segformer-finetuned-sidewalk-10k-steps

qubvel-hf/segformer-finetuned-sidewalk-10k-steps

segformer-finetuned-sidewalk-10k-steps is a image segmentation model from qubvel-hf. 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: Apr 9, 2024
Updated: Apr 9, 2024
SHA: 8e70e320

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

segformer-finetuned-sidewalk-10k-steps

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: 0.5453
Mean Iou: 0.3123
Mean Accuracy: 0.3821
Overall Accuracy: 0.8406
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.8220
Accuracy Flat-sidewalk: 0.9387
Accuracy Flat-crosswalk: 0.6663
Accuracy Flat-cyclinglane: 0.7911
Accuracy Flat-parkingdriveway: 0.3209
Accuracy Flat-railtrack: nan
Accuracy Flat-curb: 0.5714
Accuracy Human-person: 0.5996
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.9404
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: 0.0
Accuracy Vehicle-tramtrain: nan
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.6232
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.8732
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.7142
Accuracy Construction-fenceguardrail: 0.5908
Accuracy Construction-bridge: 0.0
Accuracy Construction-tunnel: 0.0
Accuracy Construction-stairs: 0.0
Accuracy Object-pole: 0.3235
Accuracy Object-trafficsign: 0.0
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.9238
Accuracy Nature-terrain: 0.8502
Accuracy Sky: 0.9659
Accuracy Void-ground: 0.0
Accuracy Void-dynamic: 0.3317
Accuracy Void-static: 0.3816
Accuracy Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.7048
Iou Flat-sidewalk: 0.8303
Iou Flat-crosswalk: 0.6312
Iou Flat-cyclinglane: 0.6782
Iou Flat-parkingdriveway: 0.2827
Iou Flat-railtrack: nan
Iou Flat-curb: 0.4000
Iou Human-person: 0.4140
Iou Human-rider: 0.0
Iou Vehicle-car: 0.8091
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: 0.0
Iou Vehicle-tramtrain: nan
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.3207
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.6911
Iou Construction-door: 0.0
Iou Construction-wall: 0.5184
Iou Construction-fenceguardrail: 0.4636
Iou Construction-bridge: 0.0
Iou Construction-tunnel: 0.0
Iou Construction-stairs: 0.0
Iou Object-pole: 0.2087
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.8468
Iou Nature-terrain: 0.7295
Iou Sky: 0.9215
Iou Void-ground: 0.0
Iou Void-dynamic: 0.2688
Iou Void-static: 0.2733
