segformer-b0-finetuned-morphpadver1-hgo-3

This model is a fine-tuned version of nvidia/mit-b0 on the NICOPOI-9/morphpad_hgo_512_4class dataset. It achieves the following results on the evaluation set:

• Loss: 0.0870
• Mean Iou: 0.9780
• Mean Accuracy: 0.9889
• Overall Accuracy: 0.9888
• Accuracy 0-0: 0.9913
• Accuracy 0-90: 0.9886
• Accuracy 90-0: 0.9892
• Accuracy 90-90: 0.9863
• Iou 0-0: 0.9811
• Iou 0-90: 0.9785
• Iou 90-0: 0.9754
• Iou 90-90: 0.9771

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: 1
• eval_batch_size: 1
• seed: 42
• 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
• num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy 0-0	Accuracy 0-90	Accuracy 90-0	Accuracy 90-90	Iou 0-0	Iou 0-90	Iou 90-0	Iou 90-90
1.2394	2.5445	4000	1.2392	0.2273	0.3754	0.3738	0.4516	0.3985	0.2299	0.4215	0.2520	0.2205	0.1802	0.2565
1.0858	5.0891	8000	1.0986	0.2889	0.4462	0.4463	0.4510	0.3646	0.5196	0.4498	0.3208	0.2453	0.2792	0.3104
1.0059	7.6336	12000	1.0060	0.3295	0.4934	0.4935	0.4793	0.6104	0.4583	0.4256	0.3472	0.3209	0.3057	0.3442
0.9457	10.1781	16000	0.9497	0.3565	0.5236	0.5240	0.4976	0.6276	0.5356	0.4335	0.3777	0.3459	0.3490	0.3532
0.93	12.7226	20000	0.9072	0.3759	0.5397	0.5412	0.4649	0.6012	0.6437	0.4491	0.4073	0.3550	0.3549	0.3863
0.8403	15.2672	24000	0.8349	0.4263	0.5967	0.5959	0.6497	0.5299	0.6555	0.5517	0.4548	0.4083	0.3976	0.4443
0.8826	17.8117	28000	0.6734	0.5172	0.6784	0.6787	0.6590	0.7037	0.6902	0.6606	0.5517	0.4864	0.4837	0.5469
0.8028	20.3562	32000	0.4582	0.6843	0.8116	0.8120	0.7923	0.7953	0.8322	0.8268	0.6986	0.6686	0.6646	0.7056
0.8461	22.9008	36000	0.2845	0.8071	0.8931	0.8928	0.9089	0.8839	0.8825	0.8971	0.8322	0.7928	0.7850	0.8182
0.7034	25.4453	40000	0.3351	0.7724	0.8716	0.8713	0.8859	0.8680	0.8485	0.8839	0.7928	0.7649	0.7472	0.7849
1.0428	27.9898	44000	0.1882	0.8750	0.9334	0.9331	0.9467	0.9277	0.9323	0.9267	0.8912	0.8673	0.8647	0.8767
0.3497	30.5344	48000	0.1620	0.8982	0.9464	0.9461	0.9582	0.9539	0.9279	0.9455	0.9163	0.8878	0.8840	0.9046
0.0803	33.0789	52000	0.1314	0.9187	0.9577	0.9575	0.9711	0.9517	0.9508	0.9574	0.9300	0.9101	0.9121	0.9224
0.1394	35.6234	56000	0.1271	0.9228	0.9598	0.9597	0.9648	0.9612	0.9530	0.9603	0.9332	0.9163	0.9167	0.9250
0.0579	38.1679	60000	0.1170	0.9351	0.9665	0.9664	0.9723	0.9652	0.9619	0.9665	0.9446	0.9300	0.9282	0.9377
0.1097	40.7125	64000	0.1121	0.9402	0.9690	0.9691	0.9634	0.9748	0.9732	0.9644	0.9477	0.9380	0.9334	0.9416
0.0615	43.2570	68000	0.1069	0.9458	0.9720	0.9721	0.9707	0.9713	0.9777	0.9685	0.9523	0.9424	0.9426	0.9459
0.0425	45.8015	72000	0.0967	0.9540	0.9764	0.9764	0.9789	0.9740	0.9798	0.9729	0.9609	0.9506	0.9514	0.9529
0.0396	48.3461	76000	0.0991	0.9599	0.9795	0.9795	0.9827	0.9766	0.9783	0.9805	0.9656	0.9562	0.9560	0.9617
0.4319	50.8906	80000	0.0975	0.9583	0.9786	0.9787	0.9759	0.9826	0.9769	0.9792	0.9607	0.9566	0.9551	0.9608
0.0299	53.4351	84000	0.0959	0.9662	0.9828	0.9827	0.9858	0.9837	0.9807	0.9811	0.9712	0.9655	0.9637	0.9644
0.0282	55.9796	88000	0.0933	0.9687	0.9841	0.9841	0.9866	0.9830	0.9823	0.9846	0.9729	0.9665	0.9655	0.9698
0.1038	58.5242	92000	0.0864	0.9707	0.9852	0.9851	0.9882	0.9874	0.9842	0.9809	0.9727	0.9709	0.9698	0.9695
0.0246	61.0687	96000	0.0990	0.9722	0.9859	0.9859	0.9885	0.9873	0.9847	0.9832	0.9764	0.9716	0.9706	0.9703
0.0208	63.6132	100000	0.0839	0.9749	0.9873	0.9873	0.9901	0.9897	0.9850	0.9845	0.9781	0.9748	0.9726	0.9743
0.0749	66.1578	104000	0.0874	0.9761	0.9879	0.9879	0.9901	0.9888	0.9875	0.9851	0.9798	0.9757	0.9734	0.9753
0.0798	68.7023	108000	0.0866	0.9753	0.9875	0.9875	0.9908	0.9881	0.9883	0.9830	0.9792	0.9759	0.9722	0.9740
0.1236	71.2468	112000	0.0933	0.9775	0.9886	0.9886	0.9911	0.9895	0.9870	0.9868	0.9813	0.9773	0.9748	0.9765
0.0165	73.7913	116000	0.0894	0.9785	0.9891	0.9891	0.9902	0.9912	0.9867	0.9886	0.9818	0.9782	0.9759	0.9783
0.0204	76.3359	120000	0.0885	0.9792	0.9895	0.9895	0.9909	0.9913	0.9874	0.9884	0.9823	0.9791	0.9766	0.9788
0.7823	78.8804	124000	0.0870	0.9780	0.9889	0.9888	0.9913	0.9886	0.9892	0.9863	0.9811	0.9785	0.9754	0.9771

Framework versions

• Transformers 4.48.3
• Pytorch 2.1.0
• Datasets 3.2.0
• Tokenizers 0.21.0