segformer-finetuned-sidewalk-10k-steps

nielsr/segformer-finetuned-sidewalk-10k-steps

segformer-finetuned-sidewalk-10k-steps is a image segmentation model from nielsr. 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 apache-2.0.

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

Downloads · 30 days

4% of all-time downloads

All-time downloads

1.6K

Public

Parameters

3.7M

530 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin15 MB · 48%

At a glance

Task: Image Segmentation
Library: transformers
License: apache-2.0
Model type: segformer
Access: Public
Created: Apr 20, 2022
Updated: Dec 24, 2024
SHA: 065f8706

Base models

nvidia/mit-b0

Spaces

daasdf/MLT6_extra

Model card

From the Hugging Face model README

segformer-finetuned-sidewalk-50-epochs

This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

Loss: 0.6350
Mean Iou: 0.3022
Mean Accuracy: 0.3724
Overall Accuracy: 0.8117
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.8240
Accuracy Flat-sidewalk: 0.8308
Accuracy Flat-crosswalk: 0.7789
Accuracy Flat-cyclinglane: 0.9052
Accuracy Flat-parkingdriveway: 0.3152
Accuracy Flat-railtrack: nan
Accuracy Flat-curb: 0.4703
Accuracy Human-person: 0.6444
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.9424
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: 0.0
Accuracy Vehicle-tramtrain: 0.0
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.7116
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.8716
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.4736
Accuracy Construction-fenceguardrail: 0.5408
Accuracy Construction-bridge: 0.0
Accuracy Construction-tunnel: nan
Accuracy Construction-stairs: 0.0048
Accuracy Object-pole: 0.4202
Accuracy Object-trafficsign: 0.0754
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.9437
Accuracy Nature-terrain: 0.8196
Accuracy Sky: 0.9525
Accuracy Void-ground: 0.0
Accuracy Void-dynamic: 0.1041
Accuracy Void-static: 0.2872
Accuracy Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.7413
Iou Flat-sidewalk: 0.7520
Iou Flat-crosswalk: 0.7629
Iou Flat-cyclinglane: 0.4453
Iou Flat-parkingdriveway: 0.2976
Iou Flat-railtrack: nan
Iou Flat-curb: 0.3701
Iou Human-person: 0.4953
Iou Human-rider: 0.0
Iou Vehicle-car: 0.7962
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: 0.0
Iou Vehicle-tramtrain: 0.0
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.4152
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.6712
Iou Construction-door: 0.0
Iou Construction-wall: 0.3749
Iou Construction-fenceguardrail: 0.4613
Iou Construction-bridge: 0.0
Iou Construction-tunnel: nan
Iou Construction-stairs: 0.0048
Iou Object-pole: 0.2337
Iou Object-trafficsign: 0.0753
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.8324
Iou Nature-terrain: 0.7277
Iou Sky: 0.9234
Iou Void-ground: 0.0
Iou Void-dynamic: 0.0913
Iou Void-static: 0.1997
Iou Void-unclear: 0.0

