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Vrjb/
SegFormer_b3_mappillary_

Name: SegFormer_b3_mappillary_
Availability: InStock

transformers

safetensors

segformer

generated_from_trainer

nvidia/segformer-b3-finetuned-cityscapes-1024-1024

finetune

other

endpoints_compatible

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SegFormer_b3_mappillary_

This model is a fine-tuned version of nvidia/segformer-b3-finetuned-cityscapes-1024-1024 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.9465
Mean Iou: 0.6876
Mean Accuracy: 0.8034
Overall Accuracy: 0.9418
Accuracy Construction--barrier--fence: 0.6800
Accuracy Construction--barrier--guard-rail: 0.8164
Accuracy Construction--barrier--other-barrier: 0.6949
Accuracy Construction--barrier--wall: 0.6949
Accuracy Construction--flat--road: 0.9600
Accuracy Construction--flat--service-lane: 0.6083
Accuracy Construction--flat--sidewalk: 0.8963
Accuracy Construction--structure--building: 0.9474
Accuracy Human--person: 0.8455
Accuracy Human--rider--bicyclist: 0.7457
Accuracy Marking--crosswalk-zebra: 0.8079
Accuracy Marking--general: 0.7057
Accuracy Nature--sky: 0.9903
Accuracy Nature--terrain: 0.8351
Accuracy Nature--vegetation: 0.9468
Accuracy Object--support--pole: 0.5784
Accuracy Object--support--traffic-sign-frame: 0.6875
Accuracy Object--traffic-light: 0.7517
Accuracy Object--traffic-sign--front: 0.8324
Accuracy Object--vehicle--bicycle: 0.7600
Accuracy Object--vehicle--bus: 0.8898
Accuracy Object--vehicle--car: 0.9598
Accuracy Object--vehicle--truck: 0.8427
Iou Construction--barrier--fence: 0.5668
Iou Construction--barrier--guard-rail: 0.6378
Iou Construction--barrier--other-barrier: 0.5595
Iou Construction--barrier--wall: 0.5403
Iou Construction--flat--road: 0.9205
Iou Construction--flat--service-lane: 0.4618
Iou Construction--flat--sidewalk: 0.7968
Iou Construction--structure--building: 0.8891
Iou Human--person: 0.6797
Iou Human--rider--bicyclist: 0.5718
Iou Marking--crosswalk-zebra: 0.7125
Iou Marking--general: 0.5860
Iou Nature--sky: 0.9811
Iou Nature--terrain: 0.6872
Iou Nature--vegetation: 0.8949
Iou Object--support--pole: 0.4599
Iou Object--support--traffic-sign-frame: 0.5531
Iou Object--traffic-light: 0.5835
Iou Object--traffic-sign--front: 0.7231
Iou Object--vehicle--bicycle: 0.5797
Iou Object--vehicle--bus: 0.7946
Iou Object--vehicle--car: 0.9055
Iou Object--vehicle--truck: 0.7304

