learning_rate: 6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 1337
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: polynomial
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy 背景	Accuracy 未水化水泥颗粒	Accuracy 孔隙	Accuracy 氢氧化钙	Accuracy 其他水化物	Iou 背景	Iou 未水化水泥颗粒	Iou 孔隙	Iou 氢氧化钙	Iou 其他水化物
No log	1.0	50	0.2994	0.7375	0.9586	0.9580	nan	0.9557	0.9290	0.9696	0.9802	0.0	0.9224	0.9066	0.9195	0.9392
0.4501	2.0	100	0.1558	0.7645	0.9767	0.9766	nan	0.9802	0.9580	0.9758	0.9929	0.0	0.9585	0.9504	0.9524	0.9609
0.4501	3.0	150	0.1193	0.7715	0.9814	0.9812	nan	0.9797	0.9718	0.9829	0.9912	0.0	0.9661	0.9628	0.9607	0.9680
0.0949	4.0	200	0.0898	0.7745	0.9835	0.9834	nan	0.9844	0.9751	0.9842	0.9902	0.0	0.9702	0.9667	0.9655	0.9699
0.0949	5.0	250	0.0766	0.7762	0.9848	0.9848	nan	0.9848	0.9799	0.9842	0.9905	0.0	0.9729	0.9696	0.9674	0.9713
0.0584	6.0	300	0.0624	0.7771	0.9856	0.9855	nan	0.9865	0.9802	0.9852	0.9905	0.0	0.9747	0.9704	0.9684	0.9723
0.0584	7.0	350	0.0628	0.7777	0.9859	0.9858	nan	0.9845	0.9817	0.9865	0.9907	0.0	0.9743	0.9717	0.9695	0.9731
0.0441	8.0	400	0.0575	0.7784	0.9863	0.9863	nan	0.9852	0.9841	0.9846	0.9914	0.0	0.9750	0.9732	0.9709	0.9732
0.0441	9.0	450	0.0500	0.7788	0.9867	0.9866	nan	0.9855	0.9847	0.9839	0.9925	0.0	0.9762	0.9738	0.9706	0.9734
0.0363	10.0	500	0.0496	0.7795	0.9870	0.9869	nan	0.9841	0.9859	0.9875	0.9905	0.0	0.9753	0.9745	0.9726	0.9751
0.0363	11.0	550	0.0458	0.7798	0.9873	0.9872	nan	0.9844	0.9863	0.9875	0.9910	0.0	0.9758	0.9749	0.9731	0.9755
0.0315	12.0	600	0.0423	0.7802	0.9875	0.9875	nan	0.9872	0.9845	0.9895	0.9891	0.0	0.9771	0.9750	0.9731	0.9757
0.0315	13.0	650	0.0437	0.7800	0.9874	0.9873	nan	0.9851	0.9848	0.9891	0.9908	0.0	0.9762	0.9749	0.9731	0.9760
0.0278	14.0	700	0.0390	0.7805	0.9878	0.9877	nan	0.9862	0.9859	0.9874	0.9916	0.0	0.9772	0.9753	0.9738	0.9762
0.0278	15.0	750	0.0404	0.7799	0.9874	0.9873	nan	0.9834	0.9872	0.9896	0.9896	0.0	0.9753	0.9738	0.9740	0.9764
0.0255	16.0	800	0.0422	0.7789	0.9868	0.9866	nan	0.9809	0.9872	0.9878	0.9911	0.0	0.9725	0.9709	0.9745	0.9765
0.0255	17.0	850	0.0387	0.7794	0.9871	0.9869	nan	0.9831	0.9858	0.9900	0.9895	0.0	0.9742	0.9720	0.9739	0.9767
0.0235	18.0	900	0.0395	0.7791	0.9869	0.9867	nan	0.9810	0.9882	0.9881	0.9903	0.0	0.9725	0.9706	0.9751	0.9770
0.0235	19.0	950	0.0364	0.7790	0.9868	0.9866	nan	0.9809	0.9886	0.9867	0.9911	0.0	0.9723	0.9706	0.9752	0.9769
0.0221	20.0	1000	0.0394	0.7785	0.9865	0.9863	nan	0.9801	0.9870	0.9887	0.9904	0.0	0.9713	0.9691	0.9751	0.9769
0.0221	21.0	1050	0.0374	0.7787	0.9866	0.9864	nan	0.9812	0.9873	0.9871	0.9910	0.0	0.9720	0.9697	0.9750	0.9768
0.021	22.0	1100	0.0364	0.7787	0.9867	0.9865	nan	0.9804	0.9874	0.9884	0.9906	0.0	0.9718	0.9695	0.9753	0.9771
0.021	23.0	1150	0.0375	0.7784	0.9865	0.9863	nan	0.9792	0.9883	0.9888	0.9897	0.0	0.9708	0.9687	0.9754	0.9774