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: polynomial
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Flat-crosswalk	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-railtrack	Accuracy Flat-curb	Accuracy Human-person	Accuracy Vehicle-car	Accuracy Vehicle-bicycle	Accuracy Construction-building	Accuracy Construction-wall	Accuracy Construction-fenceguardrail	Accuracy Construction-tunnel	Accuracy Object-pole	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-dynamic	Accuracy Void-static	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-crosswalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Vehicle-car	Iou Vehicle-bicycle	Iou Construction-building	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Construction-tunnel	Iou Object-pole	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-dynamic	Iou Void-static
2.4745	1.85	100	1.7861	0.1056	0.1555	0.6397	nan	0.2287	0.9278	0.0	0.1406	0.0032	nan	0.0	0.0	0.7757	0.0	0.8764	0.0	0.0	nan	0.0	0.8387	0.8794	0.3057	0.0	0.0	nan	0.1931	0.6432	0.0	0.1380	0.0031	nan	0.0	0.0	0.5312	0.0	0.4482	0.0	0.0	nan	0.0	0.6323	0.4860	0.3053	0.0	0.0
1.7294	3.7	200	1.3129	0.1517	0.1996	0.7410	nan	0.7928	0.8830	0.0	0.6053	0.0089	nan	0.0	0.0	0.7837	0.0	0.8530	0.0	0.0	nan	0.0	0.9138	0.7742	0.7740	0.0	0.0	nan	0.5519	0.7788	0.0	0.5131	0.0088	nan	0.0	0.0	0.5804	0.0	0.5005	0.0	0.0	nan	0.0	0.6747	0.5247	0.7209	0.0	0.0
1.4479	5.56	300	1.1309	0.1608	0.2113	0.7588	nan	0.7973	0.9008	0.0	0.7721	0.0269	nan	0.0	0.0	0.8744	0.0	0.8581	0.0007	0.0	nan	0.0	0.8622	0.8707	0.7985	0.0	0.0	nan	0.5861	0.7816	0.0	0.5877	0.0261	nan	0.0	0.0	0.6119	0.0	0.5582	0.0007	0.0	nan	0.0	0.7024	0.5206	0.7706	0.0	0.0
1.2348	7.41	400	0.9644	0.1707	0.2170	0.7736	nan	0.8125	0.9218	0.0	0.7596	0.1081	nan	0.0000	0.0	0.9080	0.0	0.8280	0.0334	0.0	nan	0.0	0.8856	0.8260	0.8612	0.0	0.0	nan	0.6003	0.7937	0.0	0.6538	0.0997	nan	0.0000	0.0	0.6189	0.0	0.5731	0.0330	0.0	nan	0.0	0.7147	0.5601	0.8139	0.0	0.0
1.0762	9.26	500	0.8819	0.1722	0.2159	0.7748	nan	0.7512	0.9353	0.0	0.7565	0.1204	nan	0.0016	0.0	0.9115	0.0	0.8689	0.0565	0.0	nan	0.0	0.9098	0.7664	0.8303	0.0	0.0	nan	0.5993	0.7850	0.0	0.6536	0.1052	nan	0.0016	0.0	0.6377	0.0	0.5767	0.0547	0.0	nan	0.0	0.7285	0.5709	0.7984	0.0	0.0
0.9933	11.11	600	0.8347	0.1814	0.2263	0.7822	nan	0.8064	0.9111	0.0	0.7880	0.1443	nan	0.0436	0.0	0.8944	0.0	0.8970	0.1914	0.0	nan	0.0	0.9053	0.8080	0.8526	0.0	0.0	nan	0.6088	0.8045	0.0	0.6845	0.1255	nan	0.0419	0.0	0.6594	0.0	0.5548	0.1585	0.0	nan	0.0	0.7440	0.6068	0.8176	0.0	0.0
