This model is a fine-tuned version of peldrak/segformer-b4-cityscapes-finetuned-coastTrain on the peldrak/grCoastline_512 dataset. It achieves the following results on the evaluation set:

Loss: 0.2751
Mean Iou: 0.7477
Mean Accuracy: 0.8306
Overall Accuracy: 0.9274
Accuracy Water: 0.9618
Accuracy Whitewater: 0.5458
Accuracy Sediment: 0.9189
Accuracy Other Natural Terrain: 0.6111
Accuracy Vegetation: 0.9584
Accuracy Development: 0.8193
Accuracy Unknown: 0.9989
Iou Water: 0.9317
Iou Whitewater: 0.4264
Iou Sediment: 0.7904
Iou Other Natural Terrain: 0.5618
Iou Vegetation: 0.8457
Iou Development: 0.6820
Iou Unknown: 0.9958
F1 Score: 0.9247

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: 30

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
0.2133	0.24	20	0.5121	0.5418	0.6551	0.8537	0.9585	0.0	0.8618	0.0673	0.9007	0.7998	0.9976	0.9023	0.0	0.6459	0.0662	0.7036	0.4840	0.9902	0.8244
0.414	0.49	40	0.3762	0.6200	0.7083	0.8876	0.9715	0.0	0.8827	0.6187	0.8259	0.6633	0.9959	0.9246	0.0	0.7128	0.4398	0.7463	0.5240	0.9926	0.8868
0.1269	0.73	60	0.4382	0.5881	0.6925	0.8750	0.9318	0.0009	0.8038	0.2996	0.9384	0.8817	0.9916	0.8967	0.0009	0.6716	0.2920	0.7370	0.5281	0.9905	0.8642
0.1997	0.98	80	0.3072	0.6505	0.7324	0.9091	0.9761	0.0012	0.8633	0.5977	0.9144	0.7777	0.9963	0.9201	0.0012	0.7241	0.5213	0.8180	0.5755	0.9932	0.9063
0.1083	1.22	100	0.3962	0.6172	0.6949	0.8968	0.9637	0.0008	0.9391	0.4493	0.9514	0.5660	0.9938	0.9245	0.0008	0.6964	0.4157	0.7968	0.4937	0.9928	0.8887
0.3105	1.46	120	0.2904	0.6512	0.7371	0.8982	0.9807	0.0948	0.9006	0.7090	0.8154	0.6621	0.9971	0.9235	0.0936	0.7400	0.4954	0.7695	0.5415	0.9947	0.8983
0.2026	1.71	140	0.3171	0.6590	0.7377	0.9117	0.9627	0.0314	0.9322	0.5886	0.9180	0.7359	0.9952	0.9374	0.0308	0.7378	0.5309	0.8117	0.5706	0.9935	0.9089
0.1502	1.95	160	0.2682	0.6830	0.7555	0.9097	0.9747	0.2278	0.8771	0.5280	0.9490	0.7334	0.9984	0.9210	0.2170	0.7785	0.4686	0.7951	0.6075	0.9934	0.9048
0.0935	2.2	180	0.3351	0.6378	0.7209	0.9009	0.9776	0.0421	0.9416	0.7188	0.8390	0.5301	0.9967	0.9143	0.0421	0.6865	0.5566	0.8033	0.4677	0.9940	0.8991
0.2849	2.44	200	0.2994	0.6750	0.7469	0.9177	0.9675	0.0987	0.9478	0.5966	0.9442	0.6781	0.9954	0.9286	0.0931	0.7319	0.5570	0.8430	0.5768	0.9943	0.9142
0.4015	2.68	220	0.3270	0.6682	0.7424	0.9058	0.9778	0.1729	0.8825	0.4336	0.9693	0.7683	0.9924	0.9117	0.1667	0.7556	0.4112	0.7914	0.6490	0.9917	0.8978
0.236	2.93	240	0.3329	0.6715	0.7520	0.9093	0.9707	0.1421	0.9684	0.6999	0.8499	0.6351	0.9981	0.9231	0.1363	0.7081	0.5718	0.8119	0.5550	0.9941	0.9083
0.0648	3.17	260	0.2652	0.6981	0.7843	0.9186	0.9715	0.1933	0.8879	0.7052	0.8762	0.8578	0.9983	0.9202	0.1728	0.7617	0.5836	0.8319	0.6221	0.9942	0.9184
