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Vrjb/
SegFormer_b2_

Name: SegFormer_b2_
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_

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: 1.9619
Mean Iou: 0.6350
Mean Accuracy: 0.7337
Overall Accuracy: 0.9277
Accuracy Road: 0.9852
Accuracy Sidewalk: 0.8497
Accuracy Building: 0.9358
Accuracy Wall: 0.5532
Accuracy Fence: 0.5198
Accuracy Pole: 0.5455
Accuracy Traffic light: 0.6870
Accuracy Traffic sign: 0.7070
Accuracy Vegetation: 0.9414
Accuracy Terrain: 0.6285
Accuracy Sky: 0.9488
Accuracy Person: 0.7732
Accuracy Rider: 0.5306
Accuracy Car: 0.9299
Accuracy Truck: 0.6569
Accuracy Bus: 0.7685
Accuracy Train: 0.6389
Accuracy Motorcycle: 0.6015
Accuracy Bicycle: 0.7396
Iou Road: 0.9605
Iou Sidewalk: 0.7409
Iou Building: 0.8722
Iou Wall: 0.4867
Iou Fence: 0.4444
Iou Pole: 0.3637
Iou Traffic light: 0.4816
Iou Traffic sign: 0.5829
Iou Vegetation: 0.8786
Iou Terrain: 0.5591
Iou Sky: 0.9110
Iou Person: 0.6011
Iou Rider: 0.3690
Iou Car: 0.8721
Iou Truck: 0.5861
Iou Bus: 0.7113
Iou Train: 0.6222
Iou Motorcycle: 0.4337
Iou Bicycle: 0.5882

