segformer-b5-finetuned-ade20k-morphpadver1-hgo-coord_40epochs_distortion_ver2_global_norm

This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the NICOPOI-9/Morphpad_HGO_1600_coord_global_norm dataset. It achieves the following results on the evaluation set:

• Loss: 0.1104
• Mean Iou: 0.9755
• Mean Accuracy: 0.9877
• Overall Accuracy: 0.9875
• Accuracy 0-0: 0.9892
• Accuracy 0-90: 0.9855
• Accuracy 90-0: 0.9863
• Accuracy 90-90: 0.9897
• Iou 0-0: 0.9778
• Iou 0-90: 0.9741
• Iou 90-0: 0.9734
• Iou 90-90: 0.9766

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: 40

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
1.3668	1.3680	4000	1.2660	0.2258	0.3802	0.3984	0.1901	0.4693	0.6808	0.1805	0.1537	0.2979	0.3155	0.1360
0.8028	2.7360	8000	1.0729	0.3341	0.5062	0.5266	0.2943	0.7462	0.6891	0.2953	0.2476	0.4225	0.4118	0.2544
0.9605	4.1040	12000	0.7966	0.5070	0.6678	0.6795	0.5623	0.7823	0.8018	0.5249	0.4831	0.5564	0.5338	0.4548
0.59	5.4720	16000	0.5049	0.6944	0.8173	0.8197	0.7840	0.8640	0.8169	0.8043	0.6977	0.6983	0.6912	0.6906
0.4359	6.8399	20000	0.3698	0.7779	0.8755	0.8748	0.8698	0.8828	0.8515	0.8977	0.7808	0.7776	0.7678	0.7855
0.3519	8.2079	24000	0.3545	0.7831	0.8734	0.8785	0.8256	0.9371	0.9178	0.8129	0.7893	0.7908	0.7801	0.7721
0.2117	9.5759	28000	0.2449	0.8532	0.9180	0.9205	0.8938	0.9490	0.9396	0.8894	0.8625	0.8530	0.8454	0.8520
0.4507	10.9439	32000	0.2180	0.8723	0.9293	0.9315	0.9214	0.9632	0.9430	0.8894	0.8865	0.8644	0.8736	0.8645
0.1921	12.3119	36000	0.1766	0.9018	0.9475	0.9480	0.9375	0.9644	0.9383	0.9499	0.9086	0.8969	0.8936	0.9081
0.6741	13.6799	40000	0.1713	0.9009	0.9467	0.9478	0.9612	0.9654	0.9563	0.9037	0.9183	0.8975	0.9027	0.8852
0.0979	15.0479	44000	0.1528	0.9257	0.9610	0.9612	0.9614	0.9568	0.9697	0.9560	0.9335	0.9255	0.9165	0.9273
0.2138	16.4159	48000	0.1637	0.9177	0.9561	0.9568	0.9494	0.9608	0.9651	0.9492	0.9240	0.9101	0.9153	0.9214
0.1426	17.7839	52000	0.1263	0.9454	0.9716	0.9718	0.9735	0.9734	0.9737	0.9659	0.9502	0.9438	0.9416	0.9460
0.1079	19.1518	56000	0.1401	0.9299	0.9625	0.9635	0.9630	0.9705	0.9793	0.9370	0.9403	0.9339	0.9225	0.9227
0.0968	20.5198	60000	0.1735	0.9231	0.9592	0.9600	0.9447	0.9648	0.9674	0.9600	0.9202	0.9239	0.9185	0.9300
0.1719	21.8878	64000	0.1326	0.9459	0.9718	0.9720	0.9579	0.9696	0.9751	0.9848	0.9464	0.9406	0.9434	0.9530
0.0587	23.2558	68000	0.1135	0.9585	0.9791	0.9786	0.9850	0.9710	0.9773	0.9831	0.9635	0.9542	0.9558	0.9603
0.2671	24.6238	72000	0.1184	0.9548	0.9761	0.9768	0.9655	0.9823	0.9827	0.9742	0.9544	0.9576	0.9485	0.9587
0.0418	25.9918	76000	0.1169	0.9605	0.9797	0.9797	0.9818	0.9765	0.9836	0.9768	0.9668	0.9574	0.9570	0.9608
0.0587	27.3598	80000	0.1084	0.9590	0.9786	0.9790	0.9714	0.9810	0.9843	0.9776	0.9600	0.9586	0.9553	0.9623
0.0067	28.7278	84000	0.1190	0.9641	0.9816	0.9815	0.9826	0.9775	0.9848	0.9815	0.9703	0.9629	0.9590	0.9641
0.0369	30.0958	88000	0.1230	0.9644	0.9818	0.9818	0.9804	0.9822	0.9811	0.9834	0.9667	0.9613	0.9627	0.9670
0.0814	31.4637	92000	0.1184	0.9664	0.9828	0.9828	0.9838	0.9827	0.9853	0.9792	0.9688	0.9674	0.9633	0.9661
0.1671	32.8317	96000	0.1193	0.9676	0.9835	0.9834	0.9839	0.9820	0.9837	0.9845	0.9718	0.9652	0.9661	0.9674
0.1289	34.1997	100000	0.1201	0.9620	0.9801	0.9806	0.9705	0.9845	0.9842	0.9811	0.9609	0.9588	0.9624	0.9657
0.0551	35.5677	104000	0.1159	0.9707	0.9851	0.9850	0.9863	0.9831	0.9854	0.9856	0.9741	0.9680	0.9689	0.9717
0.0217	36.9357	108000	0.1141	0.9732	0.9865	0.9863	0.9886	0.9826	0.9868	0.9879	0.9767	0.9705	0.9722	0.9733
0.0031	38.3037	112000	0.1177	0.9735	0.9866	0.9865	0.9891	0.9843	0.9879	0.9849	0.9770	0.9724	0.9714	0.9733
0.0435	39.6717	116000	0.1104	0.9755	0.9877	0.9875	0.9892	0.9855	0.9863	0.9897	0.9778	0.9741	0.9734	0.9766

Framework versions

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