segformer-b0-finetuned-morphpadver1-hgo-coord

This model is a fine-tuned version of nvidia/mit-b0 on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset. It achieves the following results on the evaluation set:

• Loss: 0.0306
• Mean Iou: 0.9858
• Mean Accuracy: 0.9928
• Overall Accuracy: 0.9928
• Accuracy 0-0: 0.9933
• Accuracy 0-90: 0.9937
• Accuracy 90-0: 0.9943
• Accuracy 90-90: 0.9898
• Iou 0-0: 0.9885
• Iou 0-90: 0.9850
• Iou 90-0: 0.9826
• Iou 90-90: 0.9872

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
1.2185	2.5445	4000	1.2349	0.2290	0.3745	0.3762	0.2785	0.4062	0.4936	0.3198	0.2085	0.2334	0.2525	0.2216
1.0978	5.0891	8000	1.1020	0.2905	0.4487	0.4508	0.3780	0.5302	0.5341	0.3524	0.2937	0.2870	0.2991	0.2822
0.9886	7.6336	12000	1.0139	0.3231	0.4871	0.4896	0.4154	0.4500	0.7245	0.3585	0.3291	0.3272	0.3266	0.3096
0.9358	10.1781	16000	0.9575	0.3517	0.5195	0.5215	0.3765	0.6411	0.5865	0.4740	0.3438	0.3539	0.3617	0.3473
0.8735	12.7226	20000	0.8853	0.4007	0.5704	0.5726	0.4998	0.5637	0.7536	0.4647	0.4109	0.3953	0.4055	0.3913
0.7186	15.2672	24000	0.6833	0.5558	0.7151	0.7141	0.7389	0.6650	0.6919	0.7647	0.5919	0.5261	0.5453	0.5598
0.6514	17.8117	28000	0.4379	0.7017	0.8243	0.8243	0.8344	0.8161	0.8279	0.8187	0.7198	0.6807	0.6933	0.7130
0.603	20.3562	32000	0.2900	0.7980	0.8879	0.8874	0.9117	0.8490	0.8888	0.9020	0.8160	0.7726	0.7893	0.8142
0.2448	22.9008	36000	0.2154	0.8496	0.9184	0.9185	0.9330	0.9179	0.9170	0.9058	0.8683	0.8329	0.8445	0.8527
0.2766	25.4453	40000	0.2004	0.8612	0.9254	0.9254	0.9487	0.9059	0.9381	0.9088	0.8717	0.8469	0.8635	0.8628
0.6278	27.9898	44000	0.1410	0.8976	0.9459	0.9459	0.9426	0.9377	0.9559	0.9474	0.9075	0.8863	0.8932	0.9034
0.1684	30.5344	48000	0.1163	0.9137	0.9549	0.9548	0.9595	0.9417	0.9579	0.9605	0.9245	0.9046	0.9069	0.9187
0.0638	33.0789	52000	0.0927	0.9338	0.9657	0.9657	0.9697	0.9589	0.9715	0.9627	0.9406	0.9291	0.9291	0.9363
0.0749	35.6234	56000	0.0836	0.9382	0.9680	0.9680	0.9714	0.9663	0.9680	0.9664	0.9449	0.9325	0.9339	0.9414
0.045	38.1679	60000	0.0624	0.9545	0.9767	0.9767	0.9787	0.9751	0.9763	0.9766	0.9587	0.9521	0.9499	0.9573
0.1278	40.7125	64000	0.0635	0.9546	0.9767	0.9767	0.9773	0.9743	0.9813	0.9737	0.9598	0.9521	0.9492	0.9572
0.0443	43.2570	68000	0.0598	0.9584	0.9787	0.9787	0.9815	0.9723	0.9858	0.9752	0.9624	0.9548	0.9548	0.9617
0.0337	45.8015	72000	0.0549	0.9622	0.9807	0.9807	0.9877	0.9804	0.9820	0.9726	0.9648	0.9587	0.9622	0.9632
0.0434	48.3461	76000	0.0539	0.9643	0.9816	0.9817	0.9793	0.9779	0.9913	0.9781	0.9691	0.9611	0.9565	0.9703
0.1576	50.8906	80000	0.0577	0.9656	0.9825	0.9825	0.9799	0.9822	0.9825	0.9856	0.9694	0.9634	0.9653	0.9645
0.025	53.4351	84000	0.0453	0.9724	0.9860	0.9860	0.9856	0.9884	0.9840	0.9858	0.9762	0.9698	0.9697	0.9739
0.0318	55.9796	88000	0.0401	0.9733	0.9865	0.9865	0.9884	0.9845	0.9865	0.9865	0.9766	0.9700	0.9714	0.9753
0.1355	58.5242	92000	0.0453	0.9764	0.9880	0.9880	0.9896	0.9874	0.9889	0.9861	0.9796	0.9742	0.9731	0.9786
0.0256	61.0687	96000	0.0359	0.9817	0.9907	0.9908	0.9902	0.9925	0.9902	0.9901	0.9846	0.9808	0.9783	0.9833
0.019	63.6132	100000	0.0320	0.9819	0.9908	0.9909	0.9914	0.9908	0.9936	0.9875	0.9838	0.9812	0.9787	0.9841
0.0713	66.1578	104000	0.0319	0.9827	0.9912	0.9912	0.9940	0.9922	0.9937	0.9847	0.9859	0.9812	0.9807	0.9828
0.1036	68.7023	108000	0.0369	0.9807	0.9902	0.9903	0.9932	0.9916	0.9946	0.9813	0.9844	0.9807	0.9790	0.9788
0.0575	71.2468	112000	0.0338	0.9843	0.9921	0.9921	0.9939	0.9913	0.9929	0.9901	0.9870	0.9822	0.9814	0.9867
0.0136	73.7913	116000	0.0259	0.9870	0.9934	0.9934	0.9926	0.9936	0.9946	0.9930	0.9889	0.9852	0.9850	0.9891
0.045	76.3359	120000	0.0310	0.9844	0.9921	0.9921	0.9913	0.9926	0.9941	0.9902	0.9866	0.9834	0.9805	0.9871
0.6665	78.8804	124000	0.0306	0.9858	0.9928	0.9928	0.9933	0.9937	0.9943	0.9898	0.9885	0.9850	0.9826	0.9872

Framework versions

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