0.0199	24.0	1200	0.0371	0.7782	0.9864	0.9861	nan	0.9792	0.9871	0.9878	0.9913	0.0	0.9709	0.9684	0.9749	0.9768
0.0199	25.0	1250	0.0393	0.7784	0.9865	0.9862	nan	0.9788	0.9885	0.9890	0.9897	0.0	0.9707	0.9683	0.9754	0.9776
0.0191	26.0	1300	0.0387	0.7783	0.9865	0.9862	nan	0.9791	0.9878	0.9904	0.9887	0.0	0.9709	0.9683	0.9750	0.9775
0.0191	27.0	1350	0.0384	0.7785	0.9865	0.9863	nan	0.9794	0.9880	0.9897	0.9890	0.0	0.9711	0.9685	0.9754	0.9775
0.0188	28.0	1400	0.0383	0.7783	0.9865	0.9862	nan	0.9779	0.9893	0.9884	0.9903	0.0	0.9705	0.9682	0.9754	0.9776
0.0188	29.0	1450	0.0377	0.7784	0.9864	0.9862	nan	0.9785	0.9902	0.9890	0.9880	0.0	0.9703	0.9680	0.9759	0.9775
0.018	30.0	1500	0.0378	0.9732	0.9866	0.9863	nan	0.9794	0.9885	0.9888	0.9895	nan	0.9710	0.9683	0.9757	0.9777
0.018	31.0	1550	0.0379	0.9730	0.9865	0.9862	nan	0.9794	0.9875	0.9901	0.9890	nan	0.9710	0.9681	0.9753	0.9776
0.0175	32.0	1600	0.0381	0.9730	0.9865	0.9862	nan	0.9792	0.9884	0.9894	0.9889	nan	0.9708	0.9682	0.9755	0.9775
0.0175	33.0	1650	0.0394	0.7784	0.9865	0.9862	nan	0.9783	0.9896	0.9894	0.9886	0.0	0.9705	0.9679	0.9758	0.9777
0.0171	34.0	1700	0.0390	0.7784	0.9865	0.9863	nan	0.9800	0.9871	0.9902	0.9887	0.0	0.9712	0.9682	0.9753	0.9775
0.0171	35.0	1750	0.0385	0.9729	0.9865	0.9862	nan	0.9790	0.9878	0.9892	0.9899	nan	0.9710	0.9680	0.9754	0.9774
0.0166	36.0	1800	0.0384	0.9731	0.9865	0.9863	nan	0.9791	0.9884	0.9889	0.9897	nan	0.9711	0.9682	0.9756	0.9775
0.0166	37.0	1850	0.0389	0.9730	0.9865	0.9862	nan	0.9794	0.9875	0.9891	0.9898	nan	0.9711	0.9680	0.9754	0.9775
0.0162	38.0	1900	0.0375	0.9731	0.9865	0.9863	nan	0.9797	0.9879	0.9901	0.9884	nan	0.9711	0.9681	0.9755	0.9777
0.0162	39.0	1950	0.0389	0.9731	0.9866	0.9863	nan	0.9786	0.9891	0.9891	0.9894	nan	0.9709	0.9681	0.9759	0.9776
0.0158	40.0	2000	0.0396	0.9730	0.9865	0.9862	nan	0.9783	0.9897	0.9894	0.9886	nan	0.9705	0.9678	0.9761	0.9777
0.0158	41.0	2050	0.0397	0.7784	0.9865	0.9862	nan	0.9788	0.9889	0.9887	0.9895	0.0	0.9708	0.9679	0.9759	0.9773
0.0156	42.0	2100	0.0401	0.9730	0.9865	0.9862	nan	0.9782	0.9889	0.9890	0.9898	nan	0.9707	0.9678	0.9758	0.9775
0.0156	43.0	2150	0.0399	0.9730	0.9865	0.9862	nan	0.9789	0.9886	0.9896	0.9889	nan	0.9708	0.9678	0.9757	0.9777
0.0154	44.0	2200	0.0407	0.9728	0.9864	0.9861	nan	0.9781	0.9900	0.9884	0.9891	nan	0.9702	0.9673	0.9762	0.9776
0.0154	45.0	2250	0.0405	0.7784	0.9865	0.9862	nan	0.9785	0.9901	0.9896	0.9877	0.0	0.9706	0.9675	0.9761	0.9776
0.0151	46.0	2300	0.0411	0.7782	0.9864	0.9861	nan	0.9784	0.9903	0.9901	0.9866	0.0	0.9704	0.9673	0.9758	0.9775
0.0151	47.0	2350	0.0394	0.9732	0.9866	0.9863	nan	0.9790	0.9896	0.9890	0.9886	nan	0.9709	0.9681	0.9759	0.9777
0.015	48.0	2400	0.0405	0.7784	0.9865	0.9863	nan	0.9787	0.9885	0.9892	0.9898	0.0	0.9708	0.9677	0.9757	0.9780
0.015	49.0	2450	0.0399	0.7786	0.9866	0.9863	nan	0.9787	0.9905	0.9882	0.9888	0.0	0.9707	0.9678	0.9764	0.9779
