segformerb5-finetuned-largerImages

This model is a fine-tuned version of JCAI2000/segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-2 on the JCAI2000/LargerImagesLabelled dataset. It achieves the following results on the evaluation set:

• Loss: 0.0724
• Mean Iou: 0.7754
• Mean Accuracy: 0.8589
• Overall Accuracy: 0.9828
• Accuracy Background: 0.9910
• Accuracy Branch: 0.7269
• Iou Background: 0.9824
• Iou Branch: 0.5684

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Branch	Iou Background	Iou Branch
0.0872	1.18	20	0.0678	0.6870	0.7241	0.9784	0.9954	0.4528	0.9781	0.3958
0.0848	2.35	40	0.0577	0.7333	0.7908	0.9806	0.9932	0.5884	0.9802	0.4864
0.0549	3.53	60	0.0634	0.7485	0.8857	0.9773	0.9834	0.7879	0.9767	0.5203
0.0653	4.71	80	0.0493	0.7662	0.8346	0.9827	0.9926	0.6767	0.9823	0.5500
0.0596	5.88	100	0.0476	0.7497	0.7920	0.9828	0.9955	0.5885	0.9825	0.5168
0.0458	7.06	120	0.0478	0.7636	0.8357	0.9823	0.9921	0.6793	0.9819	0.5452
0.0285	8.24	140	0.0458	0.7758	0.8574	0.9829	0.9913	0.7235	0.9825	0.5691
0.0341	9.41	160	0.0466	0.7670	0.8376	0.9827	0.9923	0.6829	0.9823	0.5517
0.0369	10.59	180	0.0491	0.7699	0.8731	0.9813	0.9885	0.7576	0.9809	0.5589
0.0352	11.76	200	0.0465	0.7731	0.8551	0.9826	0.9911	0.7191	0.9822	0.5640
0.0477	12.94	220	0.0462	0.7721	0.8415	0.9832	0.9926	0.6905	0.9828	0.5615
0.0404	14.12	240	0.0493	0.7704	0.8734	0.9814	0.9886	0.7583	0.9809	0.5599
0.0221	15.29	260	0.0458	0.7798	0.8719	0.9828	0.9901	0.7536	0.9823	0.5772
0.0263	16.47	280	0.0450	0.7778	0.8509	0.9835	0.9923	0.7096	0.9831	0.5726
0.0364	17.65	300	0.0489	0.7756	0.8537	0.9830	0.9917	0.7158	0.9827	0.5686
0.02	18.82	320	0.0493	0.7713	0.8474	0.9828	0.9918	0.7031	0.9824	0.5602
0.0193	20.0	340	0.0481	0.7786	0.8694	0.9827	0.9903	0.7484	0.9823	0.5749
0.0133	21.18	360	0.0486	0.7756	0.8552	0.9830	0.9915	0.7189	0.9826	0.5686
0.0163	22.35	380	0.0492	0.7768	0.8632	0.9828	0.9907	0.7357	0.9824	0.5713
0.0252	23.53	400	0.0510	0.7725	0.8605	0.9823	0.9904	0.7306	0.9819	0.5632
0.0178	24.71	420	0.0509	0.7770	0.8665	0.9826	0.9904	0.7427	0.9822	0.5719
0.0167	25.88	440	0.0516	0.7748	0.8615	0.9826	0.9906	0.7323	0.9822	0.5675
0.0332	27.06	460	0.0507	0.7702	0.8422	0.9829	0.9922	0.6921	0.9825	0.5578
0.021	28.24	480	0.0522	0.7710	0.8482	0.9827	0.9916	0.7048	0.9823	0.5597
0.0284	29.41	500	0.0536	0.7762	0.8643	0.9826	0.9905	0.7380	0.9822	0.5702
0.0174	30.59	520	0.0535	0.7739	0.8591	0.9826	0.9908	0.7274	0.9822	0.5657
0.0228	31.76	540	0.0527	0.7765	0.8578	0.9830	0.9913	0.7243	0.9826	0.5703
0.0347	32.94	560	0.0530	0.7754	0.8534	0.9830	0.9917	0.7151	0.9826	0.5681
0.0182	34.12	580	0.0554	0.7764	0.8670	0.9825	0.9902	0.7437	0.9821	0.5706
0.0139	35.29	600	0.0521	0.7786	0.8540	0.9834	0.9920	0.7159	0.9830	0.5742
0.0121	36.47	620	0.0549	0.7787	0.8728	0.9826	0.9899	0.7557	0.9822	0.5752
0.0191	37.65	640	0.0560	0.7766	0.8678	0.9825	0.9902	0.7454	0.9821	0.5711
0.0216	38.82	660	0.0553	0.7745	0.8543	0.9829	0.9914	0.7172	0.9825	0.5665
0.0135	40.0	680	0.0569	0.7738	0.8640	0.9823	0.9902	0.7379	0.9819	0.5658
0.0167	41.18	700	0.0566	0.7765	0.8619	0.9828	0.9908	0.7330	0.9824	0.5707
0.0224	42.35	720	0.0570	0.7768	0.8680	0.9825	0.9902	0.7458	0.9821	0.5714
0.0188	43.53	740	0.0575	0.7768	0.8630	0.9828	0.9907	0.7353	0.9824	0.5713
0.0338	44.71	760	0.0565	0.7783	0.8634	0.9830	0.9909	0.7359	0.9826	0.5741
0.0122	45.88	780	0.0585	0.7788	0.8656	0.9829	0.9907	0.7404	0.9825	0.5750
0.0119	47.06	800	0.0587	0.7774	0.8639	0.9828	0.9907	0.7371	0.9824	0.5725
