This model is a fine-tuned version of nvidia/segformer-b5-finetuned-cityscapes-1024-1024 on the peldrak/coastTrain dataset. It achieves the following results on the evaluation set:

Loss: 0.1881
Mean Iou: 0.8269
Mean Accuracy: 0.9060
Overall Accuracy: 0.9424
Accuracy Water: 0.9707
Accuracy Whitewater: 0.7995
Accuracy Sediment: 0.8682
Accuracy Other Natural Terrain: 0.8548
Accuracy Vegetation: 0.9256
Accuracy Development: 0.9477
Accuracy Unknown: 0.9753
Iou Water: 0.9366
Iou Whitewater: 0.6328
Iou Sediment: 0.8126
Iou Other Natural Terrain: 0.7202
Iou Vegetation: 0.8474
Iou Development: 0.8731
Iou Unknown: 0.9654
F1 Score: 0.9423

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
1.5137	0.16	20	1.4093	0.2798	0.3909	0.5891	0.7019	0.0298	0.0296	0.0247	0.9567	0.4761	0.5177	0.5641	0.0196	0.0292	0.0213	0.3809	0.4281	0.5158	0.5595
1.055	0.31	40	1.0631	0.3577	0.4550	0.7052	0.8814	0.0052	0.2973	0.0004	0.9572	0.4259	0.6178	0.7052	0.0050	0.2796	0.0004	0.4917	0.4043	0.6175	0.6828
1.0713	0.47	60	0.8083	0.4894	0.5712	0.8248	0.9457	0.0002	0.6146	0.0	0.8897	0.6666	0.8818	0.8231	0.0002	0.4732	0.0	0.6786	0.5733	0.8773	0.8106
1.1381	0.62	80	0.7066	0.4937	0.5901	0.8202	0.8788	0.0	0.5956	0.0051	0.9164	0.8435	0.8913	0.8319	0.0	0.4887	0.0051	0.6175	0.6277	0.8850	0.8107
1.7365	0.78	100	0.6244	0.5151	0.6193	0.8271	0.8129	0.0	0.8459	0.0000	0.8727	0.9082	0.8954	0.7790	0.0	0.5186	0.0000	0.7262	0.6899	0.8922	0.8218
0.7307	0.93	120	0.5070	0.5352	0.6115	0.8598	0.9605	0.0	0.6408	0.0	0.9080	0.8753	0.8955	0.8718	0.0	0.5549	0.0	0.6907	0.7387	0.8904	0.8458
1.1675	1.09	140	0.5019	0.5353	0.6205	0.8593	0.9221	0.0	0.6875	0.0000	0.9250	0.8983	0.9108	0.8673	0.0	0.5808	0.0000	0.7074	0.7402	0.8514	0.8465
0.701	1.24	160	0.4414	0.5432	0.6326	0.8671	0.9344	0.0007	0.7689	0.0004	0.8593	0.9636	0.9009	0.8799	0.0007	0.6322	0.0004	0.7217	0.6796	0.8876	0.8559
0.7284	1.4	180	0.4495	0.5437	0.6268	0.8576	0.8939	0.0	0.7874	0.0233	0.9336	0.8510	0.8985	0.8509	0.0	0.5802	0.0233	0.7158	0.7453	0.8902	0.8485
0.4898	1.55	200	0.3884	0.5559	0.6380	0.8772	0.9541	0.0	0.7934	0.0144	0.8666	0.9343	0.9031	0.8912	0.0	0.6746	0.0144	0.7378	0.6831	0.8901	0.8656
0.3669	1.71	220	0.3874	0.5574	0.6333	0.8752	0.9796	0.0001	0.7557	0.0380	0.8465	0.9239	0.8892	0.8747	0.0001	0.6674	0.0380	0.7369	0.7045	0.8802	0.8625