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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Construction--barrier--fence	Accuracy Construction--barrier--guard-rail	Accuracy Construction--barrier--other-barrier	Accuracy Construction--barrier--wall	Accuracy Construction--flat--road	Accuracy Construction--flat--service-lane	Accuracy Construction--flat--sidewalk	Accuracy Construction--structure--building	Accuracy Human--person	Accuracy Human--rider--bicyclist	Accuracy Marking--crosswalk-zebra	Accuracy Marking--general	Accuracy Nature--sky	Accuracy Nature--terrain	Accuracy Nature--vegetation	Accuracy Object--support--pole	Accuracy Object--support--traffic-sign-frame	Accuracy Object--traffic-light	Accuracy Object--traffic-sign--front	Accuracy Object--vehicle--bicycle	Accuracy Object--vehicle--bus	Accuracy Object--vehicle--car	Accuracy Object--vehicle--truck	Iou Construction--barrier--fence	Iou Construction--barrier--guard-rail	Iou Construction--barrier--other-barrier	Iou Construction--barrier--wall	Iou Construction--flat--road	Iou Construction--flat--service-lane	Iou Construction--flat--sidewalk	Iou Construction--structure--building	Iou Human--person	Iou Human--rider--bicyclist	Iou Marking--crosswalk-zebra	Iou Marking--general	Iou Nature--sky	Iou Nature--terrain	Iou Nature--vegetation	Iou Object--support--pole	Iou Object--support--traffic-sign-frame	Iou Object--traffic-light	Iou Object--traffic-sign--front	Iou Object--vehicle--bicycle	Iou Object--vehicle--bus	Iou Object--vehicle--car	Iou Object--vehicle--truck
1.3425	0.4444	1000	1.2765	0.2545	0.3044	0.8800	0.6134	0.0	0.0033	0.0266	0.9695	0.0	0.7990	0.9191	0.0001	0.0	0.0000	0.0467	0.9827	0.6667	0.9530	0.0001	0.0	0.0	0.0845	0.0	0.0000	0.9361	0.0	0.3190	0.0	0.0033	0.0263	0.8350	0.0	0.6117	0.8074	0.0001	0.0	0.0000	0.0426	0.9577	0.5511	0.8295	0.0001	0.0	0.0	0.0842	0.0	0.0000	0.7854	0.0
1.1254	0.8889	2000	1.0846	0.4898	0.5869	0.9169	0.6043	0.7972	0.5664	0.5400	0.9357	0.0	0.8852	0.9351	0.6966	0.0	0.6074	0.5823	0.9887	0.8180	0.9342	0.4084	0.0	0.0577	0.7376	0.0	0.7325	0.9520	0.7184	0.4699	0.4923	0.4243	0.4304	0.8822	0.0	0.6986	0.8531	0.5266	0.0	0.5141	0.4423	0.9723	0.6271	0.8680	0.3223	0.0	0.0574	0.5713	0.0	0.6531	0.8591	0.6014
1.0545	1.3333	3000	1.0331	0.5437	0.6380	0.9253	0.6281	0.6042	0.6614	0.6689	0.9598	0.1969	0.8240	0.9329	0.8189	0.0	0.6535	0.5900	0.9890	0.7623	0.9431	0.4452	0.0576	0.6242	0.7553	0.0473	0.8173	0.9560	0.7380	0.4962	0.5220	0.4592	0.5118	0.8942	0.1780	0.7109	0.8662	0.5534	0.0	0.5445	0.4827	0.9761	0.6458	0.8780	0.3593	0.0575	0.4728	0.6404	0.0470	0.7192	0.8673	0.6224
1.0702	1.7778	4000	1.0152	0.5925	0.7119	0.9278	0.6339	0.8483	0.7095	0.6748	0.9643	0.4595	0.8005	0.9253	0.8096	0.0	0.6926	0.5453	0.9910	0.7992	0.9379	0.5329	0.4324	0.6756	0.7847	0.5913	0.8819	0.9506	0.7321	0.4993	0.5053	0.4767	0.4969	0.8986	0.3391	0.7166	0.8689	0.5999	0.0	0.6029	0.4807	0.9773	0.6585	0.8804	0.3939	0.3874	0.5017	0.6616	0.4461	0.7138	0.8789	0.6421
