segformer-b4-ade-finetuned-grCoastline

peldrak/segformer-b4-ade-finetuned-grCoastline

segformer-b4-ade-finetuned-grCoastline is a image segmentation model from peldrak. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: Mar 6, 2024
Updated: Mar 6, 2024
SHA: ac885c3e

Base models

nvidia/segformer-b4-finetuned-ade-512-512

Model card

From the Hugging Face model README

segformer-b4-ade-finetuned-grCoastline

This model is a fine-tuned version of nvidia/segformer-b4-finetuned-ade-512-512 on the peldrak/grCoastline_512 dataset. It achieves the following results on the evaluation set:

Loss: 0.2116
Mean Iou: 0.7207
Mean Accuracy: 0.7864
Overall Accuracy: 0.9441
Accuracy Water: 0.9915
Accuracy Whitewater: 0.0
Accuracy Sediment: 0.9083
Accuracy Other Natural Terrain: 0.8596
Accuracy Vegetation: 0.8771
Accuracy Development: 0.8701
Accuracy Unknown: 0.9983
Iou Water: 0.9580
Iou Whitewater: 0.0
Iou Sediment: 0.8691
Iou Other Natural Terrain: 0.7108
Iou Vegetation: 0.8236
Iou Development: 0.6878
Iou Unknown: 0.9957
F1 Score: 0.9437

