This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the peldrak/coastTrain dataset. It achieves the following results on the evaluation set:

Loss: 0.3412
Mean Iou: 0.7737
Mean Accuracy: 0.8545
Overall Accuracy: 0.9252
Accuracy Water: 0.9680
Accuracy Whitewater: 0.6271
Accuracy Sediment: 0.8745
Accuracy Other Natural Terrain: 0.8030
Accuracy Vegetation: 0.9108
Accuracy Development: 0.8296
Accuracy Unknown: 0.9682
Iou Water: 0.9285
Iou Whitewater: 0.5294
Iou Sediment: 0.8047
Iou Other Natural Terrain: 0.6748
Iou Vegetation: 0.8430
Iou Development: 0.7686
Iou Unknown: 0.8673
F1 Score: 0.9245

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
1.6133	0.16	20	1.4728	0.4356	0.5582	0.7600	0.8440	0.0000	0.5434	0.2624	0.8192	0.5884	0.8502	0.7387	0.0000	0.4669	0.1171	0.5786	0.4642	0.6833	0.7532
1.4431	0.31	40	1.0911	0.4768	0.5735	0.8206	0.9076	0.0	0.7765	0.0006	0.9006	0.6686	0.7608	0.8394	0.0	0.5832	0.0006	0.6596	0.4954	0.7597	0.8114
1.5275	0.47	60	0.7874	0.5222	0.5966	0.8585	0.9553	0.0	0.7752	0.0	0.9101	0.6629	0.8727	0.8565	0.0	0.6318	0.0	0.7222	0.5763	0.8688	0.8454
1.1583	0.62	80	0.7113	0.5128	0.6001	0.8440	0.8830	0.0	0.8460	0.0	0.9323	0.6588	0.8809	0.8283	0.0	0.5762	0.0	0.7216	0.5873	0.8762	0.8348
0.9739	0.78	100	0.6059	0.5450	0.6229	0.8733	0.9534	0.0	0.8246	0.0	0.8876	0.8146	0.8803	0.8703	0.0	0.6770	0.0	0.7506	0.6408	0.8760	0.8615
0.838	0.93	120	0.5250	0.5557	0.6273	0.8800	0.9679	0.0	0.8549	0.0	0.8665	0.8087	0.8934	0.8677	0.0	0.6840	0.0	0.7703	0.6847	0.8830	0.8674
0.5815	1.09	140	0.4973	0.5528	0.6247	0.8788	0.9630	0.0	0.7933	0.0	0.9010	0.8217	0.8939	0.8812	0.0	0.6938	0.0	0.7379	0.6730	0.8837	0.8663
0.5949	1.24	160	0.4460	0.5538	0.6314	0.8813	0.9621	0.0	0.8044	0.0	0.8840	0.8692	0.9000	0.8930	0.0	0.7062	0.0	0.7445	0.6487	0.8843	0.8695
1.089	1.4	180	0.4080	0.5666	0.6331	0.8889	0.9680	0.0	0.8240	0.0	0.9200	0.8256	0.8940	0.8902	0.0	0.7323	0.0	0.7603	0.6986	0.8846	0.8762
0.959	1.55	200	0.3853	0.5675	0.6403	0.8901	0.9611	0.0	0.8645	0.0	0.8983	0.8616	0.8964	0.8975	0.0	0.7192	0.0	0.7672	0.7035	0.8848	0.8780
0.577	1.71	220	0.3735	0.5683	0.6338	0.8902	0.9760	0.0	0.7882	0.0009	0.9125	0.8542	0.9047	0.8780	0.0	0.6862	0.0009	0.8013	0.7191	0.8927	0.8769
0.9331	1.86	240	0.3605	0.5705	0.6464	0.8943	0.9648	0.0	0.8650	0.0	0.8805	0.8960	0.9181	0.9010	0.0	0.7210	0.0	0.7994	0.6841	0.8879	0.8823