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: 8
eval_batch_size: 8
seed: 1337
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	Accuracy Construction-building	Accuracy Construction-fenceguardrail	Accuracy Construction-tunnel	Accuracy Construction-wall	Accuracy Flat-crosswalk	Accuracy Flat-curb	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-railtrack	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Human-person	Accuracy Nature-terrain	Accuracy Nature-vegetation	Accuracy Object-pole	Accuracy Sky	Accuracy Unlabeled	Accuracy Vehicle-bicycle	Accuracy Vehicle-car	Accuracy Void-static	Iou Construction-building	Iou Construction-fenceguardrail	Iou Construction-tunnel	Iou Construction-wall	Iou Flat-crosswalk	Iou Flat-curb	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-road	Iou Flat-sidewalk	Iou Human-person	Iou Nature-terrain	Iou Nature-vegetation	Iou Object-pole	Iou Sky	Iou Unlabeled	Iou Vehicle-bicycle	Iou Vehicle-car	Iou Void-static	Validation Loss	Mean Accuracy	Mean Iou	Overall Accuracy
2.6565	1.0	107	0.7975	0.0	nan	0.0	0.0010	0.0001	0.1613	0.0264	nan	0.6720	0.8392	0.0	0.8404	0.8947	0.0	0.0920	nan	0.0	0.8406	0.0	0.4385	0.0	nan	0.0	0.0010	0.0001	0.1605	0.0242	0.0	0.4588	0.6841	0.0	0.4904	0.5740	0.0	0.0918	nan	0.0	0.5002	0.0	1.9726	0.1614	0.1037	0.6529
1.8153	2.0	214	0.8270	0.0	nan	0.0	0.0	0.0	0.3100	0.0063	nan	0.7586	0.8437	0.0	0.8548	0.8408	0.0	0.7401	nan	0.0	0.8490	0.0	0.4838	0.0	nan	0.0	0.0	0.0	0.3052	0.0063	nan	0.4512	0.7037	0.0	0.4988	0.6604	0.0	0.7126	nan	0.0	0.5324	0.0	1.4898	0.1884	0.1361	0.6931
1.5059	3.0	321	0.8119	0.0	nan	0.0	0.0	0.0	0.4839	0.0097	nan	0.7839	0.8792	0.0	0.8748	0.8309	0.0	0.7939	nan	0.0	0.8255	0.0	0.5222	0.0	nan	0.0	0.0	0.0	0.4461	0.0097	nan	0.5118	0.7385	0.0	0.4480	0.6201	0.0	0.7671	nan	0.0	0.5996	0.0	1.2858	0.1967	0.1457	0.7171
1.2974	4.0	428	0.9107	0.0	nan	0.0	0.0	0.0	0.5193	0.0394	nan	0.6819	0.8213	0.0	0.8799	0.8477	0.0	0.7999	nan	0.0	0.7091	0.0	0.4222	0.0	nan	0.0	0.0	0.0	0.4755	0.0373	nan	0.4326	0.7226	0.0	0.5089	0.6693	0.0	0.7720	nan	0.0	0.5591	0.0	1.2931	0.1940	0.1437	0.6947
1.2082	5.0	535	0.9251	0.0	nan	0.0	0.0	0.0	0.6249	0.0810	nan	0.8383	0.8276	0.0	0.9019	0.7639	0.0	0.8666	nan	0.0	0.8637	0.0	0.4893	0.0	nan	0.0	0.0	0.0	0.5462	0.0750	nan	0.4988	0.7480	0.0	0.4877	0.6780	0.0	0.8051	nan	0.0	0.6017	0.0	1.1346	0.2092	0.1541	0.7217