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 Sediment	Accuracy Other Natural Terrain	Accuracy Vegetation	Accuracy Development	Accuracy Unknown	Iou Water	Iou Sediment	Iou Other Natural Terrain	Iou Vegetation	Iou Development	Iou Unknown	F1 Score
1.4532	0.24	20	1.3435	0.4211	0.5264	0.7441	0.7775	0.2537	0.5410	0.7502	0.3664	0.9961	0.6995	0.2230	0.3135	0.4349	0.3008	0.9758	0.7366
1.0467	0.49	40	0.8901	0.4799	0.5609	0.8306	0.9261	0.6581	0.3109	0.9513	0.0809	0.9992	0.8704	0.5553	0.3049	0.5627	0.0804	0.9860	0.8109
0.9494	0.73	60	0.6358	0.5209	0.5972	0.8680	0.9788	0.8070	0.4142	0.9622	0.0220	0.9964	0.9044	0.6693	0.4008	0.6578	0.0220	0.9921	0.8452
0.6118	0.98	80	0.5235	0.5454	0.6191	0.8834	0.9836	0.8558	0.5071	0.9635	0.0283	0.9951	0.9172	0.6969	0.4748	0.7090	0.0282	0.9914	0.8625
0.5458	1.22	100	0.4739	0.5846	0.6514	0.8875	0.9514	0.8431	0.5217	0.9639	0.2829	0.9967	0.9258	0.7593	0.4726	0.6684	0.2728	0.9930	0.8793
0.5935	1.46	120	0.3786	0.6090	0.6793	0.9055	0.9774	0.9195	0.7271	0.8854	0.2475	0.9980	0.9221	0.7820	0.5782	0.7474	0.2415	0.9921	0.8969
0.616	1.71	140	0.3630	0.6382	0.7043	0.9141	0.9845	0.9106	0.7205	0.8929	0.4243	0.9975	0.9132	0.7515	0.6445	0.7799	0.3859	0.9920	0.9090
0.6891	1.95	160	0.3563	0.6249	0.6897	0.9049	0.9912	0.8575	0.5156	0.9541	0.5139	0.9955	0.9010	0.7908	0.4895	0.7447	0.4577	0.9906	0.8978
0.3915	2.2	180	0.3039	0.6420	0.7094	0.9122	0.9690	0.8829	0.7779	0.8809	0.4559	0.9990	0.9296	0.8211	0.5859	0.7445	0.4225	0.9901	0.9093
0.478	2.44	200	0.2957	0.6661	0.7380	0.9201	0.9884	0.8179	0.8009	0.8854	0.6765	0.9973	0.9150	0.7697	0.6348	0.7986	0.5532	0.9916	0.9186
0.2891	2.68	220	0.2687	0.6781	0.7439	0.9258	0.9890	0.9161	0.7389	0.8977	0.6776	0.9881	0.9253	0.8384	0.6330	0.7898	0.5726	0.9874	0.9238
0.3432	2.93	240	0.2629	0.6725	0.7379	0.9246	0.9752	0.9444	0.7407	0.8825	0.6246	0.9976	0.9308	0.8389	0.6136	0.7804	0.5506	0.9935	0.9224
0.4379	3.17	260	0.2528	0.6787	0.7360	0.9283	0.9837	0.9170	0.7116	0.9280	0.6139	0.9974	0.9422	0.8382	0.6218	0.7870	0.5687	0.9931	0.9255
0.2129	3.41	280	0.2250	0.6775	0.7401	0.9264	0.9911	0.9001	0.6653	0.9301	0.7006	0.9936	0.9330	0.8456	0.6021	0.7849	0.5853	0.9918	0.9237
0.1941	3.66	300	0.2191	0.6943	0.7621	0.9336	0.9770	0.9426	0.8365	0.8619	0.7193	0.9974	0.9377	0.8296	0.6934	0.8076	0.5981	0.9938	0.9325
0.3357	3.9	320	0.2315	0.6841	0.7582	0.9267	0.9782	0.8671	0.7910	0.8801	0.7948	0.9961	0.9322	0.8089	0.6298	0.8011	0.6240	0.9926	0.9261
0.2456	4.15	340	0.2462	0.6904	0.7586	0.9303	0.9895	0.9229	0.6965	0.8897	0.8146	0.9970	0.9339	0.8563	0.6091	0.7915	0.6476	0.9947	0.9284
0.3712	4.39	360	0.2366	0.6918	0.7696	0.9307	0.9848	0.8491	0.8504	0.8689	0.8389	0.9951	0.9514	0.8085	0.6517	0.8102	0.6272	0.9935	0.9311
0.4034	4.63	380	0.2442	0.6854	0.7691	0.9254	0.9799	0.9505	0.8457	0.7848	0.8289	0.9940	0.9361	0.8287	0.6650	0.7533	0.6215	0.9930	0.9248
0.1794	4.88	400	0.2162	0.6951	0.7555	0.9338	0.9831	0.8868	0.7703	0.9249	0.7271	0.9967	0.9449	0.8392	0.6450	0.8101	0.6320	0.9945	0.9324
0.3036	5.12	420	0.2356	0.6869	0.7514	0.9303	0.9889	0.9184	0.6697	0.9145	0.7701	0.9983	0.9289	0.8442	0.6064	0.8075	0.6280	0.9932	0.9277
0.1948	5.37	440	0.2138	0.6963	0.7661	0.9333	0.9801	0.8889	0.8187	0.8823	0.7944	0.9982	0.9443	0.8363	0.6453	0.8165	0.6381	0.9939	0.9330
0.5839	5.61	460	0.2248	0.6998	0.7699	0.9350	0.9913	0.9310	0.7673	0.8695	0.8340	0.9966	0.9330	0.8511	0.6645	0.8143	0.6420	0.9936	0.9338
0.2039	5.85	480	0.2311	0.6949	0.7695	0.9300	0.9801	0.8510	0.8609	0.8619	0.8362	0.9967	0.9417	0.8106	0.6365	0.8076	0.6740	0.9942	0.9304
0.1818	6.1	500	0.2297	0.7037	0.7794	0.9338	0.9788	0.9420	0.8915	0.8103	0.8374	0.9955	0.9417	0.8651	0.6649	0.7871	0.6730	0.9942	0.9338
0.1637	6.34	520	0.1984	0.7105	0.7757	0.9389	0.9817	0.9503	0.8314	0.8634	0.8062	0.9969	0.9409	0.8601	0.6899	0.8123	0.6756	0.9946	0.9380
0.2034	6.59	540	0.1970	0.7170	0.7771	0.9426	0.9875	0.8953	0.8225	0.9205	0.8200	0.9939	0.9478	0.8602	0.7085	0.8317	0.6781	0.9926	0.9418
0.2673	6.83	560	0.2055	0.7111	0.7800	0.9400	0.9907	0.9323	0.8022	0.8739	0.8651	0.9957	0.9366	0.8675	0.6941	0.8262	0.6591	0.9939	0.9392
