This model is a fine-tuned version of peldrak/segformer-b5-ade-finetuned-coastTrain on the peldrak/grCoastline_512 dataset. It achieves the following results on the evaluation set:

Loss: 0.1862
Mean Iou: 0.7261
Mean Accuracy: 0.7941
Overall Accuracy: 0.9457
Accuracy Water: 0.9892
Accuracy Whitewater: 0.1212
Accuracy Sediment: 0.8832
Accuracy Other Natural Terrain: 0.9089
Accuracy Vegetation: 0.8620
Accuracy Development: 0.7955
Accuracy Unknown: 0.9987
Iou Water: 0.9551
Iou Whitewater: 0.1179
Iou Sediment: 0.8407
Iou Other Natural Terrain: 0.7579
Iou Vegetation: 0.8186
Iou Development: 0.5970
Iou Unknown: 0.9957
F1 Score: 0.9457

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

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	F1 Score
0.3949	0.24	20	0.4674	0.5533	0.6548	0.8647	0.9629	0.0	0.8258	0.3380	0.8635	0.5954	0.9980	0.8563	0.0	0.5989	0.2978	0.6772	0.4600	0.9826	0.8527
0.3836	0.49	40	0.3509	0.6135	0.6996	0.8983	0.9860	0.0	0.9063	0.4811	0.8910	0.6351	0.9974	0.9202	0.0	0.7089	0.4491	0.6990	0.5252	0.9922	0.8914
0.4461	0.73	60	0.2453	0.6595	0.7407	0.9222	0.9948	0.0	0.8439	0.8332	0.8203	0.6988	0.9937	0.9202	0.0	0.7836	0.6604	0.7620	0.4984	0.9916	0.9219
0.7886	0.98	80	0.2794	0.6594	0.7302	0.9222	0.9841	0.0004	0.8601	0.6668	0.9358	0.6682	0.9962	0.9314	0.0004	0.8039	0.6086	0.7510	0.5271	0.9931	0.9198
0.2695	1.22	100	0.2217	0.6871	0.7557	0.9326	0.9879	0.0	0.9150	0.7878	0.8622	0.7392	0.9978	0.9159	0.0	0.8153	0.7031	0.7691	0.6121	0.9946	0.9311
0.2343	1.46	120	0.2122	0.6772	0.7477	0.9312	0.9894	0.0001	0.9146	0.8691	0.8144	0.6484	0.9975	0.9315	0.0001	0.7897	0.7006	0.7760	0.5482	0.9943	0.9300
0.1973	1.71	140	0.1803	0.6962	0.7646	0.9392	0.9769	0.0052	0.9011	0.8906	0.8519	0.7277	0.9988	0.9530	0.0052	0.8240	0.7305	0.7956	0.5717	0.9932	0.9387
0.2983	1.95	160	0.1978	0.6927	0.7630	0.9372	0.9918	0.0	0.9197	0.8532	0.8443	0.7362	0.9955	0.9476	0.0	0.8177	0.7287	0.7781	0.5832	0.9936	0.9363
0.1164	2.2	180	0.2010	0.6933	0.7540	0.9368	0.9880	0.0150	0.8760	0.8708	0.8652	0.6641	0.9990	0.9438	0.0150	0.8319	0.7074	0.7900	0.5732	0.9916	0.9357
0.1998	2.44	200	0.1988	0.6920	0.7694	0.9365	0.9824	0.0186	0.8837	0.9011	0.8253	0.7768	0.9977	0.9502	0.0186	0.8128	0.7331	0.7836	0.5517	0.9941	0.9365
0.5358	2.68	220	0.1884	0.6989	0.7633	0.9392	0.9887	0.0322	0.8630	0.8721	0.8830	0.7074	0.9969	0.9521	0.0322	0.8189	0.7330	0.7929	0.5683	0.9948	0.9386
0.184	2.93	240	0.2723	0.6598	0.7425	0.9222	0.9888	0.0024	0.8640	0.6604	0.9037	0.7791	0.9990	0.9246	0.0024	0.7290	0.6206	0.7797	0.5689	0.9936	0.9198