0.0149	50.0	2500	0.0410	0.7783	0.9864	0.9861	nan	0.9781	0.9895	0.9889	0.9891	0.0	0.9705	0.9673	0.9761	0.9776
0.0149	51.0	2550	0.0405	0.7784	0.9865	0.9862	nan	0.9793	0.9898	0.9895	0.9872	0.0	0.9707	0.9676	0.9763	0.9776
0.0145	52.0	2600	0.0402	0.7785	0.9866	0.9863	nan	0.9788	0.9895	0.9893	0.9887	0.0	0.9710	0.9678	0.9760	0.9778
0.0145	53.0	2650	0.0401	0.7786	0.9866	0.9864	nan	0.9791	0.9889	0.9898	0.9887	0.0	0.9710	0.9680	0.9761	0.9780
0.0144	54.0	2700	0.0392	0.7787	0.9867	0.9864	nan	0.9795	0.9888	0.9887	0.9896	0.0	0.9714	0.9682	0.9761	0.9777
0.0144	55.0	2750	0.0409	0.7784	0.9865	0.9862	nan	0.9787	0.9886	0.9895	0.9891	0.0	0.9706	0.9675	0.9760	0.9777
0.0141	56.0	2800	0.0410	0.7784	0.9865	0.9862	nan	0.9779	0.9897	0.9897	0.9887	0.0	0.9707	0.9675	0.9759	0.9778
0.0141	57.0	2850	0.0412	0.7784	0.9865	0.9862	nan	0.9780	0.9898	0.9891	0.9891	0.0	0.9707	0.9676	0.9761	0.9776
0.014	58.0	2900	0.0403	0.9732	0.9866	0.9863	nan	0.9794	0.9889	0.9889	0.9891	nan	0.9713	0.9680	0.9761	0.9775
0.014	59.0	2950	0.0404	0.7787	0.9867	0.9864	nan	0.9787	0.9899	0.9889	0.9892	0.0	0.9711	0.9680	0.9763	0.9779
0.0139	60.0	3000	0.0412	0.7783	0.9865	0.9862	nan	0.9786	0.9893	0.9900	0.9879	0.0	0.9708	0.9675	0.9758	0.9775
0.0139	61.0	3050	0.0410	0.7785	0.9866	0.9863	nan	0.9789	0.9893	0.9901	0.9879	0.0	0.9708	0.9676	0.9762	0.9780
0.0138	62.0	3100	0.0413	0.7784	0.9865	0.9862	nan	0.9778	0.9896	0.9893	0.9894	0.0	0.9705	0.9675	0.9763	0.9779
0.0138	63.0	3150	0.0400	0.7786	0.9866	0.9863	nan	0.9794	0.9887	0.9908	0.9874	0.0	0.9715	0.9681	0.9757	0.9776
0.0138	64.0	3200	0.0401	0.7786	0.9866	0.9864	nan	0.9800	0.9888	0.9904	0.9873	0.0	0.9715	0.9682	0.9758	0.9776
0.0138	65.0	3250	0.0414	0.7784	0.9865	0.9862	nan	0.9788	0.9888	0.9905	0.9879	0.0	0.9708	0.9675	0.9759	0.9776
0.0136	66.0	3300	0.0397	0.7787	0.9867	0.9864	nan	0.9796	0.9895	0.9897	0.9880	0.0	0.9714	0.9683	0.9763	0.9776
0.0136	67.0	3350	0.0417	0.7783	0.9864	0.9861	nan	0.9777	0.9894	0.9903	0.9884	0.0	0.9702	0.9671	0.9761	0.9779
0.0135	68.0	3400	0.0409	0.7784	0.9865	0.9862	nan	0.9790	0.9898	0.9908	0.9862	0.0	0.9711	0.9678	0.9758	0.9773
0.0135	69.0	3450	0.0399	0.7787	0.9867	0.9864	nan	0.9796	0.9896	0.9887	0.9888	0.0	0.9714	0.9681	0.9764	0.9778
0.0133	70.0	3500	0.0407	0.7785	0.9865	0.9863	nan	0.9792	0.9903	0.9901	0.9866	0.0	0.9713	0.9676	0.9761	0.9775
0.0133	71.0	3550	0.0407	0.7786	0.9866	0.9864	nan	0.9787	0.9896	0.9892	0.9890	0.0	0.9712	0.9679	0.9761	0.9778
0.0131	72.0	3600	0.0394	0.7789	0.9868	0.9865	nan	0.9790	0.9899	0.9895	0.9887	0.0	0.9714	0.9681	0.9766	0.9781
0.0131	73.0	3650	0.0410	0.7785	0.9865	0.9863	nan	0.9796	0.9897	0.9903	0.9865	0.0	0.9713	0.9678	0.9759	0.9774
0.0132	74.0	3700	0.0412	0.7785	0.9866	0.9863	nan	0.9791	0.9900	0.9901	0.9871	0.0	0.9713	0.9678	0.9761	0.9774
0.0132	75.0	3750	0.0412	0.7786	0.9866	0.9863	nan	0.9785	0.9902	0.9898	0.9879	0.0	0.9711	0.9676	0.9763	0.9779
