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Vrjb/
SegFormer_b2_2

Name: SegFormer_b2_2
Availability: InStock

transformers

safetensors

segformer

generated_from_trainer

nvidia/segformer-b2-finetuned-cityscapes-1024-1024

finetune

other

endpoints_compatible

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SegFormer_b2_2

This model is a fine-tuned version of nvidia/segformer-b2-finetuned-cityscapes-1024-1024 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5687
Mean Iou: 0.7141
Mean Accuracy: 0.8338
Overall Accuracy: 0.9518
Accuracy Road: 0.9861
Accuracy Sidewalk: 0.9403
Accuracy Building: 0.9549
Accuracy Wall: 0.7046
Accuracy Fence: 0.7106
Accuracy Pole: 0.6880
Accuracy Traffic light: 0.8719
Accuracy Traffic sign: 0.8349
Accuracy Vegetation: 0.9442
Accuracy Terrain: 0.6876
Accuracy Sky: 0.9817
Accuracy Person: 0.8778
Accuracy Rider: 0.5796
Accuracy Car: 0.9746
Accuracy Truck: 0.7663
Accuracy Bus: 0.9041
Accuracy Train: 0.7933
Accuracy Motorcycle: 0.7614
Accuracy Bicycle: 0.8798
Iou Road: 0.9809
Iou Sidewalk: 0.8418
Iou Building: 0.9125
Iou Wall: 0.5459
Iou Fence: 0.5277
Iou Pole: 0.5466
Iou Traffic light: 0.6398
Iou Traffic sign: 0.7499
Iou Vegetation: 0.9115
Iou Terrain: 0.5282
Iou Sky: 0.9396
Iou Person: 0.7568
Iou Rider: 0.4521
Iou Car: 0.9319
Iou Truck: 0.6160
Iou Bus: 0.7445
Iou Train: 0.6995
Iou Motorcycle: 0.5142
Iou Bicycle: 0.7285

