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selvaa/
segformer-b1-finetuned-cityscapes-1024-1024-latestt

Name: segformer-b1-finetuned-cityscapes-1024-1024-latestt
Availability: InStock

generated_from_trainer

nvidia/segformer-b1-finetuned-cityscapes-1024-1024

finetune

other

endpoints_compatible

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segformer-b1-finetuned-cityscapes-1024-1024-latestt

This model is a fine-tuned version of nvidia/segformer-b1-finetuned-cityscapes-1024-1024 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0311
Mean Iou: 0.9368
Mean Accuracy: 0.9605
Overall Accuracy: 0.9893
Accuracy Default: 1e-06
Accuracy Pipe: 0.8961
Accuracy Floor: 0.9893
Accuracy Background: 0.9960
Iou Default: 1e-06
Iou Pipe: 0.8401
Iou Floor: 0.9817
Iou Background: 0.9886

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: 3
eval_batch_size: 3
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Default	Accuracy Pipe	Accuracy Floor	Accuracy Background	Iou Default	Iou Pipe	Iou Floor	Iou Background
0.5401	1.0	36	0.2151	0.7616	0.8215	0.9549	1e-06	0.5060	0.9791	0.9793	1e-06	0.4188	0.9090	0.9571
0.1576	2.0	72	0.1166	0.8481	0.8906	0.9737	1e-06	0.7051	0.9739	0.9929	1e-06	0.6121	0.9598	0.9724
0.0941	3.0	108	0.0739	0.8869	0.9301	0.9802	1e-06	0.8180	0.9807	0.9916	1e-06	0.7164	0.9646	0.9798
0.0678	4.0	144	0.0600	0.9001	0.9439	0.9824	1e-06	0.8588	0.9814	0.9916	1e-06	0.7499	0.9685	0.9820
0.0566	5.0	180	0.0493	0.9087	0.9531	0.9841	1e-06	0.8887	0.9778	0.9929	1e-06	0.7711	0.9711	0.9840
0.0463	6.0	216	0.0454	0.9115	0.9455	0.9847	1e-06	0.8608	0.9809	0.9949	1e-06	0.7769	0.9735	0.9840
0.0412	7.0	252	0.0425	0.9138	0.9469	0.9854	1e-06	0.8611	0.9851	0.9945	1e-06	0.7823	0.9739	0.9852
0.038	8.0	288	0.0379	0.9226	0.9550	0.9868	1e-06	0.8864	0.9836	0.9951	1e-06	0.8049	0.9766	0.9864
0.0337	9.0	324	0.0392	0.9184	0.9468	0.9862	1e-06	0.8626	0.9812	0.9966	1e-06	0.7935	0.9765	0.9853
0.0323	10.0	360	0.0350	0.9246	0.9544	0.9872	1e-06	0.8827	0.9852	0.9953	1e-06	0.8095	0.9779	0.9864
0.0294	11.0	396	0.0350	0.9253	0.9523	0.9873	1e-06	0.8725	0.9898	0.9947	1e-06	0.8114	0.9783	0.9863
0.0275	12.0	432	0.0379	0.9185	0.9461	0.9862	1e-06	0.8606	0.9810	0.9968	1e-06	0.7950	0.9748	0.9857
0.0279	13.0	468	0.0333	0.9267	0.9572	0.9875	1e-06	0.8914	0.9853	0.9951	1e-06	0.8160	0.9770	0.9871
0.0269	14.0	504	0.0323	0.9267	0.9495	0.9878	1e-06	0.8640	0.9878	0.9967	1e-06	0.8141	0.9790	0.9872
0.0239	15.0	540	0.0300	0.9324	0.9570	0.9886	1e-06	0.8864	0.9887	0.9959	1e-06	0.8290	0.9802	0.9880
0.0229	16.0	576	0.0303	0.9343	0.9610	0.9888	1e-06	0.9005	0.9867	0.9959	1e-06	0.8344	0.9801	0.9882
0.0217	17.0	612	0.0318	0.9290	0.9531	0.9882	1e-06	0.8743	0.9889	0.9962	1e-06	0.8197	0.9797	0.9877