0.9517	2.2222	5000	0.9929	0.6200	0.7373	0.9309	0.6915	0.7243	0.6735	0.6030	0.9513	0.5424	0.8577	0.9424	0.8352	0.1893	0.7769	0.6458	0.9894	0.8427	0.9335	0.5217	0.5057	0.6906	0.8021	0.7058	0.8375	0.9478	0.7480	0.5345	0.5905	0.5076	0.4934	0.9028	0.4129	0.7423	0.8723	0.6025	0.1827	0.6044	0.5226	0.9785	0.6709	0.8819	0.4060	0.4432	0.5208	0.6637	0.4977	0.7046	0.8846	0.6402
1.0168	2.6667	6000	0.9903	0.6447	0.7756	0.9316	0.6411	0.8493	0.7208	0.6234	0.9524	0.8120	0.8049	0.9446	0.8134	0.6314	0.7481	0.6312	0.9903	0.8097	0.9423	0.4974	0.6191	0.6996	0.7810	0.7204	0.8662	0.9570	0.7842	0.5213	0.5644	0.5073	0.5028	0.8994	0.4049	0.7222	0.8763	0.6491	0.5067	0.6396	0.5282	0.9784	0.6763	0.8856	0.4040	0.4873	0.5286	0.6815	0.5415	0.7612	0.8878	0.6727
1.0717	3.1111	7000	0.9785	0.6508	0.7757	0.9340	0.6746	0.7986	0.6721	0.6354	0.9472	0.6116	0.8959	0.9425	0.8280	0.6989	0.7759	0.6393	0.9887	0.8304	0.9454	0.5240	0.5967	0.7134	0.7888	0.7306	0.8959	0.9525	0.7551	0.5325	0.6073	0.5107	0.5195	0.9065	0.4417	0.7717	0.8754	0.6435	0.5295	0.6266	0.5228	0.9791	0.6817	0.8861	0.4167	0.4969	0.5338	0.6829	0.5395	0.7210	0.8904	0.6522
1.2969	3.5556	8000	0.9726	0.6607	0.7788	0.9353	0.6778	0.7819	0.6816	0.6455	0.9504	0.7169	0.8924	0.9420	0.8285	0.7140	0.7469	0.6249	0.9890	0.8405	0.9465	0.5522	0.6019	0.6983	0.8032	0.6940	0.8498	0.9571	0.7779	0.5486	0.6167	0.5305	0.5219	0.9080	0.4372	0.7636	0.8778	0.6530	0.5394	0.6504	0.5336	0.9795	0.6808	0.8879	0.4286	0.5104	0.5458	0.6881	0.5569	0.7712	0.8914	0.6741
1.066	4.0	9000	0.9686	0.6606	0.7836	0.9352	0.6679	0.8057	0.6835	0.6398	0.9465	0.6595	0.9040	0.9473	0.8201	0.7057	0.7608	0.6587	0.9889	0.8467	0.9406	0.5365	0.6196	0.6992	0.7951	0.7535	0.8633	0.9606	0.8193	0.5390	0.6280	0.5393	0.5135	0.9071	0.4355	0.7590	0.8777	0.6543	0.5289	0.6610	0.5504	0.9798	0.6819	0.8884	0.4267	0.5145	0.5534	0.6897	0.5437	0.7678	0.8903	0.6651
0.9934	4.4444	10000	0.9634	0.6599	0.7918	0.9362	0.6866	0.8041	0.6551	0.7025	0.9550	0.7200	0.8742	0.9367	0.8221	0.6826	0.7694	0.6683	0.9876	0.8475	0.9470	0.5354	0.6706	0.7083	0.8183	0.8001	0.8479	0.9553	0.8165	0.5422	0.6144	0.5272	0.5433	0.9112	0.4443	0.7763	0.8806	0.6577	0.4982	0.6644	0.5512	0.9794	0.6814	0.8875	0.4287	0.5303	0.5516	0.6906	0.4916	0.7697	0.8931	0.6635
0.9396	4.8889	11000	0.9608	0.6706	0.7865	0.9375	0.6659	0.7949	0.6840	0.6375	0.9573	0.6485	0.8755	0.9484	0.8153	0.7668	0.7889	0.6647	0.9907	0.8336	0.9425	0.5406	0.6331	0.7248	0.8096	0.7237	0.8555	0.9560	0.8317	0.5476	0.6242	0.5600	0.5252	0.9132	0.4695	0.7721	0.8799	0.6674	0.5666	0.6676	0.5550	0.9800	0.6793	0.8900	0.4318	0.5307	0.5538	0.7017	0.5603	0.7705	0.8948	0.6825