0.5132	2.02	260	0.3680	0.5826	0.6573	0.8884	0.9366	0.0	0.8773	0.1041	0.9113	0.8567	0.9152	0.8694	0.0	0.6984	0.1041	0.8038	0.7009	0.9013	0.8784
0.3977	2.17	280	0.3233	0.5814	0.6498	0.9018	0.9623	0.0	0.8900	0.0	0.9162	0.8487	0.9313	0.9052	0.0	0.7370	0.0	0.8210	0.7242	0.8824	0.8891
0.6267	2.33	300	0.3394	0.5998	0.6646	0.8980	0.9735	0.0	0.8603	0.1726	0.9086	0.8262	0.9110	0.8862	0.0	0.7533	0.1726	0.8293	0.7044	0.8531	0.8871
0.7394	2.48	320	0.3483	0.5776	0.6508	0.8996	0.9566	0.0	0.9175	0.0028	0.9041	0.8487	0.9261	0.9054	0.0	0.7360	0.0028	0.8317	0.7110	0.8563	0.8874
0.4013	2.64	340	0.3393	0.5746	0.6458	0.8987	0.9734	0.0	0.8959	0.0001	0.8831	0.8420	0.9264	0.9071	0.0	0.7357	0.0001	0.8252	0.7013	0.8525	0.8859
0.3434	2.79	360	0.3745	0.6263	0.6907	0.9002	0.9516	0.0003	0.8148	0.3544	0.9583	0.8127	0.9431	0.9068	0.0003	0.7490	0.3541	0.7943	0.7221	0.8576	0.8911
0.5902	2.95	380	0.3275	0.6068	0.6696	0.9005	0.9722	0.0	0.8696	0.1949	0.9158	0.8191	0.9156	0.8941	0.0	0.7545	0.1948	0.8224	0.7303	0.8512	0.8898
0.2954	3.1	400	0.3389	0.6191	0.6862	0.9008	0.9662	0.0020	0.8410	0.3092	0.9207	0.8331	0.9316	0.9074	0.0020	0.7641	0.3056	0.8117	0.7045	0.8386	0.8913
0.4758	3.26	420	0.3122	0.6064	0.6709	0.9030	0.9682	0.0	0.9069	0.1837	0.9142	0.7957	0.9279	0.9092	0.0	0.7655	0.1834	0.8172	0.7227	0.8466	0.8923
0.6415	3.41	440	0.3109	0.6555	0.7172	0.9101	0.9728	0.0017	0.9050	0.4656	0.8885	0.8320	0.9550	0.9063	0.0017	0.7957	0.4534	0.8265	0.7512	0.8539	0.9014
0.5413	3.57	460	0.2993	0.6420	0.7045	0.9057	0.9737	0.0036	0.8782	0.4118	0.8932	0.8221	0.9487	0.9047	0.0036	0.7774	0.4103	0.8169	0.7262	0.8553	0.8967
0.2761	3.72	480	0.2802	0.6678	0.7309	0.9144	0.9619	0.0191	0.9168	0.5126	0.9190	0.8330	0.9542	0.9143	0.0191	0.7850	0.4981	0.8452	0.7427	0.8699	0.9067
0.3451	3.88	500	0.3261	0.6455	0.7056	0.9075	0.9695	0.0220	0.8345	0.4049	0.9307	0.8256	0.9520	0.9085	0.0220	0.7754	0.3995	0.8173	0.7375	0.8583	0.8988
0.2361	4.03	520	0.3118	0.6677	0.7285	0.9098	0.9683	0.0204	0.8678	0.5447	0.9117	0.8323	0.9547	0.9089	0.0204	0.7750	0.5374	0.8235	0.7509	0.8580	0.9019
0.2776	4.19	540	0.2765	0.6472	0.7121	0.9109	0.9670	0.0359	0.9025	0.3801	0.9057	0.8431	0.9507	0.9182	0.0358	0.7698	0.3764	0.8356	0.7324	0.8619	0.9029