0.9424	12.96	700	0.8428	0.1824	0.2271	0.7704	nan	0.6767	0.9270	0.0475	0.7655	0.1322	nan	0.2020	0.0189	0.8410	0.0	0.9205	0.2568	0.0	nan	0.0023	0.8994	0.7347	0.8413	0.0	0.0	nan	0.5838	0.7914	0.0475	0.6091	0.1095	nan	0.1597	0.0185	0.6706	0.0	0.5131	0.1872	0.0	nan	0.0023	0.7525	0.5837	0.8077	0.0	0.0
0.8673	14.81	800	0.7934	0.2089	0.2509	0.7818	nan	0.6854	0.9394	0.7072	0.7240	0.1504	nan	0.2013	0.0186	0.9071	0.0	0.9037	0.3110	0.0	nan	0.0108	0.8990	0.7171	0.8513	0.0	0.0013	nan	0.5914	0.7755	0.6900	0.6673	0.1340	nan	0.1542	0.0183	0.6792	0.0	0.5639	0.2172	0.0	nan	0.0100	0.7615	0.6014	0.8192	0.0	0.0013
0.8126	16.67	900	0.7484	0.2268	0.2784	0.7940	nan	0.6791	0.9397	0.7812	0.8009	0.1532	nan	0.3244	0.2962	0.9018	0.0	0.8567	0.4772	0.0002	nan	0.0834	0.8992	0.8280	0.8837	0.0	0.0032	nan	0.6303	0.7968	0.7079	0.6095	0.1396	nan	0.2196	0.2638	0.7100	0.0	0.6016	0.2860	0.0002	nan	0.0570	0.7678	0.6211	0.8416	0.0	0.0032
0.7989	18.52	1000	0.7241	0.2279	0.2803	0.8018	nan	0.7224	0.9402	0.7875	0.8234	0.1793	nan	0.3763	0.1974	0.9259	0.0	0.8911	0.3994	0.0029	nan	0.0758	0.8619	0.8774	0.8854	0.0	0.0225	nan	0.6579	0.8292	0.7198	0.6924	0.1660	nan	0.2392	0.1794	0.6748	0.0	0.5766	0.2654	0.0029	nan	0.0636	0.7582	0.5994	0.8455	0.0	0.0220
0.7429	20.37	1100	0.7321	0.2276	0.2862	0.7876	nan	0.8321	0.8491	0.7958	0.8572	0.2216	nan	0.3030	0.2864	0.9456	0.0	0.8668	0.3757	0.0040	nan	0.1140	0.8839	0.8499	0.9228	0.0	0.0505	nan	0.6678	0.7848	0.7342	0.5048	0.1995	nan	0.2316	0.2463	0.6379	0.0	0.5916	0.2668	0.0040	nan	0.0820	0.7827	0.6428	0.8583	0.0	0.0465
0.7131	22.22	1200	0.7231	0.2377	0.2995	0.7870	nan	0.8306	0.8458	0.7952	0.8505	0.2218	nan	0.3614	0.5001	0.9504	0.0	0.7598	0.5317	0.0405	nan	0.1381	0.9284	0.7938	0.9110	0.0	0.1262	nan	0.7038	0.7740	0.7537	0.4538	0.1996	nan	0.2521	0.3853	0.6576	0.0	0.6157	0.3046	0.0404	nan	0.0921	0.7846	0.6383	0.8588	0.0	0.0911
0.6919	24.07	1300	0.6775	0.2361	0.2885	0.8013	nan	0.7728	0.9073	0.8010	0.8366	0.1547	nan	0.3070	0.3428	0.9272	0.0	0.8568	0.5009	0.0736	nan	0.0975	0.9297	0.7567	0.8978	0.0	0.0682	nan	0.6564	0.7929	0.6932	0.6396	0.1438	nan	0.2385	0.2888	0.6807	0.0	0.6085	0.3114	0.0729	nan	0.0803	0.7857	0.6403	0.8601	0.0	0.0610
0.68	25.93	1400	0.6321	0.2575	0.3109	0.8181	nan	0.7851	0.9362	0.8041	0.8438	0.1694	nan	0.3956	0.5626	0.9306	0.0	0.8313	0.5073	0.2728	nan	0.1741	0.9221	0.7899	0.9071	0.0	0.1157	nan	0.6781	0.8336	0.7386	0.7047	0.1564	nan	0.2789	0.4291	0.6934	0.0	0.6062	0.3305	0.2579	nan	0.1228	0.7952	0.6651	0.8631	0.0	0.0865