0.0131	76.0	3800	0.0396	0.7786	0.9866	0.9864	nan	0.9798	0.9893	0.9904	0.9870	0.0	0.9716	0.9682	0.9760	0.9775
0.0131	77.0	3850	0.0418	0.7784	0.9865	0.9862	nan	0.9789	0.9896	0.9905	0.9871	0.0	0.9711	0.9676	0.9760	0.9775
0.013	78.0	3900	0.0396	0.7786	0.9866	0.9864	nan	0.9787	0.9899	0.9906	0.9872	0.0	0.9713	0.9678	0.9760	0.9779
0.013	79.0	3950	0.0398	0.7787	0.9867	0.9864	nan	0.9794	0.9898	0.9905	0.9869	0.0	0.9715	0.9680	0.9762	0.9777
0.0128	80.0	4000	0.0402	0.7788	0.9867	0.9865	nan	0.9789	0.9898	0.9896	0.9885	0.0	0.9714	0.9680	0.9765	0.9779
0.0128	81.0	4050	0.0404	0.7787	0.9867	0.9864	nan	0.9787	0.9903	0.9902	0.9874	0.0	0.9713	0.9677	0.9763	0.9779
0.0127	82.0	4100	0.0397	0.7787	0.9867	0.9864	nan	0.9794	0.9896	0.9901	0.9877	0.0	0.9716	0.9681	0.9762	0.9778
0.0127	83.0	4150	0.0411	0.7786	0.9866	0.9863	nan	0.9786	0.9898	0.9899	0.9881	0.0	0.9712	0.9677	0.9763	0.9778
0.0127	84.0	4200	0.0406	0.7788	0.9867	0.9865	nan	0.9787	0.9903	0.9890	0.9889	0.0	0.9713	0.9680	0.9766	0.9781
0.0127	85.0	4250	0.0413	0.7786	0.9866	0.9864	nan	0.9787	0.9900	0.9888	0.9891	0.0	0.9711	0.9677	0.9764	0.9779
0.0126	86.0	4300	0.0400	0.7788	0.9867	0.9865	nan	0.9792	0.9904	0.9895	0.9878	0.0	0.9715	0.9681	0.9765	0.9778
0.0126	87.0	4350	0.0397	0.7788	0.9868	0.9865	nan	0.9789	0.9898	0.9898	0.9885	0.0	0.9715	0.9682	0.9765	0.9780
0.0125	88.0	4400	0.0398	0.7788	0.9868	0.9865	nan	0.9791	0.9903	0.9894	0.9883	0.0	0.9716	0.9681	0.9767	0.9779
0.0125	89.0	4450	0.0400	0.7787	0.9867	0.9865	nan	0.9795	0.9898	0.9902	0.9872	0.0	0.9716	0.9682	0.9763	0.9776
0.0125	90.0	4500	0.0397	0.7788	0.9867	0.9865	nan	0.9788	0.9902	0.9893	0.9887	0.0	0.9716	0.9680	0.9765	0.9779
0.0125	91.0	4550	0.0400	0.7787	0.9867	0.9864	nan	0.9790	0.9901	0.9903	0.9875	0.0	0.9715	0.9680	0.9762	0.9779
0.0125	92.0	4600	0.0392	0.7787	0.9867	0.9864	nan	0.9790	0.9903	0.9898	0.9878	0.0	0.9716	0.9680	0.9765	0.9777
0.0125	93.0	4650	0.0403	0.7787	0.9867	0.9864	nan	0.9791	0.9900	0.9905	0.9873	0.0	0.9716	0.9681	0.9763	0.9777
0.0123	94.0	4700	0.0396	0.7789	0.9868	0.9865	nan	0.9797	0.9898	0.9903	0.9874	0.0	0.9718	0.9684	0.9764	0.9778
0.0123	95.0	4750	0.0405	0.7787	0.9867	0.9864	nan	0.9790	0.9901	0.9903	0.9874	0.0	0.9715	0.9679	0.9764	0.9778
0.0122	96.0	4800	0.0394	0.7789	0.9868	0.9865	nan	0.9793	0.9896	0.9898	0.9884	0.0	0.9717	0.9682	0.9764	0.9780
0.0122	97.0	4850	0.0396	0.7789	0.9868	0.9865	nan	0.9790	0.9900	0.9900	0.9882	0.0	0.9716	0.9681	0.9766	0.9780
0.0122	98.0	4900	0.0399	0.7788	0.9867	0.9865	nan	0.9797	0.9900	0.9904	0.9870	0.0	0.9718	0.9682	0.9764	0.9776
0.0122	99.0	4950	0.0394	0.7789	0.9868	0.9865	nan	0.9793	0.9896	0.9897	0.9885	0.0	0.9717	0.9682	0.9766	0.9780
0.0122	100.0	5000	0.0383	0.7790	0.9868	0.9866	nan	0.9804	0.9899	0.9895	0.9876	0.0	0.9720	0.9686	0.9767	0.9777
