segformer-b0-finetuned-morphpadver1-hgo-coord-v2

This model is a fine-tuned version of NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1 on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset. It achieves the following results on the evaluation set:

• Loss: 0.0408
• Mean Iou: 0.9952
• Mean Accuracy: 0.9976
• Overall Accuracy: 0.9976
• Accuracy 0-0: 0.9993
• Accuracy 0-90: 0.9958
• Accuracy 90-0: 0.9969
• Accuracy 90-90: 0.9983
• Iou 0-0: 0.9975
• Iou 0-90: 0.9929
• Iou 90-0: 0.9949
• Iou 90-90: 0.9955

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: 60

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
0.0654	2.5445	4000	0.1134	0.9236	0.9604	0.9602	0.9729	0.9373	0.9642	0.9674	0.9265	0.9144	0.9233	0.9301
0.0552	5.0891	8000	0.1426	0.9161	0.9562	0.9561	0.9607	0.9538	0.9547	0.9555	0.9166	0.9146	0.9112	0.9218
0.0469	7.6336	12000	0.0633	0.9556	0.9774	0.9773	0.9811	0.9714	0.9744	0.9826	0.9588	0.9516	0.9545	0.9576
0.0378	10.1781	16000	0.0506	0.9650	0.9822	0.9822	0.9826	0.9773	0.9844	0.9844	0.9661	0.9601	0.9643	0.9696
0.0582	12.7226	20000	0.0402	0.9737	0.9867	0.9866	0.9925	0.9891	0.9791	0.9860	0.9774	0.9700	0.9699	0.9774
0.0322	15.2672	24000	0.0453	0.9707	0.9850	0.9851	0.9809	0.9843	0.9909	0.9840	0.9746	0.9715	0.9637	0.9728
0.0254	17.8117	28000	0.1030	0.9652	0.9823	0.9822	0.9895	0.9808	0.9748	0.9841	0.9761	0.9599	0.9583	0.9666
2.3028	20.3562	32000	0.0572	0.9745	0.9871	0.9870	0.9861	0.9839	0.9885	0.9896	0.9789	0.9717	0.9700	0.9773
0.0769	22.9008	36000	0.0225	0.9866	0.9932	0.9932	0.9960	0.9899	0.9939	0.9932	0.9893	0.9837	0.9849	0.9884
0.0512	25.4453	40000	0.0329	0.9850	0.9924	0.9924	0.9959	0.9867	0.9954	0.9917	0.9857	0.9820	0.9843	0.9878
0.3281	27.9898	44000	0.0301	0.9866	0.9933	0.9932	0.9958	0.9913	0.9907	0.9952	0.9899	0.9858	0.9843	0.9863
0.1536	30.5344	48000	0.0355	0.9889	0.9944	0.9944	0.9981	0.9927	0.9920	0.9949	0.9941	0.9855	0.9880	0.9880
0.0079	33.0789	52000	0.0256	0.9933	0.9966	0.9966	0.9979	0.9951	0.9961	0.9974	0.9956	0.9917	0.9934	0.9924
0.0074	35.6234	56000	0.0205	0.9938	0.9969	0.9969	0.9983	0.9970	0.9966	0.9956	0.9963	0.9923	0.9928	0.9939
0.0077	38.1679	60000	0.0255	0.9933	0.9967	0.9966	0.9985	0.9946	0.9964	0.9971	0.9954	0.9925	0.9919	0.9934
0.0061	40.7125	64000	0.0282	0.9945	0.9972	0.9972	0.9987	0.9958	0.9974	0.9969	0.9967	0.9916	0.9950	0.9945
0.0051	43.2570	68000	0.0262	0.9937	0.9969	0.9968	0.9987	0.9949	0.9959	0.9979	0.9968	0.9916	0.9934	0.9930
0.0047	45.8015	72000	0.0564	0.9912	0.9956	0.9956	0.9991	0.9950	0.9940	0.9943	0.9958	0.9882	0.9897	0.9912
0.0046	48.3461	76000	0.0492	0.9939	0.9969	0.9969	0.9992	0.9941	0.9974	0.9970	0.9969	0.9903	0.9938	0.9945
0.0552	50.8906	80000	0.0438	0.9948	0.9974	0.9974	0.9992	0.9966	0.9967	0.9972	0.9980	0.9924	0.9948	0.9941
0.0039	53.4351	84000	0.0361	0.9953	0.9976	0.9976	0.9991	0.9961	0.9973	0.9981	0.9975	0.9928	0.9952	0.9956
0.0034	55.9796	88000	0.0317	0.9958	0.9979	0.9979	0.9993	0.9964	0.9974	0.9985	0.9979	0.9937	0.9955	0.9963
0.0149	58.5242	92000	0.0408	0.9952	0.9976	0.9976	0.9993	0.9958	0.9969	0.9983	0.9975	0.9929	0.9949	0.9955

Framework versions

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