0.6644	27.78	1500	0.6568	0.2555	0.3132	0.8074	nan	0.7687	0.9014	0.7631	0.8302	0.1869	nan	0.4841	0.4880	0.9294	0.0002	0.8139	0.5482	0.3042	nan	0.1974	0.9225	0.8543	0.9042	0.0	0.1259	nan	0.6723	0.8030	0.7443	0.5873	0.1742	nan	0.3013	0.3813	0.7117	0.0002	0.6159	0.3289	0.2810	nan	0.1295	0.8015	0.6848	0.8665	0.0	0.0931
0.6153	29.63	1600	0.6157	0.2586	0.3131	0.8188	nan	0.8000	0.9242	0.7980	0.8445	0.1758	nan	0.4143	0.6256	0.9155	0.0007	0.8792	0.4465	0.2182	nan	0.1970	0.9111	0.8171	0.9368	0.0	0.1136	nan	0.6844	0.8212	0.7565	0.6537	0.1636	nan	0.2857	0.4354	0.7222	0.0007	0.6274	0.3217	0.2147	nan	0.1313	0.8082	0.6809	0.8737	0.0	0.0926
0.6154	31.48	1700	0.6397	0.2621	0.3204	0.8117	nan	0.8357	0.8840	0.7908	0.8465	0.2590	nan	0.4050	0.5401	0.9393	0.0105	0.8169	0.4733	0.3188	nan	0.2505	0.9181	0.8473	0.9287	0.0	0.1890	nan	0.6774	0.8042	0.7524	0.5662	0.2300	nan	0.2971	0.4050	0.6970	0.0105	0.6489	0.3454	0.3058	nan	0.1441	0.8074	0.6913	0.8820	0.0	0.1224
0.6305	33.33	1800	0.6131	0.2641	0.3212	0.8194	nan	0.8171	0.8984	0.8212	0.8462	0.2582	nan	0.5051	0.5504	0.9421	0.0221	0.8777	0.3528	0.3169	nan	0.2249	0.9203	0.8499	0.9175	0.0	0.1587	nan	0.7209	0.8195	0.7546	0.6166	0.2267	nan	0.3408	0.4000	0.6906	0.0221	0.6055	0.2823	0.3044	nan	0.1545	0.8124	0.6994	0.8799	0.0	0.1204
0.6083	35.19	1900	0.6224	0.2646	0.3182	0.8171	nan	0.7473	0.9297	0.7826	0.8269	0.2162	nan	0.4556	0.4982	0.9169	0.0865	0.9031	0.3618	0.3583	nan	0.2603	0.8966	0.8828	0.9016	0.0	0.1587	nan	0.6824	0.8210	0.7645	0.5950	0.2019	nan	0.3166	0.3895	0.7307	0.0853	0.6063	0.2860	0.3200	nan	0.1659	0.8188	0.7017	0.8695	0.0	0.1113
0.5847	37.04	2000	0.5906	0.2713	0.3209	0.8281	nan	0.7374	0.9612	0.7764	0.8195	0.2033	nan	0.4219	0.4950	0.9339	0.0960	0.8434	0.4552	0.4437	nan	0.2250	0.9315	0.8612	0.9071	0.0	0.1567	nan	0.6883	0.8311	0.7525	0.6838	0.1851	nan	0.3228	0.3780	0.7236	0.0944	0.6338	0.3408	0.3853	nan	0.1586	0.8104	0.6978	0.8800	0.0	0.1162
0.5764	38.89	2100	0.6088	0.2752	0.3225	0.8255	nan	0.7525	0.9472	0.7709	0.8441	0.2134	nan	0.3932	0.5383	0.9030	0.3470	0.9195	0.3310	0.3215	nan	0.2234	0.9289	0.7964	0.9280	0.0	0.1604	nan	0.6993	0.8276	0.7546	0.7234	0.1997	nan	0.3005	0.4222	0.7348	0.3123	0.5918	0.2787	0.3037	nan	0.1585	0.8124	0.6781	0.8844	0.0	0.1247
0.5787	40.74	2200	0.5706	0.2824	0.3351	0.8347	nan	0.8178	0.9369	0.8003	0.8511	0.2352	nan	0.4838	0.5417	0.9025	0.3689	0.8739	0.4493	0.4040	nan	0.2524	0.9422	0.8182	0.9183	0.0	0.1276	nan	0.7292	0.8432	0.7669	0.6897	0.2161	nan	0.3484	0.4230	0.7519	0.3045	0.6407	0.3373	0.3491	nan	0.1557	0.8080	0.6803	0.8850	0.0	0.1068