0.0122	101.0	5050	0.0399	0.7788	0.9867	0.9865	nan	0.9794	0.9904	0.9895	0.9877	0.0	0.9716	0.9680	0.9766	0.9779
0.0121	102.0	5100	0.0392	0.7790	0.9868	0.9866	nan	0.9796	0.9898	0.9889	0.9890	0.0	0.9718	0.9685	0.9767	0.9779
0.0121	103.0	5150	0.0393	0.7788	0.9867	0.9865	nan	0.9788	0.9901	0.9900	0.9881	0.0	0.9715	0.9679	0.9765	0.9781
0.012	104.0	5200	0.0400	0.7788	0.9867	0.9865	nan	0.9790	0.9894	0.9904	0.9881	0.0	0.9716	0.9682	0.9763	0.9779
0.012	105.0	5250	0.0393	0.7789	0.9868	0.9865	nan	0.9796	0.9894	0.9904	0.9878	0.0	0.9718	0.9683	0.9764	0.9780
0.012	106.0	5300	0.0390	0.7789	0.9868	0.9866	nan	0.9794	0.9900	0.9890	0.9888	0.0	0.9719	0.9683	0.9766	0.9780
0.012	107.0	5350	0.0383	0.7790	0.9868	0.9866	nan	0.9801	0.9899	0.9903	0.9870	0.0	0.9721	0.9684	0.9765	0.9779
0.0119	108.0	5400	0.0380	0.7792	0.9870	0.9868	nan	0.9807	0.9892	0.9897	0.9883	0.0	0.9724	0.9690	0.9768	0.9780
0.0119	109.0	5450	0.0400	0.7787	0.9867	0.9864	nan	0.9786	0.9902	0.9902	0.9876	0.0	0.9714	0.9677	0.9764	0.9778
0.0119	110.0	5500	0.0385	0.7791	0.9869	0.9867	nan	0.9801	0.9894	0.9891	0.9889	0.0	0.9721	0.9686	0.9768	0.9780
0.0119	111.0	5550	0.0385	0.7790	0.9869	0.9866	nan	0.9798	0.9896	0.9902	0.9879	0.0	0.9719	0.9685	0.9767	0.9781
0.0118	112.0	5600	0.0377	0.7791	0.9869	0.9867	nan	0.9798	0.9891	0.9897	0.9891	0.0	0.9722	0.9687	0.9766	0.9782
0.0118	113.0	5650	0.0388	0.7790	0.9869	0.9866	nan	0.9794	0.9899	0.9904	0.9878	0.0	0.9719	0.9683	0.9767	0.9781
0.0118	114.0	5700	0.0391	0.7789	0.9868	0.9866	nan	0.9797	0.9891	0.9906	0.9880	0.0	0.9719	0.9683	0.9763	0.9781
0.0118	115.0	5750	0.0390	0.7789	0.9868	0.9866	nan	0.9796	0.9902	0.9899	0.9876	0.0	0.9719	0.9683	0.9766	0.9779
0.0118	116.0	5800	0.0390	0.7789	0.9868	0.9866	nan	0.9795	0.9899	0.9896	0.9882	0.0	0.9718	0.9682	0.9767	0.9779
0.0118	117.0	5850	0.0394	0.7788	0.9867	0.9865	nan	0.9791	0.9899	0.9896	0.9883	0.0	0.9717	0.9679	0.9765	0.9778
0.0117	118.0	5900	0.0386	0.7789	0.9868	0.9866	nan	0.9796	0.9898	0.9900	0.9879	0.0	0.9719	0.9682	0.9766	0.9779
0.0117	119.0	5950	0.0386	0.7791	0.9869	0.9867	nan	0.9800	0.9895	0.9896	0.9885	0.0	0.9721	0.9686	0.9767	0.9781
0.0117	120.0	6000	0.0388	0.7790	0.9869	0.9866	nan	0.9796	0.9899	0.9902	0.9878	0.0	0.9719	0.9684	0.9767	0.9781
0.0117	121.0	6050	0.0389	0.7790	0.9868	0.9866	nan	0.9800	0.9896	0.9894	0.9883	0.0	0.9721	0.9684	0.9767	0.9778
0.0116	122.0	6100	0.0384	0.7790	0.9869	0.9866	nan	0.9796	0.9896	0.9897	0.9886	0.0	0.9720	0.9684	0.9767	0.9780
0.0116	123.0	6150	0.0386	0.7789	0.9868	0.9865	nan	0.9793	0.9899	0.9901	0.9879	0.0	0.9718	0.9680	0.9765	0.9781
0.0115	124.0	6200	0.0383	0.7792	0.9870	0.9867	nan	0.9802	0.9890	0.9900	0.9888	0.0	0.9722	0.9688	0.9767	0.9781
0.0115	125.0	6250	0.0381	0.7790	0.9869	0.9866	nan	0.9796	0.9892	0.9900	0.9888	0.0	0.9721	0.9685	0.9766	0.9780
0.0115	126.0	6300	0.0383	0.7791	0.9869	0.9867	nan	0.9797	0.9894	0.9894	0.9893	0.0	0.9720	0.9686	0.9767	0.9782