0.3823	4.34	560	0.3514	0.6558	0.7181	0.9111	0.9592	0.0525	0.9005	0.4047	0.9183	0.8321	0.9591	0.9127	0.0523	0.7740	0.4016	0.8309	0.7587	0.8599	0.9036
0.747	4.5	580	0.3340	0.6764	0.7382	0.9130	0.9680	0.0617	0.9125	0.5379	0.8957	0.8362	0.9556	0.9146	0.0614	0.7948	0.5336	0.8279	0.7439	0.8588	0.9061
0.343	4.65	600	0.3065	0.6853	0.7474	0.9139	0.9647	0.0666	0.8925	0.5978	0.9128	0.8429	0.9542	0.9171	0.0664	0.7909	0.5886	0.8287	0.7436	0.8621	0.9074
0.2358	4.81	620	0.3229	0.6916	0.7504	0.9144	0.9719	0.0880	0.8895	0.6099	0.8976	0.8312	0.9651	0.9108	0.0874	0.7855	0.6005	0.8332	0.7563	0.8674	0.9080
0.3649	4.96	640	0.3026	0.7034	0.7644	0.9151	0.9686	0.1640	0.8804	0.6447	0.9148	0.8220	0.9562	0.9129	0.1617	0.7918	0.6134	0.8333	0.7502	0.8606	0.9101
0.201	5.12	660	0.2903	0.7089	0.7678	0.9194	0.9663	0.1156	0.9208	0.6627	0.9029	0.8379	0.9684	0.9153	0.1148	0.8061	0.6444	0.8451	0.7716	0.8648	0.9138
1.1077	5.27	680	0.2596	0.7189	0.7752	0.9221	0.9746	0.2053	0.8895	0.6365	0.9112	0.8378	0.9713	0.9192	0.2016	0.8009	0.6199	0.8479	0.7545	0.8883	0.9176
0.2368	5.43	700	0.2479	0.7271	0.7986	0.9213	0.9727	0.3222	0.8877	0.7104	0.9135	0.8259	0.9580	0.9184	0.3114	0.8003	0.5698	0.8370	0.7636	0.8894	0.9184
0.2576	5.58	720	0.3153	0.7171	0.7772	0.9173	0.9772	0.1874	0.8721	0.7060	0.9007	0.8399	0.9570	0.9152	0.1839	0.7926	0.6778	0.8408	0.7455	0.8637	0.9127
0.3873	5.74	740	0.3024	0.7395	0.7985	0.9212	0.9687	0.3518	0.9369	0.6845	0.9092	0.7793	0.9589	0.9208	0.3370	0.8203	0.6516	0.8367	0.7479	0.8622	0.9184
0.2281	5.89	760	0.2583	0.7502	0.8104	0.9227	0.9699	0.4031	0.9094	0.7032	0.9107	0.8210	0.9553	0.9229	0.3810	0.8150	0.6723	0.8431	0.7563	0.8606	0.9205
0.2202	6.05	780	0.3227	0.7612	0.8314	0.9205	0.9525	0.5297	0.9437	0.7038	0.8889	0.8332	0.9677	0.9253	0.4917	0.7900	0.6693	0.8338	0.7585	0.8599	0.9197
0.292	6.2	800	0.2558	0.7614	0.8266	0.9283	0.9710	0.4248	0.8964	0.7571	0.9185	0.8553	0.9629	0.9220	0.4076	0.8202	0.6464	0.8473	0.7915	0.8946	0.9264
0.1304	6.36	820	0.2441	0.7386	0.8067	0.9244	0.9764	0.3536	0.9007	0.6908	0.9019	0.8823	0.9412	0.9109	0.3357	0.8088	0.5524	0.8277	0.8106	0.9244	0.9221
0.1664	6.51	840	0.2565	0.7685	0.8302	0.9282	0.9728	0.4257	0.8564	0.7880	0.9277	0.8793	0.9614	0.9244	0.3944	0.8006	0.7192	0.8439	0.7941	0.9032	0.9263
0.4424	6.67	860	0.2667	0.7808	0.8611	0.9332	0.9680	0.5948	0.9061	0.7883	0.9039	0.8938	0.9727	0.9362	0.5495	0.8165	0.6008	0.8453	0.8086	0.9083	0.9326