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: 0.0002
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: 130
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Road	Accuracy Sidewalk	Accuracy Building	Accuracy Wall	Accuracy Fence	Accuracy Pole	Accuracy Traffic light	Accuracy Traffic sign	Accuracy Vegetation	Accuracy Terrain	Accuracy Sky	Accuracy Person	Accuracy Rider	Accuracy Car	Accuracy Truck	Accuracy Bus	Accuracy Train	Accuracy Motorcycle	Accuracy Bicycle	Iou Road	Iou Sidewalk	Iou Building	Iou Wall	Iou Fence	Iou Pole	Iou Traffic light	Iou Traffic sign	Iou Vegetation	Iou Terrain	Iou Sky	Iou Person	Iou Rider	Iou Car	Iou Truck	Iou Bus	Iou Train	Iou Motorcycle	Iou Bicycle
0.6593	0.2688	100	0.5767	0.7490	0.8653	0.9557	0.9879	0.9291	0.9527	0.6811	0.6907	0.7639	0.8966	0.8935	0.9505	0.7808	0.9856	0.9001	0.7336	0.9768	0.7932	0.9450	0.8737	0.8252	0.8802	0.9820	0.8569	0.9152	0.6022	0.5665	0.5525	0.6085	0.7337	0.9183	0.6399	0.9381	0.7709	0.5525	0.9413	0.7157	0.8153	0.8165	0.5705	0.7338
0.6791	0.5376	200	0.5698	0.7492	0.8600	0.9561	0.9880	0.9336	0.9583	0.6325	0.7103	0.7513	0.8553	0.8639	0.9489	0.7918	0.9852	0.8956	0.7517	0.9729	0.7818	0.9393	0.8832	0.8112	0.8840	0.9821	0.8560	0.9163	0.5714	0.5725	0.5599	0.6532	0.7479	0.9181	0.6287	0.9407	0.7748	0.5506	0.9428	0.6955	0.8014	0.8020	0.5780	0.7421
0.684	0.8065	300	0.5660	0.7540	0.8645	0.9570	0.9883	0.9310	0.9544	0.7076	0.7504	0.7494	0.8755	0.8758	0.9524	0.8069	0.9867	0.8878	0.7234	0.9740	0.7900	0.9457	0.8782	0.7426	0.9050	0.9827	0.8597	0.9187	0.6108	0.5737	0.5617	0.6427	0.7589	0.9198	0.6545	0.9430	0.7718	0.5517	0.9434	0.7264	0.7988	0.7947	0.5935	0.7189
0.658	1.0753	400	0.5648	0.7555	0.8713	0.9566	0.9876	0.9253	0.9519	0.7672	0.6932	0.7528	0.8699	0.8854	0.9538	0.7916	0.9818	0.9127	0.7634	0.9750	0.8688	0.9482	0.8984	0.7359	0.8924	0.9820	0.8559	0.9184	0.6372	0.5723	0.5547	0.6505	0.7551	0.9188	0.6353	0.9460	0.7588	0.5468	0.9409	0.7869	0.8348	0.7207	0.6050	0.7344
0.5832	1.3441	500	0.5662	0.7441	0.8714	0.9555	0.9888	0.9327	0.9422	0.7124	0.7149	0.7371	0.8847	0.8679	0.9616	0.7678	0.9862	0.8949	0.7883	0.9697	0.8754	0.9480	0.9008	0.7969	0.8863	0.9828	0.8533	0.9149	0.6197	0.5738	0.5542	0.6272	0.7509	0.9178	0.6262	0.9390	0.7630	0.5299	0.9410	0.7117	0.8474	0.7103	0.5715	0.7036
0.5949	1.6129	600	0.5658	0.7512	0.8710	0.9555	0.9878	0.9256	0.9433	0.7911	0.7468	0.7528	0.9006	0.8701	0.9563	0.7578	0.9817	0.9151	0.7248	0.9787	0.9052	0.9474	0.8130	0.7913	0.8603	0.9815	0.8545	0.9147	0.6437	0.5660	0.5541	0.6132	0.7441	0.9176	0.6410	0.9440	0.7493	0.5356	0.9394	0.7897	0.8492	0.7631	0.5436	0.7285
0.5894	1.8817	700	0.5636	0.7371	0.8666	0.9543	0.9869	0.9263	0.9460	0.7916	0.6749	0.7552	0.8802	0.8670	0.9504	0.8268	0.9835	0.9047	0.7147	0.9759	0.8055	0.9644	0.7929	0.8427	0.8767	0.9815	0.8548	0.9136	0.6382	0.5519	0.5478	0.6420	0.7505	0.9150	0.6215	0.9411	0.7632	0.5342	0.9389	0.7291	0.7657	0.7170	0.4891	0.7087
0.5715	2.1505	800	0.5679	0.7431	0.8700	0.9549	0.9862	0.9338	0.9460	0.7668	0.7404	0.7456	0.8754	0.8627	0.9608	0.7677	0.9812	0.8570	0.8195	0.9624	0.8757	0.9346	0.9068	0.7152	0.8921	0.9817	0.8523	0.9154	0.6357	0.5846	0.5438	0.6327	0.7417	0.9165	0.6558	0.9434	0.7518	0.4961	0.9354	0.6757	0.8227	0.7332	0.5868	0.7143
0.6365	2.4194	900	0.5647	0.7432	0.8610	0.9548	0.9870	0.9341	0.9505	0.6885	0.6909	0.7165	0.8781	0.8895	0.9547	0.7721	0.9881	0.8999	0.7258	0.9706	0.8670	0.9193	0.9065	0.7449	0.8744	0.9820	0.8544	0.9128	0.5677	0.5229	0.5553	0.6414	0.7557	0.9180	0.6489	0.9289	0.7704	0.5566	0.9367	0.7645	0.8216	0.7082	0.5442	0.7298
0.6795	2.6882	1000	0.5673	0.7301	0.8648	0.9525	0.9838	0.9305	0.9489	0.6736	0.7227	0.7042	0.9196	0.8746	0.9526	0.7629	0.9884	0.8850	0.7949	0.9674	0.8915	0.9001	0.8440	0.7920	0.8939	0.9788	0.8417	0.9104	0.5765	0.5507	0.5537	0.5471	0.7434	0.9160	0.6141	0.9285	0.7561	0.5163	0.9338	0.7324	0.8068	0.7732	0.4745	0.7187
0.6517	2.9570	1100	0.5647	0.7173	0.8507	0.9512	0.9836	0.9402	0.9490	0.7080	0.6173	0.7459	0.8519	0.8803	0.9454	0.7947	0.9821	0.8759	0.7743	0.9645	0.9189	0.9356	0.7035	0.7064	0.8856	0.9796	0.8370	0.9103	0.5719	0.5054	0.5426	0.6418	0.7274	0.9125	0.5885	0.9426	0.7510	0.5055	0.9346	0.7204	0.7279	0.6447	0.4707	0.7137
0.6038	3.2258	1200	0.5645	0.7330	0.8552	0.9526	0.9843	0.9456	0.9536	0.7353	0.7254	0.7528	0.8507	0.8675	0.9411	0.7901	0.9830	0.8741	0.7417	0.9695	0.7350	0.8992	0.8817	0.7758	0.8421	0.9799	0.8397	0.9131	0.6145	0.5277	0.5483	0.6496	0.7479	0.9124	0.5919	0.9413	0.7624	0.5452	0.9372	0.6455	0.7698	0.7256	0.5486	0.7257
0.6218	3.4946	1300	0.5641	0.7224	0.8571	0.9524	0.9863	0.9358	0.9425	0.7547	0.6836	0.7491	0.8775	0.8444	0.9505	0.8109	0.9820	0.9127	0.7426	0.9710	0.6970	0.9102	0.9148	0.7668	0.8517	0.9810	0.8483	0.9106	0.6126	0.5573	0.5396	0.6359	0.7261	0.9120	0.6001	0.9429	0.7474	0.5381	0.9369	0.6221	0.7279	0.6511	0.5116	0.7233
0.5334	3.7634	1400	0.5687	0.7141	0.8338	0.9518	0.9861	0.9403	0.9549	0.7046	0.7106	0.6880	0.8719	0.8349	0.9442	0.6876	0.9817	0.8778	0.5796	0.9746	0.7663	0.9041	0.7933	0.7614	0.8798	0.9809	0.8418	0.9125	0.5459	0.5277	0.5466	0.6398	0.7499	0.9115	0.5282	0.9396	0.7568	0.4521	0.9319	0.6160	0.7445	0.6995	0.5142	0.7285

Framework versions

Transformers 4.48.0
Pytorch 2.1.2+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Vrjb/SegFormer_b2_2

SegFormer_b2_2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Vrjb/
SegFormer_b2_2