0.0115	127.0	6350	0.0384	0.7790	0.9869	0.9866	nan	0.9797	0.9895	0.9901	0.9881	0.0	0.9719	0.9684	0.9766	0.9781
0.0115	128.0	6400	0.0377	0.7792	0.9870	0.9867	nan	0.9801	0.9891	0.9896	0.9891	0.0	0.9722	0.9688	0.9767	0.9781
0.0115	129.0	6450	0.0383	0.7791	0.9869	0.9867	nan	0.9800	0.9898	0.9899	0.9880	0.0	0.9721	0.9685	0.9768	0.9782
0.0115	130.0	6500	0.0377	0.7791	0.9870	0.9867	nan	0.9797	0.9895	0.9901	0.9885	0.0	0.9723	0.9687	0.9767	0.9781
0.0115	131.0	6550	0.0380	0.7791	0.9869	0.9867	nan	0.9800	0.9891	0.9897	0.9890	0.0	0.9722	0.9687	0.9767	0.9780
0.0114	132.0	6600	0.0377	0.7792	0.9870	0.9868	nan	0.9799	0.9893	0.9901	0.9887	0.0	0.9724	0.9689	0.9766	0.9782
0.0114	133.0	6650	0.0378	0.7792	0.9870	0.9867	nan	0.9801	0.9899	0.9897	0.9882	0.0	0.9722	0.9687	0.9769	0.9782
0.0114	134.0	6700	0.0379	0.7791	0.9869	0.9867	nan	0.9801	0.9896	0.9902	0.9879	0.0	0.9723	0.9688	0.9767	0.9780
0.0114	135.0	6750	0.0374	0.7793	0.9870	0.9868	nan	0.9803	0.9894	0.9899	0.9884	0.0	0.9724	0.9690	0.9768	0.9782
0.0113	136.0	6800	0.0386	0.7790	0.9869	0.9866	nan	0.9796	0.9897	0.9903	0.9878	0.0	0.9720	0.9683	0.9766	0.9781
0.0113	137.0	6850	0.0378	0.9739	0.9870	0.9867	nan	0.9802	0.9895	0.9900	0.9880	nan	0.9724	0.9688	0.9766	0.9779
0.0114	138.0	6900	0.0378	0.9740	0.9870	0.9868	nan	0.9800	0.9895	0.9893	0.9893	nan	0.9722	0.9688	0.9769	0.9783
0.0114	139.0	6950	0.0380	0.7791	0.9869	0.9867	nan	0.9797	0.9896	0.9897	0.9888	0.0	0.9722	0.9685	0.9767	0.9782
0.0113	140.0	7000	0.0374	0.7793	0.9871	0.9868	nan	0.9803	0.9893	0.9899	0.9887	0.0	0.9725	0.9690	0.9768	0.9783
0.0113	141.0	7050	0.0378	0.7792	0.9870	0.9868	nan	0.9801	0.9894	0.9900	0.9886	0.0	0.9724	0.9689	0.9767	0.9781
0.0112	142.0	7100	0.0380	0.9740	0.9870	0.9868	nan	0.9801	0.9899	0.9897	0.9882	nan	0.9724	0.9687	0.9768	0.9782
0.0112	143.0	7150	0.0380	0.9740	0.9870	0.9868	nan	0.9800	0.9897	0.9899	0.9883	nan	0.9724	0.9688	0.9768	0.9781
0.0112	144.0	7200	0.0378	0.9741	0.9870	0.9868	nan	0.9802	0.9896	0.9897	0.9887	nan	0.9725	0.9690	0.9768	0.9781
0.0112	145.0	7250	0.0376	0.7793	0.9870	0.9868	nan	0.9806	0.9892	0.9903	0.9880	0.0	0.9726	0.9690	0.9767	0.9782
0.0112	146.0	7300	0.0380	0.7792	0.9870	0.9867	nan	0.9801	0.9899	0.9898	0.9880	0.0	0.9724	0.9687	0.9767	0.9780
0.0112	147.0	7350	0.0381	0.9740	0.9870	0.9867	nan	0.9800	0.9900	0.9899	0.9880	nan	0.9723	0.9687	0.9767	0.9781
0.0111	148.0	7400	0.0374	0.9742	0.9871	0.9868	nan	0.9805	0.9895	0.9900	0.9883	nan	0.9726	0.9690	0.9768	0.9782
0.0111	149.0	7450	0.0378	0.9740	0.9870	0.9868	nan	0.9801	0.9897	0.9902	0.9879	nan	0.9724	0.9687	0.9767	0.9781
0.0112	150.0	7500	0.0377	0.9741	0.9870	0.9868	nan	0.9800	0.9892	0.9897	0.9891	nan	0.9725	0.9690	0.9767	0.9781
0.0112	151.0	7550	0.0377	0.9742	0.9871	0.9868	nan	0.9802	0.9893	0.9895	0.9893	nan	0.9725	0.9691	0.9768	0.9782