1.0533	6.0	642	0.8887	0.0	nan	0.0003	0.0034	0.0001	0.5758	0.1782	nan	0.7557	0.8698	0.0	0.8777	0.8619	0.0	0.8223	nan	0.0	0.9004	0.0	0.5200	0.0	nan	0.0003	0.0034	0.0001	0.4873	0.1339	nan	0.5271	0.7548	0.0	0.5542	0.7061	0.0	0.7894	nan	0.0	0.6049	0.0	1.0493	0.2104	0.1588	0.7364
0.9787	7.0	749	0.9096	0.0	nan	0.0068	0.0007	0.0027	0.5668	0.1591	nan	0.7674	0.8723	0.0	0.8149	0.8944	0.0005	0.8193	nan	0.0	0.9175	0.0	0.5134	0.0	nan	0.0067	0.0007	0.0027	0.4962	0.1322	nan	0.5231	0.7581	0.0	0.6447	0.7477	0.0005	0.7864	nan	0.0	0.5879	0.0	0.9818	0.2104	0.1625	0.7431
0.9352	8.0	856	0.9181	0.0	nan	0.0220	0.3888	0.0174	0.6856	0.1735	nan	0.7767	0.8766	0.0	0.9052	0.7814	0.0237	0.8373	nan	0.0	0.9184	0.0	0.5390	0.0	nan	0.0205	0.3059	0.0172	0.5643	0.1403	nan	0.5563	0.7702	0.0	0.4875	0.6788	0.0235	0.8045	nan	0.0	0.6258	0.0	0.9447	0.2289	0.1729	0.7449
0.8813	9.0	963	0.9027	0.0	nan	0.0444	0.5346	0.0815	0.6656	0.1494	nan	0.7836	0.8684	0.0098	0.8231	0.9172	0.0707	0.8615	nan	0.0	0.8208	0.0	0.5668	0.0	nan	0.0415	0.3656	0.0737	0.5123	0.1303	nan	0.5382	0.7637	0.0096	0.6133	0.7318	0.0685	0.8106	nan	0.0	0.6651	0.0	0.8816	0.2354	0.1841	0.7557
0.8472	10.0	1070	0.7958	0.0	nan	0.2135	0.5907	0.1311	0.6663	0.2215	nan	0.7736	0.8817	0.0857	0.8969	0.8565	0.1806	0.9238	nan	0.0	0.9459	0.0035	0.5868	0.0	nan	0.1541	0.3492	0.1112	0.5270	0.1677	nan	0.5794	0.7747	0.0819	0.5577	0.7238	0.1398	0.8435	nan	0.0	0.5893	0.0035	0.8800	0.2552	0.1934	0.7592
0.8106	11.0	1177	0.8961	0.0	nan	0.1263	0.6559	0.1820	0.6675	0.1933	nan	0.7409	0.8650	0.0082	0.8293	0.9050	0.1567	0.8818	nan	0.0	0.9197	0.0031	0.5524	0.0	nan	0.0953	0.3313	0.1481	0.5231	0.1613	nan	0.5669	0.7643	0.0082	0.6577	0.7539	0.1392	0.8412	nan	0.0	0.6347	0.0031	0.8711	0.2510	0.1931	0.7597
0.7419	12.0	1284	0.9082	0.0	nan	0.2287	0.5583	0.2201	0.6230	0.2109	nan	0.6572	0.9225	0.1244	0.8802	0.8402	0.2081	0.9141	nan	0.0	0.8896	0.0204	0.5947	0.0	nan	0.1544	0.3939	0.1740	0.4993	0.1772	nan	0.5394	0.7789	0.1162	0.5749	0.7338	0.1704	0.8557	nan	0.0	0.6718	0.0200	0.8464	0.2564	0.2017	0.7651
0.7609	13.0	1391	0.8639	0.0	nan	0.2723	0.5618	0.1835	0.7294	0.2595	nan	0.8081	0.8748	0.2091	0.7065	0.9278	0.1908	0.9148	nan	0.0	0.8740	0.0120	0.5903	0.0	nan	0.1742	0.3625	0.1550	0.5396	0.2070	nan	0.5826	0.7957	0.1913	0.6142	0.7560	0.1445	0.8606	nan	0.0	0.6712	0.0117	0.7896	0.2621	0.2080	0.7745