0.5724	42.59	2300	0.7562	0.2740	0.3479	0.7662	nan	0.8734	0.7169	0.7809	0.8847	0.2838	nan	0.3742	0.6758	0.9339	0.6048	0.8535	0.4435	0.4729	nan	0.2817	0.9149	0.8765	0.9329	0.0	0.2292	nan	0.7041	0.6683	0.7628	0.3371	0.2575	nan	0.2878	0.4639	0.7454	0.4190	0.6387	0.3357	0.3997	nan	0.1776	0.8183	0.7106	0.8911	0.0	0.1516
0.556	44.44	2400	0.7350	0.2665	0.3366	0.7813	nan	0.7897	0.7888	0.8022	0.8878	0.2389	nan	0.4270	0.4859	0.9401	0.4618	0.8866	0.3979	0.5050	nan	0.2580	0.9097	0.8627	0.9337	0.0	0.1948	nan	0.6902	0.7286	0.7779	0.3964	0.2231	nan	0.3011	0.3626	0.7078	0.3485	0.6171	0.3044	0.3372	nan	0.1812	0.8195	0.7011	0.8947	0.0	0.1378
0.5599	46.3	2500	0.5949	0.2846	0.3464	0.8215	nan	0.7919	0.9145	0.7935	0.8679	0.2189	nan	0.3795	0.5589	0.9334	0.5627	0.8536	0.4394	0.4730	nan	0.3260	0.9098	0.8344	0.9487	0.0	0.2801	nan	0.6901	0.8199	0.7749	0.5729	0.2084	nan	0.3034	0.4321	0.7422	0.4230	0.6491	0.3237	0.3989	nan	0.1963	0.8232	0.7048	0.8949	0.0	0.1489
0.5368	48.15	2600	0.6125	0.2829	0.3502	0.8211	nan	0.7798	0.9034	0.7913	0.9079	0.2587	nan	0.3407	0.6423	0.9351	0.6794	0.8554	0.3996	0.4884	nan	0.2870	0.9271	0.8698	0.9424	0.0	0.1992	nan	0.6878	0.8122	0.7578	0.5597	0.2427	nan	0.2680	0.4737	0.7517	0.3649	0.6557	0.3130	0.4117	nan	0.1847	0.8236	0.7137	0.8969	0.0	0.1361
0.5391	50.0	2700	0.5993	0.2877	0.3507	0.8242	nan	0.8174	0.8948	0.8094	0.8896	0.2730	nan	0.4105	0.5570	0.9164	0.5439	0.8772	0.5070	0.5443	nan	0.2691	0.9205	0.8660	0.8975	0.0	0.2294	nan	0.7059	0.8214	0.7578	0.5803	0.2537	nan	0.2892	0.4308	0.7548	0.4363	0.6490	0.3579	0.4224	nan	0.1927	0.8239	0.7040	0.8748	0.0	0.1516
0.5041	51.85	2800	0.5912	0.2859	0.3493	0.8264	nan	0.7593	0.9248	0.8029	0.8780	0.2945	nan	0.3718	0.6308	0.9078	0.6667	0.8945	0.3362	0.4834	nan	0.3167	0.9255	0.8641	0.9382	0.0	0.1836	nan	0.6993	0.8205	0.7232	0.5789	0.2712	nan	0.2852	0.4872	0.7747	0.3825	0.6382	0.2862	0.4138	nan	0.2019	0.8284	0.7271	0.8984	0.0	0.1316
0.5007	53.7	2900	0.6220	0.2839	0.3577	0.8134	nan	0.7302	0.8903	0.8180	0.9098	0.3134	nan	0.3521	0.6870	0.9429	0.7288	0.8340	0.5169	0.4700	nan	0.3105	0.9356	0.8318	0.9437	0.0003	0.2298	nan	0.6722	0.8034	0.7257	0.4922	0.2900	nan	0.2639	0.4741	0.7434	0.4082	0.6635	0.3690	0.4172	nan	0.1981	0.8205	0.6936	0.9015	0.0003	0.1483
0.4992	55.56	3000	0.5669	0.2928	0.3647	0.8317	nan	0.7826	0.9171	0.8018	0.9165	0.2758	nan	0.5273	0.6986	0.9410	0.6836	0.8296	0.4717	0.4595	nan	0.3613	0.9272	0.8671	0.9424	0.0017	0.2669	nan	0.7196	0.8377	0.7464	0.6016	0.2573	nan	0.3367	0.4767	0.7565	0.4237	0.6653	0.3438	0.4034	nan	0.1974	0.8287	0.7120	0.9031	0.0017	0.1565