0.0111	152.0	7600	0.0374	0.9741	0.9870	0.9868	nan	0.9804	0.9898	0.9898	0.9883	nan	0.9726	0.9690	0.9768	0.9782
0.0111	153.0	7650	0.0380	0.9740	0.9870	0.9868	nan	0.9800	0.9898	0.9897	0.9884	nan	0.9725	0.9688	0.9767	0.9781
0.0111	154.0	7700	0.0373	0.9742	0.9871	0.9869	nan	0.9805	0.9891	0.9901	0.9887	nan	0.9727	0.9692	0.9767	0.9782
0.0111	155.0	7750	0.0375	0.9742	0.9871	0.9868	nan	0.9804	0.9896	0.9893	0.9891	nan	0.9727	0.9692	0.9768	0.9781
0.0111	156.0	7800	0.0378	0.9741	0.9870	0.9868	nan	0.9801	0.9898	0.9897	0.9886	nan	0.9727	0.9689	0.9767	0.9781
0.0111	157.0	7850	0.0376	0.9742	0.9871	0.9868	nan	0.9805	0.9891	0.9902	0.9885	nan	0.9727	0.9691	0.9766	0.9782
0.0111	158.0	7900	0.0375	0.9742	0.9871	0.9868	nan	0.9804	0.9893	0.9899	0.9887	nan	0.9727	0.9691	0.9767	0.9782
0.0111	159.0	7950	0.0372	0.9743	0.9871	0.9869	nan	0.9805	0.9892	0.9904	0.9884	nan	0.9728	0.9693	0.9766	0.9783
0.0111	160.0	8000	0.0367	0.9743	0.9871	0.9869	nan	0.9809	0.9896	0.9898	0.9882	nan	0.9730	0.9693	0.9768	0.9782
0.0111	161.0	8050	0.0370	0.9744	0.9871	0.9869	nan	0.9808	0.9898	0.9894	0.9886	nan	0.9728	0.9693	0.9770	0.9783
0.0111	162.0	8100	0.0371	0.9743	0.9871	0.9869	nan	0.9806	0.9892	0.9901	0.9885	nan	0.9729	0.9694	0.9767	0.9782
0.0111	163.0	8150	0.0372	0.9743	0.9871	0.9869	nan	0.9807	0.9894	0.9901	0.9882	nan	0.9729	0.9694	0.9767	0.9781
0.011	164.0	8200	0.0373	0.9743	0.9871	0.9869	nan	0.9805	0.9896	0.9894	0.9889	nan	0.9728	0.9693	0.9768	0.9781
0.011	165.0	8250	0.0371	0.9743	0.9871	0.9869	nan	0.9808	0.9897	0.9898	0.9882	nan	0.9729	0.9694	0.9768	0.9783
0.011	166.0	8300	0.0372	0.9743	0.9871	0.9869	nan	0.9806	0.9897	0.9898	0.9884	nan	0.9729	0.9693	0.9768	0.9781
0.011	167.0	8350	0.0373	0.9743	0.9871	0.9869	nan	0.9804	0.9896	0.9900	0.9885	nan	0.9728	0.9692	0.9768	0.9783
0.011	168.0	8400	0.0369	0.9744	0.9872	0.9870	nan	0.9807	0.9895	0.9899	0.9885	nan	0.9731	0.9695	0.9767	0.9782
0.011	169.0	8450	0.0375	0.9742	0.9871	0.9869	nan	0.9802	0.9897	0.9898	0.9886	nan	0.9727	0.9691	0.9768	0.9782
0.0109	170.0	8500	0.0363	0.9746	0.9873	0.9871	nan	0.9814	0.9892	0.9894	0.9891	nan	0.9734	0.9699	0.9769	0.9782
0.0109	171.0	8550	0.0371	0.9743	0.9871	0.9869	nan	0.9805	0.9895	0.9900	0.9885	nan	0.9729	0.9693	0.9767	0.9782
0.011	172.0	8600	0.0371	0.9743	0.9871	0.9869	nan	0.9807	0.9896	0.9898	0.9885	nan	0.9729	0.9693	0.9768	0.9782
0.011	173.0	8650	0.0373	0.9743	0.9871	0.9869	nan	0.9807	0.9893	0.9901	0.9885	nan	0.9728	0.9694	0.9767	0.9783
0.0109	174.0	8700	0.0372	0.9744	0.9872	0.9869	nan	0.9806	0.9894	0.9898	0.9889	nan	0.9729	0.9694	0.9768	0.9783
0.0109	175.0	8750	0.0373	0.9743	0.9871	0.9869	nan	0.9806	0.9895	0.9899	0.9885	nan	0.9729	0.9694	0.9768	0.9782
0.0109	176.0	8800	0.0371	0.9744	0.9872	0.9869	nan	0.9808	0.9894	0.9898	0.9886	nan	0.9730	0.9694	0.9768	0.9782
0.0109	177.0	8850	0.0370	0.9744	0.9872	0.9870	nan	0.9808	0.9897	0.9896	0.9886	nan	0.9730	0.9695	0.9768	0.9782