0.0086	48.24	820	0.0594	0.7777	0.8567	0.9832	0.9916	0.7218	0.9828	0.5726
0.0094	49.41	840	0.0597	0.7766	0.8627	0.9828	0.9907	0.7347	0.9823	0.5708
0.0107	50.59	860	0.0619	0.7773	0.8624	0.9829	0.9909	0.7338	0.9825	0.5722
0.0175	51.76	880	0.0605	0.7752	0.8588	0.9828	0.9910	0.7266	0.9824	0.5681
0.0139	52.94	900	0.0620	0.7786	0.8675	0.9828	0.9905	0.7446	0.9824	0.5748
0.02	54.12	920	0.0633	0.7764	0.8628	0.9827	0.9907	0.7348	0.9823	0.5704
0.0294	55.29	940	0.0637	0.7762	0.8584	0.9829	0.9912	0.7256	0.9825	0.5699
0.0175	56.47	960	0.0639	0.7789	0.8717	0.9827	0.9900	0.7534	0.9822	0.5755
0.008	57.65	980	0.0640	0.7797	0.8667	0.9830	0.9907	0.7428	0.9826	0.5768
0.0132	58.82	1000	0.0652	0.7754	0.8657	0.9825	0.9902	0.7412	0.9820	0.5687
0.0339	60.0	1020	0.0640	0.7785	0.8664	0.9828	0.9906	0.7421	0.9824	0.5746
0.0144	61.18	1040	0.0618	0.7804	0.8577	0.9835	0.9919	0.7236	0.9831	0.5778
0.0206	62.35	1060	0.0653	0.7767	0.8636	0.9827	0.9907	0.7366	0.9823	0.5710
0.0165	63.53	1080	0.0651	0.7774	0.8602	0.9830	0.9912	0.7293	0.9826	0.5722
0.0175	64.71	1100	0.0648	0.7758	0.8568	0.9829	0.9913	0.7222	0.9825	0.5690
0.0104	65.88	1120	0.0669	0.7771	0.8618	0.9829	0.9909	0.7327	0.9825	0.5717
0.0191	67.06	1140	0.0662	0.7779	0.8696	0.9826	0.9901	0.7490	0.9822	0.5737
0.0123	68.24	1160	0.0668	0.7775	0.8591	0.9830	0.9913	0.7270	0.9826	0.5723
0.0127	69.41	1180	0.0676	0.7772	0.8637	0.9828	0.9907	0.7366	0.9824	0.5720
0.0092	70.59	1200	0.0673	0.7778	0.8699	0.9826	0.9901	0.7496	0.9822	0.5735
0.0101	71.76	1220	0.0680	0.7761	0.8694	0.9824	0.9899	0.7489	0.9820	0.5703
0.0204	72.94	1240	0.0676	0.7772	0.8640	0.9828	0.9907	0.7373	0.9824	0.5721
0.008	74.12	1260	0.0685	0.7768	0.8661	0.9826	0.9904	0.7417	0.9822	0.5714
0.0124	75.29	1280	0.0676	0.7776	0.8648	0.9828	0.9907	0.7390	0.9824	0.5729
0.0134	76.47	1300	0.0689	0.7770	0.8672	0.9826	0.9903	0.7441	0.9822	0.5718
0.0082	77.65	1320	0.0688	0.7755	0.8621	0.9826	0.9907	0.7336	0.9822	0.5687
0.0125	78.82	1340	0.0698	0.7761	0.8655	0.9826	0.9904	0.7407	0.9822	0.5701
0.0064	80.0	1360	0.0707	0.7760	0.8611	0.9827	0.9908	0.7314	0.9823	0.5696
0.0131	81.18	1380	0.0709	0.7765	0.8645	0.9827	0.9905	0.7384	0.9822	0.5707
0.0099	82.35	1400	0.0696	0.7769	0.8665	0.9826	0.9904	0.7427	0.9822	0.5717
0.0174	83.53	1420	0.0704	0.7757	0.8612	0.9827	0.9908	0.7317	0.9823	0.5692
0.0122	84.71	1440	0.0710	0.7754	0.8595	0.9827	0.9910	0.7281	0.9823	0.5685
0.0136	85.88	1460	0.0705	0.7765	0.8632	0.9827	0.9907	0.7356	0.9823	0.5706
0.0115	87.06	1480	0.0711	0.7763	0.8638	0.9827	0.9906	0.7370	0.9823	0.5703
0.0074	88.24	1500	0.0709	0.7763	0.8606	0.9828	0.9910	0.7302	0.9824	0.5702
0.0073	89.41	1520	0.0707	0.7767	0.8595	0.9829	0.9911	0.7278	0.9825	0.5708
0.0073	90.59	1540	0.0713	0.7756	0.8556	0.9830	0.9914	0.7197	0.9826	0.5687
0.0123	91.76	1560	0.0716	0.7763	0.8623	0.9827	0.9907	0.7339	0.9823	0.5702
0.0161	92.94	1580	0.0712	0.7762	0.8601	0.9828	0.9910	0.7293	0.9824	0.5700
0.0061	94.12	1600	0.0718	0.7754	0.8597	0.9827	0.9909	0.7284	0.9823	0.5685
0.0063	95.29	1620	0.0725	0.7751	0.8598	0.9827	0.9909	0.7288	0.9823	0.5679
0.0124	96.47	1640	0.0722	0.7750	0.8633	0.9825	0.9905	0.7361	0.9821	0.5680
0.0123	97.65	1660	0.0722	0.7751	0.8598	0.9827	0.9909	0.7287	0.9823	0.5680
0.0185	98.82	1680	0.0717	0.7753	0.8593	0.9827	0.9910	0.7277	0.9823	0.5683
0.0152	100.0	1700	0.0724	0.7754	0.8589	0.9828	0.9910	0.7269	0.9824	0.5684

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3