1.0232	5.3333	12000	0.9602	0.6665	0.7811	0.9374	0.6948	0.7343	0.6613	0.6420	0.9553	0.5946	0.8991	0.9486	0.8225	0.6960	0.7909	0.6412	0.9903	0.8492	0.9400	0.5514	0.6470	0.7395	0.8046	0.7763	0.8375	0.9602	0.7878	0.5510	0.6077	0.5071	0.5136	0.9148	0.4577	0.7800	0.8786	0.6628	0.5361	0.6571	0.5479	0.9802	0.6900	0.8905	0.4368	0.5341	0.5536	0.7050	0.5662	0.7751	0.8941	0.6901
1.003	5.7778	13000	0.9568	0.6665	0.7868	0.9377	0.6587	0.7905	0.7084	0.6866	0.9556	0.5506	0.9020	0.9400	0.8251	0.6929	0.7483	0.6743	0.9903	0.8455	0.9451	0.5720	0.6413	0.7217	0.8021	0.7812	0.9025	0.9549	0.8077	0.5459	0.6315	0.5463	0.5151	0.9130	0.4338	0.7792	0.8823	0.6636	0.5190	0.6567	0.5635	0.9804	0.6903	0.8912	0.4402	0.5333	0.5608	0.7045	0.5575	0.7314	0.8977	0.6934
1.0042	6.2222	14000	0.9524	0.6759	0.7923	0.9393	0.7133	0.7758	0.6648	0.6809	0.9609	0.5916	0.8752	0.9431	0.8173	0.7671	0.7821	0.6872	0.9899	0.8290	0.9472	0.5473	0.6624	0.7405	0.8113	0.7582	0.8978	0.9559	0.8234	0.5641	0.6347	0.5491	0.5441	0.9163	0.4613	0.7830	0.8823	0.6701	0.5536	0.6898	0.5640	0.9804	0.6937	0.8926	0.4372	0.5404	0.5589	0.7084	0.5520	0.7715	0.8982	0.7006
1.1329	6.6667	15000	0.9514	0.6774	0.7927	0.9398	0.6569	0.8159	0.6914	0.7315	0.9660	0.6223	0.8705	0.9411	0.8354	0.7894	0.7848	0.6641	0.9904	0.8079	0.9484	0.5592	0.6137	0.7370	0.8166	0.7257	0.8908	0.9567	0.8171	0.5473	0.6384	0.5535	0.5377	0.9185	0.4755	0.7851	0.8851	0.6648	0.5548	0.6978	0.5690	0.9804	0.6840	0.8920	0.4418	0.5268	0.5655	0.7083	0.5549	0.7825	0.9004	0.7167
0.9527	7.1111	16000	0.9583	0.6728	0.7944	0.9376	0.7232	0.8220	0.6712	0.6710	0.9460	0.5458	0.9175	0.9498	0.8358	0.7740	0.8209	0.6919	0.9886	0.7697	0.9432	0.5861	0.6600	0.7535	0.8073	0.7433	0.8595	0.9565	0.8342	0.5638	0.6379	0.5591	0.5278	0.9112	0.4177	0.7759	0.8823	0.6661	0.5778	0.6754	0.5674	0.9800	0.6777	0.8915	0.4479	0.5367	0.5604	0.7129	0.5705	0.7603	0.8962	0.6784
0.9354	7.5556	17000	0.9510	0.6782	0.7953	0.9394	0.7130	0.8167	0.6822	0.6615	0.9568	0.5684	0.8837	0.9473	0.8334	0.7628	0.8149	0.7023	0.9909	0.8648	0.9403	0.5304	0.7092	0.7378	0.8073	0.7204	0.8791	0.9603	0.8079	0.5636	0.6413	0.5599	0.5420	0.9149	0.4203	0.7894	0.8852	0.6728	0.5804	0.6862	0.5697	0.9806	0.6873	0.8920	0.4373	0.5428	0.5693	0.7165	0.5854	0.7695	0.8976	0.6953
0.908	8.0	18000	0.9500	0.6777	0.8005	0.9399	0.7093	0.7857	0.7006	0.6132	0.9574	0.6719	0.9108	0.9499	0.8358	0.8035	0.8172	0.6634	0.9909	0.8249	0.9405	0.5649	0.6957	0.7319	0.8177	0.7674	0.8684	0.9584	0.8323	0.5724	0.6381	0.5655	0.5112	0.9189	0.4591	0.8012	0.8812	0.6700	0.5631	0.6707	0.5631	0.9807	0.6972	0.8931	0.4485	0.5452	0.5741	0.7061	0.5525	0.7896	0.8983	0.6877
0.8843	8.4444	19000	0.9511	0.6796	0.7993	0.9401	0.7004	0.8178	0.6519	0.7174	0.9572	0.6224	0.9077	0.9474	0.8269	0.7723	0.7965	0.6727	0.9898	0.8237	0.9426	0.5698	0.6640	0.7518	0.8313	0.7532	0.8897	0.9604	0.8164	0.5690	0.6276	0.5562	0.5450	0.9174	0.4548	0.7880	0.8860	0.6724	0.5485	0.6921	0.5652	0.9808	0.6932	0.8931	0.4511	0.5478	0.5682	0.7127	0.5552	0.7931	0.9005	0.7126