0.6277	1.86	240	0.3964	0.5600	0.6406	0.8770	0.9221	0.0029	0.8164	0.0	0.9202	0.9157	0.9069	0.8787	0.0029	0.6458	0.0	0.7487	0.7474	0.8965	0.8657
0.3567	2.02	260	0.3927	0.5648	0.6499	0.8825	0.9215	0.0000	0.8792	0.0016	0.8867	0.9531	0.9073	0.8862	0.0000	0.6898	0.0016	0.7659	0.7127	0.8974	0.8717
0.4065	2.17	280	0.3756	0.5633	0.6377	0.8810	0.9551	0.0145	0.7178	0.0058	0.9349	0.9232	0.9125	0.9051	0.0145	0.6369	0.0058	0.7220	0.7586	0.9000	0.8686
0.7596	2.33	300	0.3224	0.5907	0.6599	0.9000	0.9569	0.0001	0.8956	0.0230	0.9042	0.9352	0.9044	0.9019	0.0001	0.7377	0.0230	0.7935	0.7806	0.8977	0.8879
0.3008	2.48	320	0.3164	0.6114	0.6773	0.9011	0.9518	0.0022	0.8608	0.1780	0.9504	0.8886	0.9089	0.8993	0.0022	0.7541	0.1777	0.7957	0.7517	0.8992	0.8919
0.346	2.64	340	0.2927	0.6143	0.6807	0.9064	0.9643	0.0198	0.8652	0.1390	0.9219	0.9389	0.9156	0.9072	0.0197	0.7542	0.1388	0.8234	0.7570	0.8999	0.8967
0.272	2.79	360	0.2769	0.6170	0.6786	0.9098	0.9711	0.0228	0.8956	0.1033	0.9136	0.9310	0.9130	0.9071	0.0226	0.7745	0.1033	0.8178	0.7908	0.9030	0.8992
1.1467	2.95	380	0.2737	0.6153	0.6746	0.9107	0.9669	0.0005	0.9234	0.0834	0.9132	0.9179	0.9170	0.9041	0.0005	0.7624	0.0834	0.8207	0.8251	0.9110	0.8995
0.2723	3.1	400	0.3110	0.6254	0.6931	0.8984	0.9796	0.0115	0.8782	0.2913	0.8216	0.9572	0.9124	0.8902	0.0115	0.7610	0.2882	0.7610	0.7626	0.9032	0.8900
0.5568	3.26	420	0.3337	0.6544	0.7155	0.9012	0.9500	0.0359	0.7990	0.4369	0.9408	0.9111	0.9345	0.8904	0.0356	0.7152	0.4322	0.7726	0.8280	0.9068	0.8948
1.1779	3.41	440	0.2947	0.6237	0.6857	0.9057	0.9679	0.0133	0.7896	0.2042	0.9229	0.9476	0.9541	0.9116	0.0133	0.7095	0.2041	0.7783	0.8135	0.9357	0.8961
0.4611	3.57	460	0.2659	0.6563	0.7149	0.9153	0.9562	0.0761	0.9265	0.2765	0.9315	0.9160	0.9215	0.9051	0.0754	0.7745	0.2759	0.8331	0.8170	0.9133	0.9084
0.3981	3.72	480	0.2964	0.6657	0.7269	0.9046	0.9789	0.0658	0.8862	0.4978	0.8601	0.8802	0.9193	0.8972	0.0648	0.7316	0.4900	0.7955	0.7687	0.9118	0.8991
0.2398	3.88	500	0.2707	0.7112	0.7714	0.9181	0.9553	0.2649	0.8826	0.5232	0.9516	0.8983	0.9236	0.9130	0.2463	0.7796	0.5159	0.8301	0.7813	0.9125	0.9154
0.251	4.03	520	0.2693	0.6980	0.7686	0.9135	0.9680	0.2384	0.8886	0.5380	0.8746	0.9509	0.9215	0.9247	0.2301	0.7644	0.5082	0.8135	0.7432	0.9019	0.9110
