segformer-b0-finetuned-morphpadver1-hgo-coord-v3

This model is a fine-tuned version of nvidia/mit-b3 on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset. It achieves the following results on the evaluation set:

• Loss: 0.0298
• Mean Iou: 0.9952
• Mean Accuracy: 0.9976
• Overall Accuracy: 0.9976
• Accuracy 0-0: 0.9983
• Accuracy 0-90: 0.9969
• Accuracy 90-0: 0.9980
• Accuracy 90-90: 0.9971
• Iou 0-0: 0.9959
• Iou 0-90: 0.9950
• Iou 90-0: 0.9945
• Iou 90-90: 0.9954

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
0.9708	2.6525	4000	0.9827	0.3521	0.5133	0.5141	0.6031	0.4019	0.6068	0.4413	0.4004	0.3106	0.2982	0.3990
1.8598	5.3050	8000	0.6389	0.5316	0.6914	0.6913	0.6875	0.7105	0.5770	0.7908	0.6004	0.4655	0.5143	0.5463
0.4633	7.9576	12000	0.5041	0.6369	0.7767	0.7768	0.7883	0.7582	0.7869	0.7735	0.6734	0.6126	0.5897	0.6720
0.5576	10.6101	16000	0.4221	0.6949	0.8191	0.8194	0.8477	0.7814	0.8269	0.8206	0.7084	0.7075	0.6533	0.7106
0.3003	13.2626	20000	0.3963	0.7293	0.8413	0.8414	0.8524	0.8809	0.8047	0.8273	0.7565	0.6617	0.7326	0.7666
0.3174	15.9151	24000	0.4310	0.7511	0.8573	0.8572	0.8538	0.8774	0.8402	0.8577	0.7779	0.7125	0.7412	0.7729
0.2307	18.5676	28000	0.3326	0.8024	0.8901	0.8902	0.9029	0.8852	0.8832	0.8893	0.8196	0.7874	0.7805	0.8221
0.1946	21.2202	32000	0.2625	0.8409	0.9134	0.9134	0.9136	0.9197	0.8974	0.9230	0.8533	0.8114	0.8410	0.8581
0.1325	23.8727	36000	0.1298	0.9185	0.9575	0.9576	0.9647	0.9488	0.9691	0.9474	0.9319	0.9114	0.9158	0.9150
0.1007	26.5252	40000	0.0752	0.9596	0.9794	0.9794	0.9824	0.9811	0.9748	0.9792	0.9671	0.9557	0.9532	0.9624
0.0336	29.1777	44000	0.2640	0.9435	0.9709	0.9709	0.9750	0.9668	0.9713	0.9706	0.9473	0.9389	0.9382	0.9497
0.0182	31.8302	48000	0.1066	0.9680	0.9837	0.9837	0.9882	0.9801	0.9845	0.9821	0.9712	0.9677	0.9627	0.9703
0.0141	34.4828	52000	0.0716	0.9806	0.9902	0.9902	0.9880	0.9898	0.9893	0.9937	0.9822	0.9789	0.9792	0.9821
0.0117	37.1353	56000	0.0705	0.9850	0.9925	0.9925	0.9914	0.9920	0.9929	0.9935	0.9861	0.9838	0.9842	0.9859
0.0129	39.7878	60000	0.0932	0.9833	0.9916	0.9916	0.9897	0.9930	0.9920	0.9916	0.9834	0.9823	0.9827	0.9846
0.0101	42.4403	64000	0.0302	0.9924	0.9962	0.9962	0.9964	0.9966	0.9972	0.9946	0.9938	0.9917	0.9921	0.9921
0.0071	45.0928	68000	0.0294	0.9933	0.9966	0.9966	0.9971	0.9961	0.9958	0.9976	0.9946	0.9931	0.9925	0.9930
0.0066	47.7454	72000	0.0637	0.9912	0.9956	0.9956	0.9959	0.9957	0.9962	0.9946	0.9927	0.9917	0.9895	0.9910
0.0083	50.3979	76000	0.0494	0.9901	0.9950	0.9950	0.9956	0.9951	0.9946	0.9948	0.9901	0.9899	0.9887	0.9917
0.0049	53.0504	80000	0.0286	0.9944	0.9972	0.9972	0.9982	0.9967	0.9976	0.9963	0.9956	0.9935	0.9939	0.9946
0.0104	55.7029	84000	0.0244	0.9953	0.9976	0.9976	0.9984	0.9971	0.9978	0.9971	0.9957	0.9953	0.9942	0.9959
0.0046	58.3554	88000	0.0298	0.9952	0.9976	0.9976	0.9983	0.9969	0.9980	0.9971	0.9959	0.9950	0.9945	0.9954

Framework versions

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