segformer-b0-finetuned-arabidopsis-roots-multi

This model is a fine-tuned version of nvidia/mit-b0 on the jacquelinegrimm/arabidopsis-roots-multi dataset. It achieves the following results on the evaluation set:

• Loss: 0.0675
• Mean Iou: 0.5272
• Mean Accuracy: 0.7048
• Overall Accuracy: 0.6874
• Accuracy Background: nan
• Accuracy Main root: 0.6670
• Accuracy Lateral root: 0.6181
• Accuracy Shoot: 0.7576
• Accuracy Botrytis: 0.7765
• Iou Background: 0.0
• Iou Main root: 0.6256
• Iou Lateral root: 0.5603
• Iou Shoot: 0.6767
• Iou Botrytis: 0.7734

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Main root	Accuracy Lateral root	Accuracy Shoot	Accuracy Botrytis	Iou Background	Iou Main root	Iou Lateral root	Iou Shoot	Iou Botrytis
1.1832	1.0	20	1.2347	0.3383	0.4952	0.4799	nan	0.1614	0.7764	0.1747	0.8683	0.0	0.1377	0.5355	0.1700	0.8484
0.901	2.0	40	0.7864	0.3051	0.4105	0.3438	nan	0.0062	0.3933	0.3166	0.9258	0.0	0.0061	0.3238	0.2853	0.9101
0.7161	3.0	60	0.6386	0.3059	0.4133	0.3430	nan	0.0271	0.3389	0.3349	0.9523	0.0	0.0270	0.2919	0.2847	0.9261
0.6358	4.0	80	0.6021	0.3155	0.4225	0.3502	nan	0.0390	0.3338	0.3713	0.9458	0.0	0.0387	0.2878	0.3241	0.9271
0.5111	5.0	100	0.4770	0.2871	0.3779	0.3175	nan	0.0567	0.2880	0.2798	0.8871	0.0	0.0562	0.2493	0.2571	0.8728
0.4842	6.0	120	0.4069	0.2422	0.3232	0.2792	nan	0.1142	0.2559	0.3734	0.5495	0.0	0.1112	0.2290	0.3240	0.5470
0.3631	7.0	140	0.3527	0.2692	0.3581	0.3537	nan	0.3382	0.3411	0.3353	0.4179	0.0	0.3168	0.3021	0.3093	0.4179
0.3316	8.0	160	0.2986	0.4239	0.5731	0.5496	nan	0.4712	0.5003	0.5306	0.7904	0.0	0.4372	0.4333	0.4719	0.7773
0.3074	9.0	180	0.2755	0.3755	0.5061	0.4932	nan	0.4440	0.4876	0.5202	0.5727	0.0	0.4129	0.4281	0.4679	0.5687
0.3216	10.0	200	0.2317	0.4262	0.5762	0.5625	nan	0.5676	0.4656	0.5853	0.6864	0.0	0.5153	0.4206	0.5142	0.6811
0.2426	11.0	220	0.2022	0.4659	0.6319	0.5981	nan	0.5068	0.5241	0.6682	0.8285	0.0	0.4766	0.4531	0.5856	0.8139
0.2063	12.0	240	0.1752	0.4274	0.5759	0.5627	nan	0.5756	0.4687	0.6275	0.6319	0.0	0.5259	0.4287	0.5547	0.6277
0.1811	13.0	260	0.1542	0.4608	0.6216	0.6022	nan	0.5509	0.5639	0.6604	0.7113	0.0	0.5166	0.4893	0.5899	0.7083
0.1738	14.0	280	0.1396	0.4393	0.5926	0.5781	nan	0.6143	0.4492	0.6755	0.6314	0.0	0.5544	0.4218	0.5914	0.6291
0.2094	15.0	300	0.1193	0.4422	0.5906	0.5683	nan	0.5321	0.4922	0.6475	0.6907	0.0	0.4953	0.4505	0.5760	0.6891
0.129	16.0	320	0.1200	0.4816	0.6520	0.6415	nan	0.6646	0.5644	0.7487	0.6303	0.0	0.6072	0.5158	0.6552	0.6298
0.1326	17.0	340	0.1109	0.4622	0.6200	0.6005	nan	0.5568	0.5628	0.7030	0.6576	0.0	0.5257	0.5029	0.6270	0.6555
0.0935	18.0	360	0.1043	0.5018	0.6743	0.6542	nan	0.5992	0.6207	0.7253	0.7519	0.0	0.5665	0.5412	0.6523	0.7490
0.0862	19.0	380	0.1044	0.4790	0.6435	0.6272	nan	0.5848	0.6080	0.7212	0.6600	0.0	0.5538	0.5333	0.6492	0.6586
0.122	20.0	400	0.0940	0.5244	0.7048	0.6846	nan	0.6428	0.6277	0.7498	0.7987	0.0	0.6000	0.5576	0.6708	0.7939
0.0886	21.0	420	0.0945	0.5390	0.7263	0.7016	nan	0.6253	0.6676	0.7686	0.8439	0.0	0.5912	0.5754	0.6902	0.8382
0.0742	22.0	440	0.0933	0.4919	0.6620	0.6407	nan	0.6001	0.5945	0.7732	0.6803	0.0	0.5677	0.5301	0.6829	0.6788