0.021	18.0	648	0.0305	0.9314	0.9540	0.9886	1e-06	0.8756	0.9904	0.9961	1e-06	0.8256	0.9806	0.9879
0.0209	19.0	684	0.0296	0.9344	0.9637	0.9887	1e-06	0.9059	0.9915	0.9937	1e-06	0.8357	0.9795	0.9880
0.0199	20.0	720	0.0306	0.9335	0.9585	0.9888	1e-06	0.8896	0.9902	0.9955	1e-06	0.8319	0.9802	0.9883
0.0187	21.0	756	0.0305	0.9331	0.9576	0.9887	1e-06	0.8877	0.9891	0.9959	1e-06	0.8308	0.9803	0.9881
0.0184	22.0	792	0.0298	0.9353	0.9594	0.9891	1e-06	0.8926	0.9896	0.9959	1e-06	0.8364	0.9810	0.9884
0.0175	23.0	828	0.0301	0.9340	0.9576	0.9889	1e-06	0.8866	0.9906	0.9957	1e-06	0.8332	0.9806	0.9882
0.0174	24.0	864	0.0288	0.9360	0.9633	0.9891	1e-06	0.9062	0.9883	0.9953	1e-06	0.8389	0.9805	0.9885
0.0178	25.0	900	0.0306	0.9343	0.9603	0.9889	1e-06	0.8956	0.9902	0.9951	1e-06	0.8342	0.9804	0.9882
0.0165	26.0	936	0.0304	0.9349	0.9579	0.9891	1e-06	0.8884	0.9889	0.9963	1e-06	0.8353	0.9810	0.9883
0.016	27.0	972	0.0300	0.9352	0.9597	0.9891	1e-06	0.8934	0.9902	0.9956	1e-06	0.8362	0.9810	0.9884
0.0159	28.0	1008	0.0311	0.9343	0.9575	0.9890	1e-06	0.8872	0.9891	0.9962	1e-06	0.8340	0.9804	0.9884
0.0157	29.0	1044	0.0302	0.9362	0.9631	0.9891	1e-06	0.9050	0.9894	0.9951	1e-06	0.8389	0.9813	0.9883
0.015	30.0	1080	0.0312	0.9344	0.9601	0.9890	1e-06	0.8959	0.9887	0.9957	1e-06	0.8340	0.9809	0.9883
0.0162	31.0	1116	0.0334	0.9321	0.9558	0.9886	1e-06	0.8833	0.9876	0.9965	1e-06	0.8276	0.9807	0.9879
0.0144	32.0	1152	0.0312	0.9352	0.9610	0.9890	1e-06	0.8976	0.9900	0.9952	1e-06	0.8366	0.9805	0.9883
0.0147	33.0	1188	0.0299	0.9375	0.9607	0.9895	1e-06	0.8960	0.9902	0.9959	1e-06	0.8419	0.9817	0.9888
0.0144	34.0	1224	0.0323	0.9342	0.9592	0.9889	1e-06	0.8937	0.9879	0.9961	1e-06	0.8341	0.9802	0.9884
0.0144	35.0	1260	0.0303	0.9359	0.9608	0.9892	1e-06	0.8977	0.9890	0.9959	1e-06	0.8378	0.9812	0.9886
0.014	36.0	1296	0.0314	0.9359	0.9577	0.9892	1e-06	0.8878	0.9886	0.9967	1e-06	0.8378	0.9814	0.9885
0.0136	37.0	1332	0.0316	0.9365	0.9600	0.9893	1e-06	0.8954	0.9883	0.9963	1e-06	0.8397	0.9813	0.9885
0.0138	38.0	1368	0.0325	0.9352	0.9577	0.9891	1e-06	0.8869	0.9899	0.9962	1e-06	0.8361	0.9812	0.9884
0.0137	39.0	1404	0.0316	0.9363	0.9597	0.9893	1e-06	0.8933	0.9896	0.9960	1e-06	0.8391	0.9811	0.9886
0.0132	40.0	1440	0.0320	0.9353	0.9590	0.9891	1e-06	0.8930	0.9876	0.9965	1e-06	0.8368	0.9807	0.9884
0.0129	41.0	1476	0.0311	0.9368	0.9605	0.9893	1e-06	0.8961	0.9893	0.9960	1e-06	0.8401	0.9817	0.9886

Framework versions

Transformers 4.35.2
Pytorch 2.0.1
Datasets 2.15.0
Tokenizers 0.15.0

selvaa/segformer-b1-finetuned-cityscapes-1024-1024-latestt

segformer-b1-finetuned-cityscapes-1024-1024-latestt

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

selvaa/
segformer-b1-finetuned-cityscapes-1024-1024-latestt