0.2446	7.07	580	0.2391	0.6769	0.7454	0.9286	0.9829	0.9409	0.9085	0.8298	0.5581	0.9979	0.9531	0.8470	0.6501	0.7880	0.5054	0.9944	0.9274
0.1747	7.32	600	0.2450	0.6993	0.7642	0.9351	0.9872	0.9200	0.7115	0.9121	0.8223	0.9967	0.9458	0.8690	0.6249	0.8060	0.6552	0.9939	0.9335
0.1262	7.56	620	0.2595	0.6955	0.7652	0.9315	0.9878	0.9453	0.7199	0.8657	0.8406	0.9970	0.9399	0.8588	0.6152	0.7856	0.6745	0.9946	0.9298
0.1223	7.8	640	0.2334	0.7028	0.7704	0.9339	0.9861	0.9214	0.8069	0.8572	0.8234	0.9977	0.9527	0.8672	0.6354	0.7841	0.6854	0.9948	0.9334
0.0915	8.05	660	0.2561	0.6879	0.7660	0.9258	0.9905	0.8363	0.8988	0.8221	0.8176	0.9967	0.9483	0.8053	0.6239	0.7819	0.6615	0.9946	0.9266
0.1095	8.29	680	0.2018	0.7179	0.7813	0.9431	0.9851	0.9306	0.7844	0.9086	0.8650	0.9950	0.9425	0.8742	0.6973	0.8350	0.6831	0.9933	0.9421
0.196	8.54	700	0.2125	0.7115	0.7834	0.9392	0.9800	0.9376	0.8437	0.8523	0.8722	0.9979	0.9515	0.8737	0.6855	0.8046	0.6707	0.9947	0.9389
0.1548	8.78	720	0.1893	0.7261	0.7833	0.9467	0.9830	0.9370	0.8452	0.9035	0.8170	0.9973	0.9545	0.8776	0.7329	0.8299	0.6936	0.9944	0.9458
0.1376	9.02	740	0.2467	0.7101	0.7706	0.9394	0.9868	0.9268	0.7615	0.9067	0.8150	0.9977	0.9536	0.8685	0.6624	0.8077	0.6831	0.9952	0.9381
0.1569	9.27	760	0.2038	0.7184	0.7823	0.9432	0.9904	0.9064	0.8209	0.8961	0.8651	0.9976	0.9505	0.8672	0.7048	0.8265	0.6845	0.9952	0.9425
0.1633	9.51	780	0.1953	0.7213	0.7820	0.9443	0.9882	0.9093	0.8436	0.8986	0.8371	0.9975	0.9499	0.8690	0.7129	0.8304	0.6919	0.9950	0.9436
0.1501	9.76	800	0.2605	0.7013	0.7688	0.9352	0.9799	0.8636	0.8899	0.8816	0.7689	0.9975	0.9573	0.8129	0.6623	0.8220	0.6595	0.9950	0.9353
0.2376	10.0	820	0.2071	0.7186	0.7850	0.9438	0.9922	0.9306	0.8279	0.8795	0.8683	0.9962	0.9505	0.8731	0.7145	0.8282	0.6695	0.9947	0.9431
0.0856	10.24	840	0.2190	0.7113	0.7689	0.9410	0.9878	0.9119	0.8175	0.9127	0.7559	0.9964	0.9583	0.8737	0.6801	0.8152	0.6568	0.9948	0.9401
0.2257	10.49	860	0.2133	0.7124	0.7847	0.9402	0.9871	0.9328	0.8304	0.8554	0.8895	0.9980	0.9565	0.8557	0.7005	0.8108	0.6686	0.9948	0.9398
0.2022	10.73	880	0.2517	0.7030	0.7746	0.9355	0.9845	0.8451	0.8379	0.8945	0.8631	0.9973	0.9514	0.8109	0.6656	0.8229	0.6754	0.9949	0.9353
0.0923	10.98	900	0.2118	0.7122	0.7717	0.9416	0.9863	0.8904	0.8660	0.9057	0.7555	0.9979	0.9563	0.8418	0.7016	0.8320	0.6582	0.9952	0.9409
0.1639	11.22	920	0.1961	0.7244	0.7817	0.9471	0.9835	0.9354	0.8432	0.9118	0.8013	0.9967	0.9584	0.8649	0.7337	0.8397	0.6790	0.9951	0.9462
0.0676	11.46	940	0.2238	0.7135	0.7829	0.9421	0.9885	0.9482	0.7915	0.8765	0.8784	0.9973	0.9503	0.8653	0.7000	0.8282	0.6555	0.9952	0.9413
0.056	11.71	960	0.1908	0.7284	0.7848	0.9478	0.9862	0.9206	0.8434	0.9143	0.8319	0.9973	0.9583	0.8724	0.7308	0.8375	0.7048	0.9948	0.9470
0.1205	11.95	980	0.2000	0.7248	0.7914	0.9457	0.9844	0.9377	0.8256	0.8807	0.9132	0.9986	0.9581	0.8681	0.7249	0.8265	0.7016	0.9947	0.9450
0.0429	12.2	1000	0.1937	0.7285	0.7865	0.9476	0.9856	0.9298	0.8327	0.9064	0.8529	0.9982	0.9590	0.8687	0.7326	0.8335	0.7108	0.9952	0.9468
0.0992	12.44	1020	0.2081	0.7225	0.7858	0.9448	0.9863	0.9394	0.8478	0.8780	0.8522	0.9972	0.9576	0.8668	0.7191	0.8230	0.6955	0.9953	0.9441
0.089	12.68	1040	0.2044	0.7243	0.7812	0.9466	0.9832	0.9240	0.8403	0.9128	0.8095	0.9987	0.9561	0.8724	0.7274	0.8351	0.6844	0.9949	0.9457
0.1252	12.93	1060	0.2146	0.7138	0.7750	0.9417	0.9824	0.9125	0.8768	0.8895	0.7662	0.9973	0.9573	0.8521	0.6993	0.8267	0.6659	0.9950	0.9412
0.3201	13.17	1080	0.2169	0.7197	0.7849	0.9439	0.9796	0.9489	0.8613	0.8713	0.8352	0.9977	0.9580	0.8636	0.7225	0.8209	0.6774	0.9955	0.9434
0.101	13.41	1100	0.2023	0.7268	0.7848	0.9482	0.9871	0.9314	0.8350	0.9111	0.8315	0.9978	0.9587	0.8702	0.7416	0.8412	0.6806	0.9952	0.9473
0.0522	13.66	1120	0.2116	0.7207	0.7864	0.9441	0.9915	0.9083	0.8596	0.8771	0.8701	0.9983	0.9580	0.8691	0.7108	0.8236	0.6878	0.9957	0.9437

Framework versions

Transformers 4.38.1
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Mar 6, 2024
Updated: Mar 6, 2024