0.1789	3.17	260	0.3020	0.6541	0.7355	0.9089	0.9738	0.0623	0.8362	0.9736	0.6716	0.6330	0.9981	0.9485	0.0620	0.7995	0.6046	0.6513	0.5177	0.9953	0.9099
0.1925	3.41	280	0.1866	0.7052	0.7741	0.9420	0.9869	0.0404	0.8583	0.8487	0.9004	0.7852	0.9990	0.9463	0.0404	0.8083	0.7554	0.8171	0.5749	0.9943	0.9416
0.1533	3.66	300	0.1827	0.7119	0.7673	0.9460	0.9870	0.0374	0.8885	0.8771	0.9152	0.6690	0.9968	0.9497	0.0374	0.8259	0.7684	0.8228	0.5836	0.9951	0.9448
0.3405	3.9	320	0.1840	0.7068	0.7791	0.9420	0.9918	0.0417	0.8530	0.9050	0.8582	0.8061	0.9978	0.9467	0.0416	0.8163	0.7590	0.8089	0.5793	0.9955	0.9419
0.1095	4.15	340	0.1938	0.7142	0.7842	0.9419	0.9842	0.1098	0.8741	0.9043	0.8602	0.7577	0.9989	0.9536	0.1080	0.8306	0.7499	0.8076	0.5549	0.9949	0.9420
0.0942	4.39	360	0.2193	0.7139	0.7905	0.9384	0.9785	0.1433	0.8578	0.8932	0.8522	0.8094	0.9987	0.9456	0.1394	0.8182	0.7408	0.7945	0.5640	0.9947	0.9388
0.151	4.63	380	0.1964	0.7189	0.7915	0.9388	0.9826	0.1692	0.8619	0.8826	0.8601	0.7854	0.9983	0.9491	0.1609	0.8171	0.7329	0.7912	0.5858	0.9950	0.9388
0.1181	4.88	400	0.2256	0.7023	0.7752	0.9370	0.9880	0.1247	0.8074	0.8731	0.8893	0.7453	0.9988	0.9505	0.1227	0.7869	0.7375	0.8042	0.5191	0.9953	0.9370
0.0898	5.12	420	0.2061	0.7193	0.7950	0.9429	0.9908	0.1206	0.8387	0.8846	0.8758	0.8559	0.9983	0.9504	0.1163	0.8109	0.7627	0.8149	0.5839	0.9961	0.9432
0.1225	5.37	440	0.1905	0.7361	0.8013	0.9460	0.9887	0.1963	0.8653	0.9102	0.8772	0.7726	0.9991	0.9502	0.1790	0.8365	0.7638	0.8197	0.6085	0.9953	0.9458
0.1501	5.61	460	0.1883	0.7325	0.7938	0.9453	0.9921	0.1697	0.8818	0.9120	0.8677	0.7350	0.9983	0.9480	0.1599	0.8421	0.7570	0.8113	0.6134	0.9960	0.9448
0.0686	5.85	480	0.2161	0.7313	0.8038	0.9429	0.9870	0.2042	0.8679	0.8909	0.8651	0.8126	0.9987	0.9538	0.1884	0.8378	0.7427	0.8065	0.5939	0.9960	0.9432
0.166	6.1	500	0.2229	0.7250	0.7891	0.9438	0.9887	0.1570	0.8673	0.8510	0.9086	0.7524	0.9985	0.9540	0.1485	0.8359	0.7417	0.8132	0.5857	0.9959	0.9434
0.1168	6.34	520	0.2215	0.7207	0.7956	0.9412	0.9911	0.1826	0.8410	0.9082	0.8622	0.7868	0.9976	0.9508	0.1697	0.8113	0.7493	0.8156	0.5523	0.9959	0.9416
0.1935	6.59	540	0.1862	0.7261	0.7941	0.9457	0.9892	0.1212	0.8832	0.9089	0.8620	0.7955	0.9987	0.9551	0.1179	0.8407	0.7579	0.8186	0.5970	0.9957	0.9457

Framework versions

Transformers 4.37.0
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.1

peldrak/segformer-b5-ade-finetuned-coastTrain-grCoastline

segformer-b5-ade-finetuned-coastTrain-grCoastline

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

peldrak/
segformer-b5-ade-finetuned-coastTrain-grCoastline