0.7455	14.0	1498	0.8564	0.0019	nan	0.2225	0.5236	0.1934	0.8098	0.2290	nan	0.7359	0.8144	0.3450	0.8487	0.9055	0.2513	0.9280	nan	0.0	0.9383	0.0673	0.6308	0.0019	nan	0.1726	0.3830	0.1549	0.3913	0.1841	nan	0.5589	0.7489	0.2969	0.6690	0.7714	0.1872	0.8581	nan	0.0	0.6264	0.0586	0.8565	0.2710	0.2092	0.7539
0.6881	15.0	1605	0.8287	0.0033	nan	0.3191	0.5507	0.2979	0.5358	0.2541	nan	0.6901	0.9374	0.4436	0.8288	0.9143	0.3035	0.8749	nan	0.0	0.9256	0.1109	0.6175	0.0033	nan	0.1864	0.3985	0.2078	0.4672	0.2015	nan	0.5951	0.7873	0.2857	0.6630	0.7731	0.2011	0.8380	nan	0.0	0.6740	0.0885	0.7964	0.2756	0.2184	0.7782
0.6765	16.0	1712	0.8966	0.0040	nan	0.2551	0.5566	0.2521	0.7439	0.2253	nan	0.7619	0.8998	0.3746	0.8767	0.8728	0.3282	0.9049	nan	0.0	0.9103	0.0815	0.6382	0.0040	nan	0.1860	0.3390	0.1969	0.5335	0.1895	nan	0.5992	0.8004	0.2743	0.6415	0.7725	0.2014	0.8593	nan	0.0	0.6739	0.0690	0.7522	0.2795	0.2181	0.7840
0.6916	17.0	1819	0.9197	0.0012	nan	0.2264	0.5975	0.3264	0.6124	0.2644	nan	0.7144	0.9387	0.4727	0.7551	0.9054	0.2726	0.8723	nan	0.0	0.9130	0.1324	0.6320	0.0012	nan	0.1790	0.3650	0.2435	0.4937	0.2143	nan	0.6024	0.8068	0.3370	0.6333	0.7786	0.2202	0.8384	nan	0.0	0.6874	0.1116	0.7345	0.2789	0.2233	0.7894
0.6542	18.0	1926	0.8497	0.0173	nan	0.3213	0.5996	0.3201	0.6111	0.3493	nan	0.6840	0.9287	0.4330	0.8411	0.8923	0.2688	0.8652	nan	0.0	0.9027	0.0931	0.5867	0.0170	nan	0.1595	0.3689	0.2485	0.5211	0.2366	nan	0.5922	0.7996	0.3077	0.6825	0.7806	0.2212	0.8294	nan	0.0	0.6811	0.0783	0.7572	0.2805	0.2222	0.7793
0.6571	19.0	2033	0.9083	0.0447	nan	0.2459	0.5112	0.2124	0.7723	0.2259	nan	0.7202	0.9209	0.5086	0.7827	0.9200	0.3190	0.9195	nan	0.0	0.9093	0.1057	0.6473	0.0437	nan	0.1863	0.4262	0.1823	0.5132	0.1920	nan	0.6192	0.8032	0.3263	0.6546	0.7807	0.2313	0.8716	nan	0.0	0.7148	0.0893	0.7364	0.2821	0.2276	0.7931
0.6384	20.0	2140	0.8849	0.0414	nan	0.1962	0.5820	0.3236	0.6575	0.2474	nan	0.8182	0.8822	0.6807	0.8467	0.9131	0.3208	0.9268	nan	0.0	0.8965	0.1188	0.6475	0.0407	nan	0.1568	0.5335	0.2405	0.5179	0.2066	nan	0.5750	0.7900	0.2993	0.6815	0.7709	0.2355	0.8693	nan	0.0	0.7333	0.0981	0.7340	0.2918	0.2311	0.7859
0.6252	21.0	2247	0.8263	0.1258	nan	0.2778	0.6121	0.3499	0.6934	0.2266	nan	0.7969	0.8769	0.6939	0.9227	0.8686	0.4050	0.9405	nan	0.0	0.9128	0.1771	0.6456	0.1131	nan	0.1814	0.3747	0.2492	0.5367	0.1801	nan	0.5816	0.8003	0.3315	0.5921	0.7556	0.2651	0.8754	nan	0.0	0.7049	0.1212	0.7321	0.3033	0.2284	0.7796