0.4024	4.19	540	0.2900	0.6988	0.7641	0.9099	0.9662	0.2524	0.8416	0.5462	0.8835	0.8924	0.9662	0.9022	0.2351	0.7297	0.4979	0.7912	0.8078	0.9278	0.9065
0.3683	4.34	560	0.2852	0.7226	0.7853	0.9153	0.9567	0.3140	0.8381	0.5886	0.9349	0.9398	0.9248	0.9165	0.2981	0.7355	0.5773	0.8091	0.8062	0.9154	0.9132
0.2335	4.5	580	0.3128	0.7163	0.7833	0.9052	0.9370	0.3077	0.8492	0.6411	0.9398	0.8903	0.9182	0.8904	0.2859	0.6985	0.6154	0.8052	0.8088	0.9103	0.9038
1.5312	4.65	600	0.2551	0.7199	0.7784	0.9199	0.9599	0.3232	0.8606	0.4929	0.9314	0.9399	0.9410	0.9213	0.3082	0.7482	0.4884	0.8132	0.8258	0.9342	0.9175
0.3685	4.81	620	0.2286	0.7447	0.8053	0.9245	0.9757	0.3914	0.8468	0.6323	0.9213	0.9265	0.9431	0.9183	0.3353	0.7791	0.5920	0.8197	0.8356	0.9329	0.9226
0.2664	4.96	640	0.3448	0.7422	0.8128	0.9120	0.9329	0.4951	0.8208	0.6164	0.9396	0.9166	0.9679	0.9033	0.4493	0.7043	0.5822	0.7999	0.8185	0.9377	0.9112
0.2662	5.12	660	0.2932	0.7448	0.8158	0.9168	0.9516	0.5045	0.8308	0.6171	0.9215	0.9234	0.9616	0.9128	0.4277	0.7379	0.5809	0.8022	0.8260	0.9261	0.9157
1.1342	5.27	680	0.2631	0.7772	0.8420	0.9267	0.9816	0.5460	0.7974	0.7644	0.9196	0.9208	0.9640	0.9220	0.4753	0.7476	0.6784	0.8126	0.8527	0.9521	0.9253
0.2968	5.43	700	0.2302	0.7884	0.8577	0.9318	0.9537	0.5790	0.8663	0.7551	0.9368	0.9450	0.9680	0.9260	0.5104	0.7765	0.6694	0.8353	0.8467	0.9547	0.9315
0.3812	5.58	720	0.2051	0.7779	0.8372	0.9336	0.9772	0.4866	0.8627	0.7113	0.9184	0.9430	0.9612	0.9189	0.3999	0.8046	0.6717	0.8473	0.8508	0.9517	0.9325
0.1902	5.74	740	0.2044	0.7815	0.8584	0.9330	0.9651	0.5500	0.8494	0.8160	0.9469	0.9219	0.9597	0.9253	0.4528	0.7914	0.6608	0.8459	0.8452	0.9490	0.9325
0.2853	5.89	760	0.2718	0.7844	0.8523	0.9239	0.9479	0.5953	0.8483	0.7417	0.9288	0.9486	0.9554	0.9179	0.5321	0.7413	0.6981	0.8124	0.8399	0.9495	0.9238
0.1833	6.05	780	0.2191	0.7903	0.8613	0.9288	0.9598	0.6288	0.8627	0.7658	0.9246	0.9327	0.9546	0.9209	0.5326	0.7646	0.6852	0.8253	0.8539	0.9494	0.9286
0.2128	6.2	800	0.1897	0.8066	0.8748	0.9392	0.9703	0.6067	0.9032	0.8183	0.9098	0.9555	0.9599	0.9274	0.5356	0.8336	0.7080	0.8593	0.8315	0.9506	0.9388
0.2327	6.36	820	0.2148	0.7999	0.8734	0.9341	0.9531	0.6428	0.9396	0.7743	0.8991	0.9443	0.9604	0.9131	0.5264	0.8037	0.6901	0.8537	0.8609	0.9518	0.9342