0.1835	3.41	280	0.2945	0.6778	0.7568	0.9165	0.9734	0.0494	0.9337	0.6959	0.8648	0.7820	0.9986	0.9185	0.0483	0.7555	0.5665	0.8205	0.6412	0.9942	0.9154
0.0845	3.66	300	0.3184	0.6904	0.7687	0.9124	0.9741	0.2469	0.9595	0.6890	0.8701	0.6442	0.9969	0.9117	0.2429	0.7297	0.5577	0.8332	0.5628	0.9949	0.9112
0.1369	3.9	320	0.2729	0.7262	0.8066	0.9229	0.9706	0.3939	0.9080	0.5670	0.9483	0.8607	0.9976	0.9268	0.3526	0.7736	0.5358	0.8440	0.6550	0.9954	0.9195
0.2455	4.15	340	0.2923	0.7087	0.7941	0.9148	0.9687	0.3314	0.9364	0.7874	0.8373	0.6999	0.9978	0.9457	0.2815	0.7742	0.5592	0.8056	0.5992	0.9954	0.9160
0.1555	4.39	360	0.2976	0.6953	0.7665	0.9227	0.9694	0.1557	0.9383	0.6031	0.9438	0.7572	0.9977	0.9260	0.1480	0.7761	0.5530	0.8467	0.6235	0.9939	0.9194
0.1789	4.63	380	0.2856	0.7085	0.7802	0.9229	0.9717	0.2405	0.9347	0.6306	0.9315	0.7538	0.9983	0.9227	0.2238	0.7697	0.5606	0.8466	0.6410	0.9948	0.9202
0.1023	4.88	400	0.2929	0.7024	0.7723	0.9217	0.9639	0.2536	0.9496	0.6680	0.9365	0.6356	0.9988	0.9294	0.2279	0.7536	0.5796	0.8489	0.5825	0.9951	0.9191
0.1112	5.12	420	0.3065	0.7011	0.7720	0.9210	0.9812	0.1859	0.9282	0.6461	0.9172	0.7497	0.9955	0.9220	0.1800	0.7803	0.5507	0.8348	0.6454	0.9944	0.9187
0.1932	5.37	440	0.3091	0.7137	0.7938	0.9183	0.9588	0.3565	0.9580	0.6726	0.8990	0.7139	0.9980	0.9241	0.3106	0.7708	0.5589	0.8277	0.6077	0.9960	0.9170
0.092	5.61	460	0.3078	0.7082	0.7831	0.9188	0.9659	0.2639	0.9494	0.6480	0.9015	0.7550	0.9979	0.9312	0.2488	0.7690	0.5543	0.8162	0.6422	0.9957	0.9170
0.0515	5.85	480	0.3024	0.7363	0.8322	0.9208	0.9660	0.5072	0.9229	0.7492	0.8596	0.8222	0.9980	0.9318	0.3939	0.7716	0.5899	0.8228	0.6482	0.9959	0.9213
0.1004	6.1	500	0.3174	0.7201	0.7917	0.9265	0.9707	0.2714	0.9527	0.5768	0.9471	0.8254	0.9976	0.9234	0.2468	0.7821	0.5443	0.8508	0.6974	0.9958	0.9228
0.0747	6.34	520	0.3035	0.7158	0.7896	0.9233	0.9645	0.3336	0.9640	0.6184	0.9394	0.7088	0.9987	0.9279	0.2958	0.7659	0.5631	0.8517	0.6106	0.9958	0.9204
0.205	6.59	540	0.2899	0.7245	0.8015	0.9234	0.9788	0.3345	0.9541	0.7218	0.8790	0.7447	0.9977	0.9340	0.3038	0.7685	0.5897	0.8380	0.6410	0.9961	0.9228
0.0729	6.83	560	0.2751	0.7477	0.8306	0.9274	0.9618	0.5458	0.9189	0.6111	0.9584	0.8193	0.9989	0.9317	0.4264	0.7904	0.5618	0.8457	0.6820	0.9958	0.9247

Framework versions

Transformers 4.38.1
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

peldrak/segformer-b4-cityscapes-finetuned-coastTrain-grCoastline

segformer-b4-cityscapes-finetuned-coastTrain-grCoastline

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

peldrak/
segformer-b4-cityscapes-finetuned-coastTrain-grCoastline