0.6698	22.0	2354	0.8912	0.1502	nan	0.2718	0.6639	0.3004	0.6492	0.3218	nan	0.6951	0.9186	0.4743	0.8959	0.8911	0.3409	0.9252	nan	0.0001	0.9031	0.1087	0.6475	0.1340	nan	0.2023	0.3261	0.2150	0.5312	0.2352	nan	0.5859	0.8034	0.2980	0.6442	0.7695	0.2464	0.8737	nan	0.0001	0.6981	0.0865	0.7266	0.2938	0.2280	0.7872
0.6429	23.0	2461	0.9294	0.0817	nan	0.2604	0.6158	0.3461	0.7275	0.1766	nan	0.8067	0.9012	0.5425	0.8040	0.9204	0.3580	0.9378	nan	0.0373	0.8848	0.0688	0.6194	0.0780	nan	0.1961	0.4622	0.2457	0.5838	0.1559	nan	0.6136	0.8213	0.2747	0.7080	0.7948	0.2476	0.8849	nan	0.0372	0.7229	0.0594	0.6875	0.2937	0.2346	0.7987
0.5917	24.0	2568	0.7986	0.1549	nan	0.2686	0.6394	0.3909	0.6896	0.2851	nan	0.7589	0.9191	0.6678	0.8044	0.9486	0.3270	0.9296	nan	0.0751	0.9440	0.1671	0.6617	0.1325	nan	0.1979	0.3704	0.2705	0.5359	0.2339	nan	0.6163	0.8235	0.4201	0.7010	0.7736	0.2251	0.8827	nan	0.0747	0.6398	0.1288	0.6978	0.3053	0.2403	0.7975
0.5778	25.0	2675	0.9212	0.1422	nan	0.2097	0.5965	0.2734	0.7111	0.2954	nan	0.7440	0.9157	0.6745	0.8814	0.8879	0.3736	0.9391	nan	0.1615	0.9217	0.1080	0.6438	0.1322	nan	0.1576	0.4708	0.2033	0.5371	0.2346	nan	0.6170	0.8108	0.3663	0.6723	0.7908	0.2612	0.8791	nan	0.1579	0.7238	0.0896	0.7019	0.3049	0.2421	0.7968
0.5839	26.0	2782	0.8477	0.1731	nan	0.2914	0.6101	0.3760	0.6827	0.2357	nan	0.8232	0.8993	0.5657	0.9195	0.9062	0.3810	0.9409	nan	0.1232	0.9046	0.1500	0.6593	0.1446	nan	0.2071	0.4717	0.2693	0.5334	0.1979	nan	0.6011	0.8287	0.4161	0.6505	0.7672	0.2521	0.8827	nan	0.1228	0.7147	0.1207	0.6879	0.3072	0.2450	0.7973
0.5734	27.0	2889	0.8007	0.2540	nan	0.3862	0.6117	0.4273	0.7247	0.3942	nan	0.7622	0.9146	0.7149	0.8766	0.8963	0.4298	0.9339	nan	0.3326	0.9294	0.1441	0.6389	0.2109	nan	0.2249	0.3922	0.2973	0.5280	0.2871	nan	0.6366	0.8226	0.3818	0.6832	0.8028	0.2587	0.8848	nan	0.2959	0.7255	0.1113	0.6813	0.3292	0.2557	0.8006
0.5741	28.0	2996	0.9092	0.1978	nan	0.2749	0.7645	0.3532	0.7271	0.2706	nan	0.7194	0.9155	0.5846	0.8878	0.9022	0.3896	0.9181	nan	0.3047	0.9347	0.1532	0.6509	0.1716	nan	0.2177	0.3532	0.2519	0.5745	0.2240	nan	0.6098	0.8118	0.3964	0.6719	0.8016	0.2893	0.8860	nan	0.2832	0.7055	0.1234	0.6998	0.3190	0.2507	0.8005
0.5836	29.0	3103	0.8758	0.2305	nan	0.2952	0.6134	0.3681	0.6283	0.3272	nan	0.6950	0.9400	0.6323	0.8473	0.9286	0.3703	0.9201	nan	0.3964	0.9223	0.1975	0.6749	0.1947	nan	0.2209	0.4202	0.2705	0.5066	0.2588	nan	0.5920	0.8115	0.4589	0.7138	0.7930	0.2682	0.8848	nan	0.3418	0.7119	0.1442	0.7093	0.3184	0.2583	0.8015