0.5151	57.41	3100	0.6131	0.2864	0.3598	0.8169	nan	0.7793	0.9005	0.7894	0.8762	0.2508	nan	0.3852	0.6197	0.9316	0.6506	0.7819	0.5348	0.5782	nan	0.3853	0.9211	0.8624	0.9390	0.0	0.3278	nan	0.6967	0.8145	0.7436	0.5453	0.2362	nan	0.2992	0.4656	0.7549	0.4221	0.6246	0.3873	0.3923	nan	0.1937	0.8257	0.7204	0.8994	0.0	0.1417
0.4688	59.26	3200	0.7342	0.2674	0.3425	0.7758	nan	0.6724	0.8138	0.8211	0.8881	0.2106	nan	0.3435	0.4240	0.9345	0.6881	0.8684	0.4808	0.5494	nan	0.2968	0.9269	0.8322	0.9291	0.0	0.2817	nan	0.6227	0.7395	0.7654	0.4008	0.1990	nan	0.2434	0.3473	0.7526	0.3733	0.5567	0.3425	0.4056	nan	0.2033	0.8238	0.7088	0.8978	0.0	0.1748
0.4657	61.11	3300	0.7162	0.2737	0.3487	0.7884	nan	0.6859	0.8395	0.7919	0.8974	0.2306	nan	0.4086	0.6012	0.9212	0.7186	0.8738	0.4323	0.5271	nan	0.3163	0.9373	0.8107	0.9381	0.0	0.2280	nan	0.6253	0.7668	0.7584	0.4350	0.2180	nan	0.2835	0.4646	0.7649	0.3505	0.5817	0.3184	0.4275	nan	0.1989	0.8181	0.6916	0.9021	0.0	0.1529
0.4789	62.96	3400	0.6510	0.2824	0.3535	0.8065	nan	0.7245	0.8835	0.7760	0.8886	0.2720	nan	0.3709	0.6675	0.9351	0.6668	0.8450	0.4917	0.5508	nan	0.3585	0.9367	0.7684	0.9321	0.0022	0.2404	nan	0.6754	0.7938	0.7682	0.4856	0.2514	nan	0.2841	0.4779	0.7566	0.3801	0.6118	0.3623	0.4464	nan	0.1990	0.8150	0.6727	0.9029	0.0022	0.1516
0.4718	64.81	3500	0.7369	0.2741	0.3491	0.7687	nan	0.7886	0.7455	0.8159	0.8865	0.2585	nan	0.3583	0.6014	0.9362	0.6741	0.8728	0.4488	0.5138	nan	0.3533	0.9343	0.8363	0.9345	0.0002	0.2111	nan	0.6800	0.6730	0.7173	0.3412	0.2406	nan	0.2736	0.4651	0.7688	0.3688	0.6494	0.3507	0.4403	nan	0.1950	0.8287	0.7216	0.9039	0.0002	0.1536
0.4586	66.67	3600	0.7463	0.2799	0.3515	0.7620	nan	0.8497	0.6965	0.7931	0.9041	0.2737	nan	0.3983	0.5616	0.9365	0.5892	0.8439	0.5213	0.4720	nan	0.3429	0.9332	0.8690	0.9431	0.0	0.3213	nan	0.7435	0.6450	0.7808	0.3120	0.2517	nan	0.3134	0.4378	0.7305	0.4349	0.6399	0.3813	0.4243	nan	0.2097	0.8287	0.7225	0.9085	0.0	0.1926
0.4506	68.52	3700	0.6409	0.2859	0.3587	0.8030	nan	0.7887	0.8394	0.8054	0.8912	0.2518	nan	0.3799	0.6292	0.9273	0.7090	0.8655	0.4989	0.5447	nan	0.3519	0.9335	0.8362	0.9278	0.0	0.2975	nan	0.7248	0.7574	0.7649	0.4118	0.2326	nan	0.2996	0.4840	0.7856	0.3424	0.6639	0.3766	0.4576	nan	0.2055	0.8284	0.7274	0.9032	0.0	0.1823