1.0808	6.51	840	0.2443	0.7872	0.8597	0.9273	0.9631	0.6632	0.8257	0.7386	0.9302	0.9392	0.9580	0.9267	0.5440	0.7644	0.6807	0.8081	0.8341	0.9524	0.9270
0.2563	6.67	860	0.2376	0.7825	0.8473	0.9288	0.9637	0.5780	0.8420	0.7267	0.9379	0.9213	0.9619	0.9198	0.4945	0.7607	0.6624	0.8265	0.8596	0.9542	0.9282
0.209	6.82	880	0.2008	0.7996	0.8652	0.9353	0.9713	0.5989	0.8857	0.7859	0.9103	0.9475	0.9567	0.9236	0.5153	0.8064	0.7099	0.8452	0.8452	0.9516	0.9349
0.1402	6.98	900	0.2454	0.7832	0.8475	0.9295	0.9673	0.6082	0.8135	0.7021	0.9518	0.9292	0.9603	0.9271	0.5157	0.7680	0.6419	0.8117	0.8636	0.9544	0.9289
0.383	7.13	920	0.2064	0.7999	0.8706	0.9334	0.9720	0.6260	0.8447	0.8266	0.9219	0.9434	0.9599	0.9269	0.5389	0.7808	0.7077	0.8303	0.8620	0.9528	0.9329
0.5665	7.29	940	0.2529	0.7892	0.8836	0.9279	0.9513	0.7850	0.8478	0.7735	0.9296	0.9380	0.9601	0.9205	0.5403	0.7625	0.6755	0.8270	0.8449	0.9539	0.9284
0.2031	7.44	960	0.2445	0.7782	0.8562	0.9276	0.9491	0.6769	0.8635	0.6824	0.9435	0.9101	0.9680	0.9197	0.5111	0.7486	0.6226	0.8341	0.8524	0.9592	0.9277
0.1641	7.6	980	0.1669	0.8201	0.8875	0.9475	0.9762	0.6290	0.9007	0.8526	0.9303	0.9519	0.9719	0.9374	0.5367	0.8539	0.7152	0.8790	0.8560	0.9624	0.9472
0.1214	7.75	1000	0.1696	0.8279	0.8914	0.9479	0.9685	0.7061	0.9217	0.7959	0.9472	0.9376	0.9630	0.9371	0.5860	0.8561	0.7139	0.8779	0.8673	0.9570	0.9478
0.0926	7.91	1020	0.2069	0.8005	0.8677	0.9339	0.9733	0.6746	0.8183	0.7692	0.9485	0.9307	0.9595	0.9405	0.5605	0.7717	0.7126	0.8248	0.8397	0.9540	0.9334
0.4292	8.06	1040	0.1985	0.8140	0.8845	0.9408	0.9620	0.6695	0.9283	0.8152	0.9259	0.9310	0.9596	0.9263	0.5722	0.8215	0.6976	0.8728	0.8538	0.9535	0.9409
0.1741	8.22	1060	0.2038	0.8082	0.8877	0.9352	0.9619	0.7341	0.8483	0.8190	0.9322	0.9496	0.9685	0.9296	0.5834	0.7820	0.7134	0.8411	0.8503	0.9579	0.9351
0.218	8.37	1080	0.1888	0.8054	0.8766	0.9409	0.9770	0.7073	0.8644	0.7571	0.9442	0.9211	0.9653	0.9381	0.5579	0.8125	0.6568	0.8547	0.8599	0.9581	0.9406
0.1687	8.53	1100	0.1887	0.8208	0.8996	0.9424	0.9617	0.7261	0.9048	0.8567	0.9210	0.9514	0.9754	0.9320	0.6109	0.8214	0.6963	0.8650	0.8558	0.9643	0.9425
0.1115	8.68	1120	0.1924	0.8137	0.8967	0.9390	0.9737	0.7728	0.8477	0.8500	0.9296	0.9305	0.9726	0.9374	0.6039	0.7985	0.6894	0.8367	0.8657	0.9640	0.9389