0.5577	30.0	3210	0.9147	0.2788	nan	0.2229	0.5876	0.4227	0.7800	0.2299	nan	0.7470	0.8943	0.6187	0.8646	0.9010	0.3976	0.9387	nan	0.4046	0.9205	0.1340	0.6683	0.2358	nan	0.1901	0.4637	0.2960	0.4967	0.2003	nan	0.6012	0.8010	0.4205	0.7067	0.8031	0.2925	0.8828	nan	0.3489	0.7312	0.1072	0.6963	0.3205	0.2577	0.7976
0.5545	31.0	3317	0.9001	0.2977	nan	0.2712	0.6252	0.3806	0.7290	0.2459	nan	0.7609	0.9054	0.7156	0.8482	0.9031	0.4154	0.9352	nan	0.4643	0.9343	0.1637	0.6673	0.2376	nan	0.2101	0.3995	0.2665	0.5230	0.2174	nan	0.6141	0.8159	0.4339	0.7005	0.8009	0.2695	0.8859	nan	0.3878	0.7091	0.1213	0.6836	0.3280	0.2581	0.8010
0.5269	32.0	3424	0.9060	0.4759	nan	0.3232	0.6432	0.3927	0.6342	0.2602	nan	0.7524	0.9194	0.7033	0.8792	0.8537	0.4221	0.9403	nan	0.4556	0.9178	0.1575	0.6672	0.3371	nan	0.2263	0.3706	0.2765	0.5210	0.2255	nan	0.5975	0.8101	0.4598	0.6359	0.7793	0.2807	0.8920	nan	0.3796	0.7200	0.1272	0.6946	0.3324	0.2596	0.7966
0.5213	33.0	3531	0.8315	0.4657	nan	0.3881	0.6345	0.4295	0.6866	0.2536	nan	0.7532	0.9289	0.6601	0.8186	0.9079	0.3757	0.9176	nan	0.5423	0.9215	0.1955	0.6437	0.3329	nan	0.2176	0.4091	0.2939	0.5582	0.2099	nan	0.6195	0.8257	0.4062	0.6797	0.8014	0.2790	0.8839	nan	0.4195	0.7277	0.1398	0.6696	0.3347	0.2640	0.8028
0.5055	34.0	3638	0.8649	0.3888	nan	0.3544	0.5920	0.3898	0.7622	0.2193	nan	0.7865	0.9063	0.6768	0.8973	0.8918	0.4342	0.9390	nan	0.5511	0.9095	0.2330	0.6676	0.3081	nan	0.2152	0.4420	0.2797	0.5614	0.1915	nan	0.6343	0.8194	0.4193	0.6603	0.7899	0.2946	0.8978	nan	0.4256	0.7625	0.1620	0.6759	0.3374	0.2666	0.8045
0.5124	35.0	3745	0.9149	0.2266	nan	0.2506	0.5803	0.3588	0.7242	0.2005	nan	0.7093	0.9308	0.6701	0.8525	0.8968	0.4241	0.9384	nan	0.5869	0.9321	0.1433	0.6710	0.2056	nan	0.1856	0.4767	0.2627	0.5190	0.1790	nan	0.6001	0.8052	0.4527	0.7062	0.8069	0.3050	0.8952	nan	0.4466	0.7249	0.1195	0.6842	0.3231	0.2613	0.8015
0.4959	36.0	3852	0.8790	0.3951	nan	0.3026	0.6862	0.3491	0.6539	0.2649	nan	0.7458	0.9103	0.7193	0.7866	0.9228	0.4530	0.9465	nan	0.5683	0.9294	0.2218	0.6744	0.2824	nan	0.1899	0.4262	0.2589	0.5171	0.2221	nan	0.6050	0.8097	0.4873	0.6860	0.7991	0.2925	0.9010	nan	0.4504	0.7251	0.1442	0.6853	0.3355	0.2647	0.7987
0.4958	37.0	3959	0.7509	0.4632	nan	0.3745	0.6063	0.4119	0.6774	0.2271	nan	0.8061	0.9174	0.7258	0.8569	0.9360	0.4130	0.9352	nan	0.7228	0.8839	0.3199	0.6300	0.2861	nan	0.1945	0.4176	0.2925	0.5179	0.1990	nan	0.6393	0.8261	0.4386	0.7103	0.7994	0.2762	0.8997	nan	0.4071	0.7774	0.1933	0.6565	0.3446	0.2658	0.8017