0.4659	70.37	3800	0.6466	0.2884	0.3577	0.8081	nan	0.8256	0.8420	0.7982	0.8692	0.3484	nan	0.4035	0.4964	0.9489	0.6461	0.8281	0.5593	0.5404	nan	0.3533	0.9345	0.7861	0.9426	0.0	0.3225	nan	0.7403	0.7665	0.7649	0.4456	0.2991	nan	0.3198	0.3976	0.7512	0.4217	0.6537	0.3859	0.4470	nan	0.2219	0.8223	0.6908	0.9109	0.0	0.1898
0.4416	72.22	3900	0.6944	0.2824	0.3648	0.7953	nan	0.8073	0.8044	0.8200	0.9039	0.2713	nan	0.4385	0.6632	0.9435	0.7130	0.8448	0.5050	0.5552	nan	0.3791	0.9316	0.8332	0.9378	0.0047	0.3183	nan	0.7045	0.7445	0.6571	0.4107	0.2536	nan	0.3089	0.4711	0.7504	0.3814	0.6468	0.3800	0.4413	nan	0.2243	0.8294	0.7257	0.9078	0.0047	0.1964
0.4347	74.07	4000	0.5742	0.2960	0.3615	0.8319	nan	0.8135	0.9088	0.8067	0.8959	0.3006	nan	0.3611	0.6055	0.9354	0.6851	0.8692	0.4956	0.5065	nan	0.3493	0.9264	0.8500	0.9368	0.0018	0.3210	nan	0.7436	0.8254	0.7615	0.5609	0.2797	nan	0.3045	0.4733	0.7745	0.4006	0.6424	0.3800	0.4600	nan	0.2126	0.8296	0.7251	0.9085	0.0018	0.1876
0.4191	75.93	4100	0.6454	0.2879	0.3671	0.8068	nan	0.7757	0.8432	0.8171	0.8803	0.3169	nan	0.4971	0.6474	0.9274	0.7272	0.8520	0.4847	0.5414	nan	0.4113	0.9400	0.8335	0.9348	0.0167	0.3000	nan	0.7112	0.7615	0.6876	0.4533	0.2904	nan	0.3375	0.4768	0.7857	0.3483	0.6544	0.3636	0.4546	nan	0.2086	0.8293	0.7293	0.9093	0.0165	0.1938
0.4355	77.78	4200	0.5871	0.2915	0.3601	0.8236	nan	0.6673	0.9324	0.8063	0.8730	0.2988	nan	0.5014	0.5734	0.9480	0.6629	0.8653	0.4649	0.5559	nan	0.3890	0.9183	0.8681	0.9537	0.0088	0.2359	nan	0.6266	0.8175	0.7309	0.5730	0.2746	nan	0.3471	0.4465	0.7567	0.4103	0.6684	0.3482	0.4615	nan	0.2062	0.8356	0.7347	0.9131	0.0088	0.1686
0.431	79.63	4300	0.5778	0.2902	0.3540	0.8266	nan	0.8325	0.9042	0.7971	0.8575	0.2707	nan	0.4318	0.5731	0.9428	0.6701	0.8781	0.4081	0.5480	nan	0.3573	0.9299	0.7480	0.9397	0.0343	0.2046	nan	0.7428	0.8112	0.7719	0.5907	0.2545	nan	0.3259	0.4272	0.7505	0.4255	0.6496	0.3209	0.4384	nan	0.2061	0.8142	0.6646	0.9118	0.0338	0.1477
0.4105	81.48	4400	0.7355	0.2837	0.3547	0.7802	nan	0.8194	0.7548	0.8125	0.9004	0.2421	nan	0.4411	0.5260	0.9344	0.6628	0.9003	0.4114	0.5457	nan	0.3720	0.9386	0.8336	0.9269	0.0905	0.2364	nan	0.7295	0.6964	0.7754	0.3477	0.2325	nan	0.3336	0.4069	0.7641	0.4284	0.6483	0.3512	0.4444	nan	0.2140	0.8260	0.7200	0.9047	0.0883	0.1667
0.4102	83.33	4500	0.6431	0.2832	0.3550	0.8023	nan	0.6173	0.8926	0.8233	0.8684	0.3015	nan	0.4774	0.5853	0.9435	0.7118	0.8678	0.4544	0.5288	nan	0.3435	0.9438	0.7934	0.9323	0.0264	0.2495	nan	0.5793	0.7784	0.7849	0.5220	0.2750	nan	0.3433	0.4263	0.7478	0.3651	0.6236	0.3489	0.4347	nan	0.2243	0.8184	0.6879	0.9082	0.0258	0.1674