0.0109	178.0	8900	0.0373	0.9744	0.9872	0.9869	nan	0.9808	0.9895	0.9899	0.9885	nan	0.9729	0.9694	0.9768	0.9783
0.0109	179.0	8950	0.0372	0.9743	0.9871	0.9869	nan	0.9806	0.9894	0.9897	0.9888	nan	0.9729	0.9694	0.9768	0.9782
0.0109	180.0	9000	0.0368	0.9744	0.9872	0.9870	nan	0.9811	0.9894	0.9897	0.9885	nan	0.9731	0.9696	0.9768	0.9781
0.0109	181.0	9050	0.0371	0.9744	0.9872	0.9869	nan	0.9807	0.9894	0.9900	0.9886	nan	0.9730	0.9694	0.9768	0.9783
0.0109	182.0	9100	0.0370	0.9744	0.9872	0.9869	nan	0.9808	0.9894	0.9898	0.9887	nan	0.9730	0.9695	0.9768	0.9782
0.0109	183.0	9150	0.0368	0.9744	0.9872	0.9870	nan	0.9810	0.9892	0.9901	0.9885	nan	0.9732	0.9697	0.9767	0.9782
0.0108	184.0	9200	0.0373	0.9743	0.9871	0.9869	nan	0.9805	0.9896	0.9897	0.9887	nan	0.9729	0.9693	0.9767	0.9782
0.0108	185.0	9250	0.0371	0.9743	0.9872	0.9869	nan	0.9806	0.9895	0.9900	0.9885	nan	0.9730	0.9694	0.9767	0.9783
0.0108	186.0	9300	0.0371	0.9744	0.9872	0.9870	nan	0.9808	0.9897	0.9896	0.9886	nan	0.9731	0.9695	0.9769	0.9782
0.0108	187.0	9350	0.0371	0.9744	0.9872	0.9870	nan	0.9808	0.9895	0.9899	0.9886	nan	0.9731	0.9695	0.9768	0.9782
0.0108	188.0	9400	0.0370	0.9744	0.9872	0.9870	nan	0.9808	0.9893	0.9900	0.9886	nan	0.9730	0.9695	0.9768	0.9782
0.0108	189.0	9450	0.0371	0.9743	0.9872	0.9869	nan	0.9807	0.9895	0.9901	0.9883	nan	0.9730	0.9694	0.9767	0.9782
0.0108	190.0	9500	0.0370	0.9744	0.9872	0.9870	nan	0.9808	0.9896	0.9899	0.9885	nan	0.9731	0.9695	0.9768	0.9782
0.0108	191.0	9550	0.0373	0.9743	0.9871	0.9869	nan	0.9806	0.9895	0.9899	0.9886	nan	0.9729	0.9693	0.9768	0.9782
0.0108	192.0	9600	0.0371	0.9744	0.9872	0.9869	nan	0.9807	0.9894	0.9899	0.9887	nan	0.9730	0.9695	0.9768	0.9782
0.0108	193.0	9650	0.0374	0.9743	0.9871	0.9869	nan	0.9805	0.9898	0.9897	0.9886	nan	0.9729	0.9693	0.9768	0.9782
0.0108	194.0	9700	0.0371	0.9744	0.9872	0.9870	nan	0.9807	0.9896	0.9899	0.9885	nan	0.9730	0.9695	0.9768	0.9783
0.0108	195.0	9750	0.0370	0.9744	0.9872	0.9870	nan	0.9809	0.9896	0.9898	0.9885	nan	0.9731	0.9696	0.9768	0.9782
0.0108	196.0	9800	0.0370	0.9745	0.9872	0.9870	nan	0.9810	0.9894	0.9898	0.9887	nan	0.9732	0.9697	0.9768	0.9782
0.0108	197.0	9850	0.0371	0.9744	0.9872	0.9870	nan	0.9809	0.9896	0.9897	0.9886	nan	0.9731	0.9695	0.9768	0.9782
0.0108	198.0	9900	0.0371	0.9744	0.9872	0.9870	nan	0.9809	0.9896	0.9897	0.9886	nan	0.9731	0.9695	0.9768	0.9782
0.0108	199.0	9950	0.0371	0.9744	0.9872	0.9870	nan	0.9809	0.9895	0.9898	0.9886	nan	0.9731	0.9696	0.9768	0.9782
0.0108	200.0	10000	0.0372	0.9744	0.9872	0.9869	nan	0.9806	0.9893	0.9901	0.9887	nan	0.9730	0.9695	0.9767	0.9782

Framework versions

Transformers 4.52.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

TommyClas/phaseseg_models

phaseseg_models

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

TommyClas/
phaseseg_models