0.5013	38.0	4066	0.8619	0.3570	nan	0.3168	0.6329	0.4214	0.5528	0.2093	nan	0.7294	0.9391	0.7966	0.8516	0.9236	0.4781	0.9385	nan	0.5837	0.9275	0.2179	0.6793	0.3130	nan	0.2073	0.4319	0.2970	0.4523	0.1862	nan	0.6205	0.8096	0.3777	0.7073	0.8023	0.2939	0.8996	nan	0.4674	0.7528	0.1439	0.6816	0.3356	0.2638	0.8016
0.5281	39.0	4173	0.8928	0.4702	nan	0.3180	0.6194	0.3986	0.6849	0.2775	nan	0.7503	0.9264	0.6477	0.8682	0.9023	0.4431	0.9526	nan	0.6658	0.9184	0.1814	0.6700	0.3452	nan	0.2057	0.4170	0.2878	0.5042	0.2297	nan	0.6408	0.8273	0.4644	0.7025	0.8064	0.3063	0.8981	nan	0.4867	0.7473	0.1373	0.6531	0.3412	0.2711	0.8084
0.4642	40.0	4280	0.8705	0.4131	nan	0.3329	0.6256	0.4232	0.6321	0.3194	nan	0.7360	0.9466	0.7107	0.9011	0.8641	0.4380	0.9412	nan	0.6649	0.9078	0.1999	0.6692	0.3172	nan	0.2010	0.3998	0.2886	0.5335	0.2623	nan	0.6463	0.8276	0.4132	0.6283	0.7706	0.3106	0.8973	nan	0.4979	0.7587	0.1438	0.6581	0.3415	0.2677	0.8055
0.4831	41.0	4387	0.8860	0.4131	nan	0.2894	0.6372	0.4171	0.6812	0.2075	nan	0.7951	0.9141	0.6933	0.8844	0.8988	0.4327	0.9427	nan	0.6858	0.9285	0.2420	0.6691	0.3088	nan	0.2073	0.4344	0.3032	0.5591	0.1867	nan	0.6253	0.8188	0.4484	0.7043	0.8055	0.3157	0.8995	nan	0.4878	0.7087	0.1631	0.6573	0.3422	0.2702	0.8071
0.4714	42.0	4494	0.8946	0.3704	nan	0.2957	0.6643	0.4568	0.6497	0.3592	nan	0.7478	0.9243	0.6273	0.9198	0.8512	0.4352	0.9436	nan	0.7072	0.9447	0.2599	0.6701	0.2442	nan	0.2178	0.3513	0.3192	0.5243	0.2831	nan	0.6260	0.8325	0.4782	0.6241	0.7831	0.3127	0.8992	nan	0.4946	0.7056	0.1871	0.6549	0.3454	0.2673	0.8045
0.4878	43.0	4601	0.9033	0.3113	nan	0.2935	0.6221	0.3967	0.6793	0.3211	nan	0.7428	0.9121	0.6910	0.8810	0.9132	0.4458	0.9358	nan	0.6824	0.9124	0.1703	0.6776	0.2277	nan	0.2084	0.4498	0.2853	0.4922	0.2737	nan	0.6147	0.8098	0.4437	0.7178	0.8046	0.2922	0.8994	nan	0.5097	0.7565	0.1302	0.6586	0.3379	0.2685	0.8038
0.4805	44.0	4708	0.8824	0.4241	nan	0.3467	0.7158	0.3892	0.7250	0.2814	nan	0.7464	0.8815	0.6941	0.8797	0.8895	0.4336	0.9494	nan	0.7047	0.9271	0.2682	0.6706	0.3130	nan	0.2076	0.3547	0.2856	0.4866	0.2421	nan	0.6148	0.7963	0.4777	0.6900	0.7974	0.3125	0.9002	nan	0.4969	0.7504	0.1810	0.6820	0.3481	0.2680	0.7946
0.4747	45.0	4815	0.8139	0.4088	nan	0.3106	0.6807	0.4671	0.6958	0.2292	nan	0.8321	0.9179	0.7484	0.8543	0.9247	0.4923	0.9555	nan	0.7380	0.9415	0.2129	0.6686	0.3037	nan	0.1998	0.4850	0.3274	0.5518	0.2008	nan	0.6553	0.8314	0.4621	0.7211	0.8091	0.3011	0.9038	nan	0.4781	0.7437	0.1502	0.6262	0.3507	0.2748	0.8131