0.3753	6.82	880	0.2585	0.7353	0.8117	0.9236	0.9765	0.4103	0.8985	0.7286	0.9126	0.7922	0.9633	0.9221	0.3532	0.8025	0.5636	0.8374	0.7631	0.9054	0.9215
0.2642	6.98	900	0.2955	0.7734	0.8443	0.9247	0.9586	0.5628	0.9093	0.7641	0.9085	0.8357	0.9708	0.9240	0.5068	0.8133	0.6949	0.8429	0.7634	0.8685	0.9238
0.5008	7.13	920	0.3048	0.7703	0.8430	0.9262	0.9712	0.5688	0.8871	0.7660	0.9077	0.8329	0.9676	0.9260	0.5015	0.8170	0.6626	0.8502	0.7697	0.8651	0.9251
0.169	7.29	940	0.2969	0.7778	0.8528	0.9252	0.9644	0.6690	0.9288	0.7274	0.8966	0.8274	0.9559	0.9263	0.5909	0.8081	0.6410	0.8444	0.7733	0.8608	0.9246
0.2429	7.44	960	0.3385	0.7906	0.8551	0.9283	0.9626	0.6420	0.9047	0.7576	0.9131	0.8281	0.9776	0.9322	0.5836	0.8160	0.7156	0.8415	0.7752	0.8701	0.9276
0.1542	7.6	980	0.3259	0.7882	0.8574	0.9264	0.9646	0.6734	0.9031	0.7509	0.9007	0.8403	0.9686	0.9298	0.5949	0.8138	0.7056	0.8366	0.7707	0.8663	0.9258
0.1378	7.75	1000	0.3257	0.7815	0.8589	0.9249	0.9675	0.6849	0.8784	0.7864	0.9151	0.8219	0.9580	0.9265	0.5842	0.8076	0.6769	0.8375	0.7767	0.8610	0.9242
0.2144	7.91	1020	0.3347	0.7889	0.8561	0.9276	0.9578	0.6604	0.9128	0.7455	0.9176	0.8261	0.9729	0.9317	0.5932	0.8189	0.7046	0.8416	0.7636	0.8688	0.9270
0.1011	8.06	1040	0.3099	0.7831	0.8546	0.9267	0.9655	0.6394	0.8900	0.7685	0.9091	0.8402	0.9695	0.9289	0.5594	0.8137	0.6999	0.8434	0.7682	0.8685	0.9260
0.1543	8.22	1060	0.3291	0.7921	0.8667	0.9294	0.9667	0.7434	0.9074	0.7434	0.9025	0.8251	0.9781	0.9357	0.6127	0.8145	0.6936	0.8460	0.7703	0.8720	0.9289
0.2624	8.37	1080	0.3284	0.7884	0.8593	0.9247	0.9644	0.6802	0.8663	0.7915	0.9233	0.8317	0.9580	0.9297	0.5924	0.8031	0.7343	0.8332	0.7633	0.8631	0.9241
0.1817	8.53	1100	0.3187	0.7848	0.8557	0.9255	0.9705	0.6361	0.9041	0.7843	0.8918	0.8460	0.9573	0.9265	0.5584	0.8179	0.7210	0.8371	0.7698	0.8626	0.9248
0.1702	8.68	1120	0.3412	0.7737	0.8545	0.9252	0.9680	0.6271	0.8745	0.8030	0.9108	0.8296	0.9682	0.9285	0.5294	0.8047	0.6748	0.8430	0.7686	0.8673	0.9245

Framework versions

Transformers 4.37.0
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.1

peldrak/segformer-b5-ade-finetuned-coastTrain

segformer-b5-ade-finetuned-coastTrain

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

peldrak/
segformer-b5-ade-finetuned-coastTrain