0779
0.9545	3.09	500	1.7256	0.0840	0.1627	0.3359	0.3366	0.0	0.0026	0.0	0.6959	0.0077	0.0960	0.2007	0.0	0.0025	0.0	0.2969	0.0072	0.0808
1.176	3.21	520	1.7334	0.0827	0.1616	0.3357	0.3396	0.0	0.0024	0.0	0.6963	0.0016	0.0915	0.1999	0.0	0.0024	0.0	0.2966	0.0015	0.0785
1.5622	3.33	540	1.7790	0.0689	0.1528	0.3311	0.2393	0.0	0.0015	0.0	0.8186	0.0009	0.0091	0.1638	0.0	0.0015	0.0	0.3073	0.0009	0.0089
1.2673	3.46	560	1.7339	0.0803	0.1585	0.3302	0.3228	0.0	0.0113	0.0	0.7035	0.0036	0.0686	0.1930	0.0	0.0104	0.0	0.2942	0.0034	0.0613
1.418	3.58	580	1.7648	0.0760	0.1563	0.3325	0.3074	0.0	0.0022	0.0	0.7416	0.0041	0.0386	0.1902	0.0	0.0022	0.0	0.2994	0.0038	0.0363
1.3578	3.7	600	1.7338	0.0845	0.1619	0.3327	0.3548	0.0	0.0119	0.0	0.6693	0.0076	0.0898	0.2039	0.0	0.0108	0.0	0.2940	0.0070	0.0758
1.1991	3.83	620	1.7711	0.0761	0.1546	0.3285	0.3473	0.0	0.0049	0.0	0.6906	0.0018	0.0379	0.1995	0.0	0.0046	0.0	0.2912	0.0017	0.0356
1.3699	3.95	640	1.7421	0.0829	0.1595	0.3312	0.4290	0.0	0.0048	0.0	0.5994	0.0032	0.0804	0.2223	0.0	0.0045	0.0	0.2819	0.0031	0.0687
1.308	4.07	660	1.7769	0.0709	0.1518	0.3250	0.2512	0.0	0.0035	0.0	0.7784	0.0034	0.0260	0.1656	0.0	0.0033	0.0	0.2996	0.0033	0.0245
1.3746	4.2	680	1.7811	0.0749	0.1538	0.3283	0.3497	0.0	0.0077	0.0	0.6953	0.0037	0.0200	0.2022	0.0	0.0070	0.0	0.2928	0.0035	0.0191
1.2085	4.32	700	1.7401	0.0825	0.1600	0.3319	0.3632	0.0	0.0106	0.0	0.6663	0.0042	0.0759	0.2064	0.0	0.0096	0.0	0.2912	0.0040	0.0664
0.8119	4.44	720	1.7638	0.0755	0.1539	0.3284	0.3660	0.0	0.0060	0.0	0.6784	0.0034	0.0237	0.2073	0.0	0.0053	0.0	0.2902	0.0033	0.0226
1.1547	4.57	740	1.7581	0.0795	0.1573	0.3289	0.3410	0.0	0.0140	0.0	0.6879	0.0030	0.0550	0.1996	0.0	0.0116	0.0	0.2929	0.0029	0.0497
1.2229	4.69	760	1.7817	0.0730	0.1550	0.3243	0.1861	0.0	0.0124	0.0	0.8198	0.0036	0.0631	0.1365	0.0	0.0103	0.0	0.3050	0.0034	0.0557
1.3332	4.81	780	1.7580	0.0769	0.1563	0.3276	0.2656	0.0	0.0044	0.0	0.7524	0.0042	0.0677	0.1721	0.0	0.0041	0.0	0.2998	0.0040	0.0581
1.1668	4.94	800	1.7456	0.0841	0.1629	0.3309	0.2728	0.0	0.0194	0.0	0.7325	0.0055	0.1102	0.1767	0.0	0.0157	0.0	0.3002	0.0052	0.0908

Framework versions

Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

peldrak/segformer-finetuned-coastal

segformer-finetuned-coastal

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

peldrak/
segformer-finetuned-coastal