0.4681	46.0	4922	0.8402	0.3950	nan	0.3435	0.6379	0.4827	0.6967	0.3347	nan	0.8343	0.9109	0.7782	0.8995	0.9110	0.4448	0.9547	nan	0.7459	0.9330	0.2634	0.6763	0.3256	nan	0.2373	0.4296	0.3347	0.5622	0.2714	nan	0.6528	0.8350	0.4949	0.7053	0.8056	0.3120	0.9040	nan	0.4853	0.7434	0.1809	0.6220	0.3565	0.2799	0.8162
0.419	47.0	5029	0.8527	0.4471	nan	0.3417	0.6008	0.4955	0.7197	0.2757	nan	0.8323	0.8835	0.7060	0.8791	0.9219	0.3697	0.9280	nan	0.7093	0.9327	0.2186	0.6613	0.3293	nan	0.2184	0.4937	0.3240	0.5170	0.2344	nan	0.6411	0.8225	0.5321	0.7023	0.7950	0.2979	0.8961	nan	0.5071	0.7187	0.1744	0.6605	0.3473	0.2770	0.8059
0.4736	48.0	5136	0.8026	0.5737	nan	0.4110	0.6952	0.3557	0.7365	0.3253	nan	0.7093	0.9250	0.7357	0.8942	0.8800	0.4467	0.9579	nan	0.6806	0.9206	0.2863	0.6519	0.3273	nan	0.2130	0.3824	0.2797	0.5295	0.2802	nan	0.5993	0.8177	0.5033	0.6894	0.8022	0.3029	0.9009	nan	0.5006	0.7643	0.1923	0.6830	0.3543	0.2730	0.8008
0.4425	49.0	5243	0.7921	0.5531	nan	0.3940	0.7707	0.4011	0.6824	0.3842	nan	0.7273	0.9257	0.7685	0.8880	0.9076	0.4550	0.9551	nan	0.7330	0.9277	0.2620	0.6538	0.2907	nan	0.2122	0.3565	0.2962	0.5324	0.3094	nan	0.6311	0.8308	0.4845	0.7015	0.8021	0.2936	0.9025	nan	0.4894	0.7460	0.1746	0.6564	0.3602	0.2721	0.8055
0.4661	50.0	5350	0.8057	0.5361	nan	0.3774	0.6634	0.4568	0.7309	0.2670	nan	0.7893	0.9067	0.7933	0.9024	0.8721	0.4749	0.9422	nan	0.7443	0.9375	0.3137	0.6580	0.3192	nan	0.1932	0.3982	0.3127	0.5787	0.2306	nan	0.6296	0.8278	0.4690	0.6596	0.7925	0.3145	0.9057	nan	0.4979	0.7491	0.1928	0.6489	0.3598	0.2728	0.8032
0.4256	51.0	5457	0.8672	0.4777	nan	0.3676	0.6395	0.5028	0.6649	0.3376	nan	0.7862	0.9337	0.7858	0.8546	0.9026	0.4770	0.9547	nan	0.7407	0.9300	0.2173	0.6749	0.3788	nan	0.2209	0.4977	0.3397	0.5864	0.2740	nan	0.6558	0.8276	0.4978	0.7259	0.8143	0.3176	0.9035	nan	0.5077	0.7595	0.1557	0.6223	0.3575	0.2856	0.8180
0.4521	52.0	5564	0.8747	0.4678	nan	0.3192	0.6484	0.3583	0.7574	0.2903	nan	0.6127	0.9469	0.7390	0.8844	0.9108	0.4266	0.9525	nan	0.7503	0.9363	0.2319	0.6755	0.3182	nan	0.1914	0.3799	0.2700	0.5715	0.2491	nan	0.5706	0.8172	0.5261	0.7168	0.8136	0.3114	0.9068	nan	0.4979	0.7388	0.1589	0.7228	0.3471	0.2723	0.8057

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

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Apr 9, 2024
Updated: Apr 9, 2024