0.2189	8.84	1140	0.1950	0.8102	0.8791	0.9397	0.9718	0.6680	0.8758	0.8029	0.9293	0.9407	0.9655	0.9321	0.5657	0.8183	0.6952	0.8497	0.8525	0.9581	0.9395
0.2046	8.99	1160	0.1955	0.8183	0.8889	0.9409	0.9707	0.7188	0.8764	0.8236	0.9345	0.9287	0.9693	0.9349	0.5922	0.8067	0.7146	0.8500	0.8690	0.9610	0.9407
0.0574	9.15	1180	0.1943	0.8258	0.9017	0.9423	0.9642	0.7756	0.8757	0.8409	0.9354	0.9389	0.9813	0.9362	0.6173	0.8057	0.7304	0.8544	0.8714	0.9653	0.9423
0.1683	9.3	1200	0.1685	0.8342	0.8967	0.9487	0.9695	0.7096	0.9281	0.8261	0.9382	0.9376	0.9676	0.9368	0.6100	0.8515	0.7291	0.8807	0.8726	0.9590	0.9486
0.1265	9.46	1220	0.1684	0.8278	0.8829	0.9482	0.9764	0.6761	0.9042	0.7785	0.9515	0.9278	0.9657	0.9404	0.5943	0.8480	0.7065	0.8732	0.8729	0.9595	0.9479
0.1052	9.61	1240	0.1743	0.8176	0.8969	0.9441	0.9750	0.7880	0.8907	0.7930	0.9261	0.9333	0.9721	0.9343	0.5809	0.8331	0.6799	0.8665	0.8654	0.9634	0.9441
0.1972	9.77	1260	0.2006	0.8135	0.8781	0.9385	0.9731	0.6571	0.8506	0.8227	0.9316	0.9379	0.9735	0.9315	0.5703	0.7969	0.7257	0.8386	0.8669	0.9643	0.9380
0.2727	9.92	1280	0.1952	0.8091	0.8666	0.9412	0.9714	0.6697	0.8829	0.7132	0.9561	0.9058	0.9672	0.9344	0.5762	0.8125	0.6553	0.8544	0.8714	0.9594	0.9407
0.1478	10.08	1300	0.1935	0.8153	0.8976	0.9395	0.9675	0.7368	0.8569	0.8718	0.9352	0.9506	0.9644	0.9317	0.6118	0.7976	0.6876	0.8580	0.8621	0.9584	0.9395
0.2574	10.23	1320	0.2107	0.8154	0.8888	0.9384	0.9586	0.7452	0.8745	0.8039	0.9545	0.9184	0.9668	0.9285	0.5996	0.7961	0.7035	0.8515	0.8681	0.9603	0.9385
0.9414	10.39	1340	0.1919	0.8221	0.8939	0.9428	0.9653	0.7195	0.8869	0.8314	0.9350	0.9456	0.9736	0.9319	0.5980	0.8148	0.7137	0.8644	0.8668	0.9652	0.9427
0.1415	10.54	1360	0.2000	0.8247	0.8946	0.9417	0.9687	0.7613	0.8609	0.8152	0.9407	0.9392	0.9761	0.9386	0.6259	0.8026	0.7224	0.8437	0.8722	0.9673	0.9416
0.1511	10.7	1380	0.1881	0.8269	0.9060	0.9424	0.9707	0.7995	0.8682	0.8548	0.9256	0.9477	0.9753	0.9366	0.6328	0.8126	0.7202	0.8474	0.8731	0.9654	0.9423

Framework versions

Transformers 4.37.0
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.1

peldrak/segformer-b5-cityscapes-finetuned-coastTrain

segformer-b5-cityscapes-finetuned-coastTrain

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

peldrak/
segformer-b5-cityscapes-finetuned-coastTrain