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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: 500
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy Bicycle	Accuracy Building	Accuracy Bus	Accuracy Car	Accuracy Fence	Accuracy Motorcycle	Accuracy Person	Accuracy Pole	Accuracy Rider	Accuracy Road	Accuracy Sidewalk	Accuracy Sky	Accuracy Terrain	Accuracy Traffic light	Accuracy Traffic sign	Accuracy Train	Accuracy Truck	Accuracy Vegetation	Accuracy Wall	Iou Bicycle	Iou Building	Iou Bus	Iou Car	Iou Fence	Iou Motorcycle	Iou Person	Iou Pole	Iou Rider	Iou Road	Iou Sidewalk	Iou Sky	Iou Terrain	Iou Traffic light	Iou Traffic sign	Iou Train	Iou Truck	Iou Vegetation	Iou Wall	Validation Loss	Mean Accuracy	Mean Iou	Overall Accuracy
6.8123	2.1290	100	0.6984	0.9338	0.8122	0.9277	0.5514	0.5692	0.7411	0.5353	0.5676	0.9832	0.8382	0.9456	0.6402	0.6671	0.6933	0.6673	0.6179	0.9322	0.5422	0.5707	0.8673	0.7055	0.8721	0.4429	0.4264	0.5663	0.3490	0.3608	0.9568	0.7169	0.9046	0.5479	0.4801	0.5658	0.6310	0.5609	0.8722	0.4836	1.9557	0.7297	0.6253	0.9235
5.5587	4.2581	200	0.7256	0.9379	0.7677	0.9250	0.5431	0.5447	0.7266	0.5255	0.5780	0.9827	0.8446	0.9559	0.6846	0.6513	0.6983	0.7100	0.6409	0.9234	0.5933	0.5811	0.8659	0.6798	0.8731	0.4480	0.4214	0.5739	0.3477	0.3697	0.9569	0.7187	0.9050	0.5508	0.4799	0.5715	0.6571	0.5627	0.8720	0.5035	1.9616	0.7347	0.6284	0.9237
6.8729	6.3871	300	0.7543	0.9334	0.7977	0.9311	0.5201	0.4788	0.7494	0.5370	0.5464	0.9821	0.8502	0.9495	0.6429	0.6814	0.6879	0.6860	0.6341	0.9325	0.5513	0.5783	0.8654	0.7097	0.8738	0.4383	0.3938	0.5814	0.3495	0.3657	0.9587	0.7267	0.9026	0.5385	0.4817	0.5703	0.6481	0.5721	0.8733	0.4796	2.0128	0.7287	0.6267	0.9243
6.3252	8.5161	400	0.6642	0.9384	0.7729	0.9231	0.5929	0.4225	0.7956	0.5462	0.5785	0.9811	0.8564	0.9407	0.6866	0.6477	0.7079	0.7334	0.6215	0.9150	0.5680	0.5609	0.8657	0.6914	0.8719	0.4471	0.3463	0.5605	0.3529	0.3504	0.9575	0.7210	0.9026	0.5582	0.4800	0.5663	0.6632	0.5712	0.8702	0.4953	2.0218	0.7312	0.6228	0.9227
6.3303	10.6452	500	0.7177	0.9441	0.8494	0.9181	0.5622	0.5395	0.7004	0.5211	0.5410	0.9835	0.8381	0.9392	0.6200	0.6394	0.6761	0.6616	0.6112	0.9303	0.4396	0.5813	0.8632	0.6945	0.8714	0.4555	0.4227	0.5589	0.3463	0.3686	0.9574	0.7176	0.9037	0.5494	0.4400	0.5567	0.6219	0.5686	0.8730	0.4040	2.0294	0.7175	0.6187	0.9231
6.1549	12.7742	600	0.7508	0.9573	0.7949	0.9217	0.5561	0.6094	0.7175	0.4992	0.3451	0.9822	0.8461	0.9377	0.6346	0.6769	0.6496	0.6472	0.5465	0.8897	0.5458	0.5683	0.8503	0.6773	0.8694	0.4253	0.4378	0.5759	0.3383	0.2860	0.9581	0.7156	0.8943	0.5439	0.4632	0.5551	0.6143	0.5262	0.8543	0.4745	2.1125	0.7110	0.6120	0.9192
7.3083	14.9032	700	0.7100	0.9398	0.8242	0.9160	0.5903	0.6395	0.7402	0.5186	0.5756	0.9850	0.8476	0.9476	0.6635	0.6793	0.7236	0.6976	0.7515	0.9247	0.5481	0.5762	0.8665	0.7249	0.8740	0.4511	0.4341	0.5749	0.3518	0.3727	0.9589	0.7281	0.9005	0.5730	0.4763	0.5694	0.6395	0.6627	0.8741	0.4982	2.0793	0.7486	0.6372	0.9252
6.576	17.0215	800	0.7703	0.9409	0.7932	0.9190	0.5249	0.5203	0.7513	0.5143	0.5919	0.9821	0.8585	0.9530	0.6539	0.6359	0.6807	0.6271	0.6034	0.9322	0.4577	0.5755	0.8684	0.6832	0.8710	0.4431	0.3965	0.5864	0.3538	0.3662	0.9589	0.7242	0.9068	0.5658	0.4663	0.5700	0.5985	0.5316	0.8743	0.4129	2.0442	0.7216	0.6186	0.9247