0.9566	8.8889	20000	0.9472	0.6787	0.7985	0.9404	0.7049	0.7575	0.6818	0.6768	0.9555	0.6506	0.9006	0.9482	0.8200	0.7445	0.8169	0.7053	0.9907	0.8590	0.9422	0.5622	0.6687	0.7411	0.8172	0.7526	0.8892	0.9563	0.8225	0.5685	0.6161	0.5442	0.5352	0.9182	0.4805	0.7979	0.8858	0.6724	0.5217	0.7002	0.5769	0.9808	0.6892	0.8930	0.4473	0.5409	0.5736	0.7173	0.5570	0.7833	0.9012	0.7092
0.9415	9.3333	21000	0.9548	0.6777	0.7951	0.9399	0.6808	0.8055	0.6928	0.6894	0.9626	0.6218	0.8789	0.9473	0.8325	0.7719	0.7636	0.6931	0.9905	0.8210	0.9433	0.5703	0.6522	0.7388	0.8204	0.7483	0.8854	0.9558	0.8208	0.5609	0.6273	0.5627	0.5270	0.9183	0.4640	0.7927	0.8843	0.6738	0.5507	0.6784	0.5680	0.9808	0.6784	0.8926	0.4504	0.5386	0.5761	0.7179	0.5593	0.7798	0.9016	0.7029
0.9323	9.7778	22000	0.9473	0.6801	0.8004	0.9401	0.6976	0.7806	0.7003	0.6717	0.9568	0.5970	0.9007	0.9481	0.8302	0.7417	0.8203	0.6961	0.9901	0.8677	0.9394	0.5692	0.7008	0.7428	0.8164	0.7686	0.8670	0.9575	0.8475	0.5692	0.6195	0.5507	0.5221	0.9184	0.4674	0.7960	0.8852	0.6797	0.5621	0.6920	0.5766	0.9808	0.6814	0.8924	0.4532	0.5521	0.5755	0.7186	0.5649	0.7876	0.9007	0.6960
0.9317	10.2222	23000	0.9506	0.6815	0.8006	0.9404	0.7121	0.8045	0.7110	0.6755	0.9526	0.6830	0.9059	0.9489	0.8399	0.7169	0.8078	0.7014	0.9907	0.8323	0.9433	0.5671	0.6756	0.7389	0.8177	0.7487	0.8808	0.9624	0.7964	0.5753	0.6195	0.5531	0.5441	0.9163	0.4886	0.7851	0.8864	0.6739	0.5363	0.7008	0.5833	0.9809	0.6951	0.8940	0.4526	0.5529	0.5792	0.7162	0.5601	0.7802	0.9008	0.7007
1.0166	10.6667	24000	0.9497	0.6823	0.8009	0.9409	0.7061	0.8256	0.7003	0.6733	0.9620	0.6408	0.8786	0.9487	0.8402	0.7293	0.8262	0.6825	0.9900	0.8168	0.9442	0.5813	0.6780	0.7523	0.8205	0.7576	0.8812	0.9605	0.8254	0.5732	0.6280	0.5625	0.5400	0.9194	0.4772	0.7939	0.8869	0.6747	0.5375	0.6894	0.5738	0.9810	0.6867	0.8939	0.4566	0.5533	0.5755	0.7220	0.5683	0.7850	0.9019	0.7119
0.9552	11.1111	25000	0.9514	0.6839	0.7956	0.9410	0.6848	0.8055	0.6767	0.6773	0.9627	0.5812	0.8873	0.9497	0.8387	0.7154	0.7963	0.6822	0.9906	0.8297	0.9442	0.5835	0.7141	0.7414	0.8294	0.7613	0.8648	0.9590	0.8237	0.5684	0.6419	0.5621	0.5352	0.9190	0.4497	0.7908	0.8864	0.6778	0.5532	0.6995	0.5788	0.9809	0.6913	0.8940	0.4588	0.5643	0.5798	0.7216	0.5840	0.7834	0.9024	0.7064
0.9968	11.5556	26000	0.9473	0.6823	0.8022	0.9405	0.6980	0.8150	0.6930	0.6878	0.9563	0.6225	0.9037	0.9493	0.8403	0.7341	0.8076	0.6876	0.9899	0.8503	0.9409	0.5806	0.7029	0.7571	0.8294	0.7585	0.8656	0.9608	0.8181	0.5709	0.6372	0.5610	0.5313	0.9178	0.4644	0.7953	0.8871	0.6764	0.5590	0.6897	0.5793	0.9810	0.6847	0.8930	0.4576	0.5509	0.5765	0.7208	0.5659	0.7791	0.9003	0.7139