0.0727	23.0	460	0.0886	0.4711	0.6279	0.6139	nan	0.6072	0.5489	0.6892	0.6664	0.0	0.5695	0.5005	0.6199	0.6656
0.0708	24.0	480	0.0854	0.4966	0.6684	0.6574	nan	0.6409	0.6351	0.7520	0.6454	0.0	0.6040	0.5612	0.6737	0.6443
0.0614	25.0	500	0.0857	0.4882	0.6563	0.6405	nan	0.6005	0.6244	0.7464	0.6541	0.0	0.5691	0.5524	0.6658	0.6535
0.151	26.0	520	0.0834	0.5106	0.6852	0.6667	nan	0.6271	0.6253	0.7566	0.7320	0.0	0.5918	0.5566	0.6762	0.7284
0.0658	27.0	540	0.0823	0.5055	0.6768	0.6636	nan	0.6741	0.5807	0.7498	0.7027	0.0	0.6250	0.5332	0.6673	0.7017
0.0643	28.0	560	0.0797	0.4959	0.6636	0.6481	nan	0.6315	0.5930	0.7399	0.6901	0.0	0.5921	0.5366	0.6619	0.6889
0.059	29.0	580	0.0782	0.5115	0.6845	0.6682	nan	0.6490	0.6092	0.7531	0.7265	0.0	0.6100	0.5472	0.6769	0.7234
0.1726	30.0	600	0.0786	0.5235	0.7039	0.6846	nan	0.6190	0.6734	0.7552	0.7679	0.0	0.5905	0.5825	0.6807	0.7639
0.0546	31.0	620	0.0745	0.5116	0.6843	0.6654	nan	0.6337	0.6072	0.7468	0.7494	0.0	0.5956	0.5467	0.6701	0.7457
0.1096	32.0	640	0.0746	0.5093	0.6814	0.6590	nan	0.5992	0.6202	0.7427	0.7634	0.0	0.5666	0.5550	0.6656	0.7595
0.0552	33.0	660	0.0750	0.5358	0.7160	0.6944	nan	0.6520	0.6285	0.7572	0.8262	0.0	0.6147	0.5639	0.6783	0.8223
0.0557	34.0	680	0.0731	0.5123	0.6878	0.6709	nan	0.6271	0.6496	0.7669	0.7076	0.0	0.5955	0.5745	0.6853	0.7060
0.0516	35.0	700	0.0733	0.5307	0.7108	0.6929	nan	0.6696	0.6260	0.7665	0.7813	0.0	0.6276	0.5654	0.6825	0.7781
0.105	36.0	720	0.0717	0.5242	0.7020	0.6823	nan	0.6365	0.6397	0.7633	0.7684	0.0	0.6029	0.5682	0.6841	0.7660
0.1305	37.0	740	0.0713	0.5232	0.7002	0.6845	nan	0.6739	0.6137	0.7624	0.7510	0.0	0.6289	0.5585	0.6794	0.7490
0.0479	38.0	760	0.0707	0.5196	0.6944	0.6763	nan	0.6517	0.6116	0.7556	0.7588	0.0	0.6125	0.5532	0.6754	0.7567
0.0552	39.0	780	0.0705	0.5198	0.6977	0.6837	nan	0.6561	0.6543	0.7664	0.7139	0.0	0.6209	0.5803	0.6852	0.7124
0.0997	40.0	800	0.0699	0.5219	0.6968	0.6789	nan	0.6586	0.6057	0.7478	0.7752	0.0	0.6180	0.5504	0.6680	0.7729
0.0774	41.0	820	0.0708	0.5171	0.6900	0.6727	nan	0.6592	0.5927	0.7395	0.7686	0.0	0.6163	0.5402	0.6635	0.7656
0.0899	42.0	840	0.0692	0.5324	0.7125	0.6927	nan	0.6683	0.6169	0.7765	0.7883	0.0	0.6265	0.5601	0.6902	0.7851
0.0492	43.0	860	0.0682	0.5390	0.7216	0.7043	nan	0.6740	0.6497	0.7676	0.7950	0.0	0.6348	0.5793	0.6891	0.7918
0.0712	44.0	880	0.0690	0.5121	0.6844	0.6692	nan	0.6570	0.6071	0.7533	0.7204	0.0	0.6153	0.5524	0.6743	0.7186
0.1034	45.0	900	0.0685	0.5503	0.7379	0.7191	nan	0.6822	0.6645	0.7832	0.8215	0.0	0.6439	0.5905	0.6994	0.8175
0.0478	46.0	920	0.0681	0.5365	0.7179	0.6998	nan	0.6726	0.6369	0.7719	0.7902	0.0	0.6326	0.5728	0.6901	0.7869
0.0452	47.0	940	0.0682	0.5341	0.7157	0.6993	nan	0.6723	0.6495	0.7765	0.7647	0.0	0.6346	0.5803	0.6935	0.7621
0.0542	48.0	960	0.0675	0.5382	0.7206	0.7021	nan	0.6695	0.6444	0.7726	0.7961	0.0	0.6313	0.5772	0.6899	0.7928
0.0738	49.0	980	0.0680	0.5360	0.7168	0.6977	nan	0.6714	0.6254	0.7666	0.8040	0.0	0.6302	0.5658	0.6838	0.8004
0.1169	50.0	1000	0.0675	0.5272	0.7048	0.6874	nan	0.6670	0.6181	0.7576	0.7765	0.0	0.6256	0.5603	0.6767	0.7734

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2