6.1906	19.1505	900	0.6906	0.9467	0.8470	0.9224	0.5170	0.4961	0.7813	0.5332	0.5720	0.9847	0.8472	0.9447	0.6608	0.7185	0.7102	0.4138	0.5740	0.9189	0.4473	0.5695	0.8643	0.6888	0.8751	0.4150	0.3911	0.5883	0.3517	0.3777	0.9604	0.7371	0.9050	0.5748	0.4555	0.5705	0.4080	0.5304	0.8708	0.4035	2.0855	0.7119	0.6072	0.9242
7.41	21.2796	1000	0.7659	0.9404	0.7758	0.9210	0.5056	0.5361	0.7416	0.5777	0.6048	0.9830	0.8693	0.9475	0.6439	0.6821	0.7203	0.6634	0.6090	0.9171	0.4731	0.5747	0.8631	0.7228	0.8708	0.4197	0.4150	0.5772	0.3588	0.3654	0.9599	0.7358	0.9058	0.5547	0.4700	0.5752	0.6282	0.5567	0.8737	0.4317	2.0099	0.7304	0.6242	0.9239
5.5115	23.4086	1100	0.7564	0.9446	0.7604	0.9148	0.6497	0.5345	0.7515	0.5375	0.5970	0.9812	0.8744	0.9407	0.6428	0.6566	0.6718	0.7071	0.6244	0.9197	0.4471	0.5694	0.8659	0.7115	0.8685	0.4391	0.4190	0.5891	0.3549	0.3717	0.9597	0.7354	0.9072	0.5640	0.4857	0.5729	0.6665	0.5744	0.8741	0.4060	2.0197	0.7322	0.6282	0.9244
7.0305	25.5376	1200	0.7267	0.9504	0.7640	0.9260	0.5518	0.5643	0.7378	0.5290	0.5458	0.9839	0.8701	0.9395	0.6378	0.6620	0.6745	0.7368	0.6248	0.9167	0.5230	0.5713	0.8643	0.7156	0.8710	0.4571	0.4256	0.5882	0.3598	0.3668	0.9612	0.7406	0.9042	0.5554	0.4727	0.5726	0.6853	0.5750	0.8728	0.4675	1.9816	0.7297	0.6330	0.9258
5.2368	27.6667	1300	0.7217	0.9374	0.7948	0.9332	0.5699	0.5587	0.7443	0.5263	0.5490	0.9843	0.8599	0.9396	0.6362	0.6413	0.7024	0.7470	0.6401	0.9401	0.5039	0.5817	0.8697	0.7120	0.8732	0.4659	0.4127	0.5929	0.3582	0.3795	0.9610	0.7402	0.9039	0.5671	0.4764	0.5836	0.6913	0.5853	0.8782	0.4550	1.9868	0.7332	0.6362	0.9274
6.2172	29.7957	1400	0.7696	0.9371	0.7514	0.9329	0.5485	0.5655	0.7389	0.5560	0.5959	0.9858	0.8414	0.9414	0.6290	0.7111	0.6842	0.7097	0.5908	0.9342	0.5966	0.5896	0.8689	0.7058	0.8719	0.4575	0.4462	0.5838	0.3593	0.3561	0.9610	0.7383	0.9079	0.5570	0.4571	0.5676	0.6666	0.5487	0.8797	0.5196	2.0414	0.7379	0.6338	0.9268
5.8238	31.9247	1500	0.7155	0.9423	0.7631	0.9290	0.6080	0.5156	0.7468	0.5452	0.6095	0.9852	0.8504	0.9454	0.6533	0.6628	0.6916	0.6605	0.6024	0.9245	0.5715	0.5826	0.8678	0.7121	0.8697	0.4594	0.4105	0.5992	0.3578	0.3635	0.9609	0.7403	0.9070	0.5617	0.4771	0.5806	0.6362	0.5521	0.8765	0.5023	2.0376	0.7328	0.6325	0.9264
5.5947	34.0430	1600	0.7463	0.9465	0.8115	0.9198	0.5313	0.5075	0.7525	0.5320	0.5378	0.9834	0.8629	0.9352	0.6095	0.6516	0.6914	0.6838	0.6637	0.9242	0.5233	0.5755	0.8663	0.6922	0.8715	0.4515	0.4040	0.5862	0.3583	0.3677	0.9593	0.7355	0.9035	0.5453	0.4746	0.5805	0.6466	0.5881	0.8729	0.4686	1.9786	0.7271	0.6288	0.9253
6.8136	36.1720	1700	0.7539	0.9418	0.7692	0.9278	0.5493	0.5517	0.7664	0.5399	0.5394	0.9841	0.8605	0.9423	0.6269	0.6547	0.6952	0.6466	0.6442	0.9287	0.5673	0.5902	0.8702	0.7151	0.8725	0.4427	0.4200	0.5972	0.3619	0.3700	0.9602	0.7376	0.9088	0.5491	0.4791	0.5807	0.6186	0.5809	0.8764	0.4945	2.0119	0.7311	0.6329	0.9268
4.8895	39.0860	1800	1.9619	0.6350	0.7337	0.9277	0.9852	0.8497	0.9358	0.5532	0.5198	0.5455	0.6870	0.7070	0.9414	0.6285	0.9488	0.7732	0.5306	0.9299	0.6569	0.7685	0.6389	0.6015	0.7396	0.9605	0.7409	0.8722	0.4867	0.4444	0.3637	0.4816	0.5829	0.8786	0.5591	0.9110	0.6011	0.3690	0.8721	0.5861	0.7113	0.6222	0.4337	0.5882

Framework versions

Transformers 4.47.1
Pytorch 2.1.2+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Vrjb/SegFormer_b2_

SegFormer_b2_

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Vrjb/
SegFormer_b2_