0.9555	12.0	27000	0.9485	0.6820	0.7971	0.9410	0.6763	0.8329	0.6877	0.6743	0.9597	0.5537	0.8978	0.9477	0.8351	0.7162	0.7749	0.7018	0.9903	0.8405	0.9465	0.5790	0.6895	0.7582	0.8251	0.7846	0.8723	0.9619	0.8265	0.5656	0.6363	0.5703	0.5333	0.9185	0.4344	0.7931	0.8880	0.6813	0.5455	0.6994	0.5813	0.9810	0.6898	0.8941	0.4572	0.5481	0.5793	0.7227	0.5605	0.7893	0.9011	0.7158
0.9325	12.4444	28000	0.9490	0.6829	0.8029	0.9412	0.6928	0.7981	0.7084	0.6846	0.9600	0.6005	0.8961	0.9461	0.8434	0.7252	0.8082	0.6989	0.9895	0.8267	0.9466	0.5831	0.7146	0.7602	0.8321	0.7699	0.8824	0.9595	0.8409	0.5665	0.6315	0.5542	0.5330	0.9202	0.4673	0.7991	0.8880	0.6772	0.5431	0.7031	0.5830	0.9808	0.6882	0.8944	0.4585	0.5421	0.5785	0.7214	0.5722	0.7859	0.9029	0.7148
0.9039	12.8889	29000	0.9449	0.6862	0.7980	0.9417	0.6883	0.8044	0.6908	0.6811	0.9607	0.5807	0.8982	0.9472	0.8457	0.7476	0.7988	0.7018	0.9902	0.8109	0.9498	0.5777	0.6796	0.7492	0.8243	0.7585	0.8824	0.9601	0.8252	0.5666	0.6462	0.5669	0.5394	0.9201	0.4516	0.7983	0.8889	0.6767	0.5723	0.7040	0.5827	0.9811	0.6910	0.8946	0.4583	0.5528	0.5807	0.7217	0.5810	0.7921	0.9035	0.7126
0.936	13.3333	30000	0.9458	0.6848	0.8025	0.9414	0.7056	0.7957	0.7069	0.6933	0.9611	0.6014	0.8949	0.9441	0.8329	0.7333	0.8139	0.6904	0.9892	0.8341	0.9471	0.5854	0.7129	0.7506	0.8271	0.7628	0.8837	0.9617	0.8291	0.5683	0.6342	0.5463	0.5447	0.9202	0.4538	0.7957	0.8889	0.6829	0.5634	0.7052	0.5821	0.9809	0.6864	0.8944	0.4613	0.5455	0.5809	0.7217	0.5705	0.7921	0.9039	0.7260
0.9636	13.7778	31000	0.9470	0.6856	0.8003	0.9416	0.6972	0.8154	0.6967	0.6890	0.9617	0.6171	0.8940	0.9500	0.8438	0.7202	0.7881	0.7000	0.9909	0.8198	0.9431	0.5814	0.6997	0.7480	0.8320	0.7590	0.8715	0.9590	0.8301	0.5733	0.6329	0.5570	0.5440	0.9204	0.4667	0.7962	0.8880	0.6784	0.5502	0.7071	0.5841	0.9811	0.6878	0.8946	0.4602	0.5583	0.5827	0.7208	0.5780	0.7890	0.9039	0.7138
0.8986	14.2222	32000	0.9472	0.6864	0.8013	0.9417	0.6857	0.7904	0.6909	0.6910	0.9619	0.6165	0.8941	0.9449	0.8406	0.7531	0.8035	0.7000	0.9899	0.8171	0.9487	0.5884	0.6967	0.7541	0.8281	0.7664	0.8738	0.9624	0.8329	0.5684	0.6394	0.5559	0.5415	0.9206	0.4706	0.7962	0.8889	0.6812	0.5703	0.7083	0.5841	0.9811	0.6856	0.8946	0.4616	0.5548	0.5814	0.7220	0.5702	0.7908	0.9032	0.7170
0.9031	14.6667	33000	0.9465	0.6876	0.8034	0.9418	0.6800	0.8164	0.6949	0.6949	0.9600	0.6083	0.8963	0.9474	0.8455	0.7457	0.8079	0.7057	0.9903	0.8351	0.9468	0.5784	0.6875	0.7517	0.8324	0.7600	0.8898	0.9598	0.8427	0.5668	0.6378	0.5595	0.5403	0.9205	0.4618	0.7968	0.8891	0.6797	0.5718	0.7125	0.5860	0.9811	0.6872	0.8949	0.4599	0.5531	0.5835	0.7231	0.5797	0.7946	0.9055	0.7304

Framework versions

Transformers 4.55.2
Pytorch 2.7.1+cu118
Datasets 4.0.0
Tokenizers 0.21.4

Vrjb/SegFormer_b3_mappillary_

SegFormer_b3_mappillary_

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Vrjb/
SegFormer_b3_mappillary_