0.4172	85.19	4600	0.6988	0.2875	0.3537	0.7940	nan	0.7505	0.8194	0.8168	0.9128	0.2640	nan	0.4022	0.4961	0.9391	0.6453	0.8769	0.4600	0.5182	nan	0.3740	0.9378	0.8263	0.9455	0.0900	0.2436	nan	0.7048	0.7401	0.7654	0.3938	0.2454	nan	0.2874	0.3973	0.7572	0.4779	0.6427	0.3531	0.4565	nan	0.2402	0.8333	0.7320	0.9149	0.0880	0.1706
0.3885	87.04	4700	0.5978	0.2953	0.3647	0.8175	nan	0.8142	0.8718	0.8027	0.8554	0.3059	nan	0.3787	0.5867	0.9403	0.6845	0.8471	0.5315	0.5788	nan	0.3874	0.9354	0.8156	0.9494	0.1221	0.2636	nan	0.7263	0.7825	0.7874	0.4784	0.2859	nan	0.2981	0.4480	0.7604	0.3820	0.6694	0.3781	0.4545	nan	0.2385	0.8301	0.7216	0.9144	0.1131	0.1798
0.3949	88.89	4800	0.5747	0.2961	0.3643	0.8282	nan	0.8129	0.8976	0.8121	0.8713	0.2894	nan	0.4694	0.5562	0.9391	0.6947	0.8395	0.5260	0.5481	nan	0.3852	0.9428	0.8221	0.9365	0.0559	0.2580	nan	0.7394	0.8130	0.7924	0.5533	0.2658	nan	0.3447	0.4378	0.7620	0.3851	0.6633	0.3722	0.4533	nan	0.2184	0.8217	0.7122	0.9124	0.0534	0.1742
0.4158	90.74	4900	0.6449	0.2916	0.3657	0.8070	nan	0.8043	0.8271	0.8157	0.9192	0.3073	nan	0.4380	0.6344	0.9340	0.7171	0.8572	0.5188	0.5406	nan	0.3852	0.9420	0.8552	0.9459	0.0450	0.2148	nan	0.6975	0.7564	0.7902	0.4563	0.2853	nan	0.3171	0.4654	0.7879	0.3571	0.6623	0.3819	0.4583	nan	0.2243	0.8302	0.7431	0.9150	0.0421	0.1602
0.3856	92.59	5000	0.7492	0.2796	0.3559	0.7680	nan	0.8020	0.7250	0.8248	0.9139	0.2500	nan	0.3621	0.5930	0.9411	0.6964	0.9036	0.3460	0.5234	nan	0.4271	0.9255	0.8871	0.9524	0.0666	0.2471	nan	0.6954	0.6697	0.7878	0.3256	0.2365	nan	0.2864	0.4452	0.7724	0.3838	0.6413	0.2968	0.4239	nan	0.2271	0.8382	0.7554	0.9171	0.0624	0.1808
0.3915	94.44	5100	0.6402	0.2893	0.3608	0.8012	nan	0.7614	0.8406	0.7898	0.9029	0.3080	nan	0.3857	0.6328	0.9373	0.7010	0.8626	0.5045	0.5235	nan	0.3802	0.9442	0.7561	0.9401	0.1133	0.2603	nan	0.6850	0.7546	0.7750	0.4451	0.2827	nan	0.3049	0.4715	0.7694	0.3810	0.6626	0.3832	0.4394	nan	0.2214	0.8125	0.6725	0.9138	0.1034	0.1797
0.3732	96.3	5200	0.7308	0.2840	0.3598	0.7795	nan	0.7534	0.7741	0.8137	0.9035	0.2614	nan	0.4308	0.6431	0.9315	0.7293	0.8884	0.4166	0.5225	nan	0.3992	0.9329	0.8517	0.9519	0.0756	0.2354	nan	0.6723	0.6942	0.7836	0.3665	0.2474	nan	0.3333	0.4669	0.7857	0.3545	0.6375	0.3443	0.4311	nan	0.2377	0.8346	0.7428	0.9173	0.0659	0.1722

The model card is truncated. Read the rest on Hugging Face.

Task: Image Segmentation
Library: transformers
Type: segformer
License: apache-2.0
Created: Apr 20, 2022
Updated: Dec 24, 2024