Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Melt	Accuracy Substrate	Iou Background	Iou Melt	Iou Substrate
0.3678	0.3030	50	0.1206	0.8584	0.9180	0.9591	0.9811	0.8082	0.9648	0.9560	0.6832	0.9361
0.1315	0.6061	100	0.0573	0.9293	0.9609	0.9808	0.9953	0.9068	0.9805	0.9764	0.8404	0.9710
0.0983	0.9091	150	0.0426	0.9427	0.9712	0.9855	0.9927	0.9330	0.9879	0.9865	0.8645	0.9772
0.0302	1.2121	200	0.0397	0.9420	0.9562	0.9860	0.9937	0.8783	0.9965	0.9870	0.8609	0.9781
0.0378	1.5152	250	0.0366	0.9447	0.9804	0.9856	0.9916	0.9655	0.9840	0.9872	0.8704	0.9765
0.232	1.8182	300	0.0278	0.9582	0.9810	0.9893	0.9894	0.9599	0.9938	0.9875	0.9026	0.9844
0.023	2.1212	350	0.0252	0.9630	0.9821	0.9905	0.9958	0.9595	0.9910	0.9895	0.9141	0.9852
0.0254	2.4242	400	0.0263	0.9626	0.9841	0.9901	0.9964	0.9675	0.9885	0.9887	0.9146	0.9846
0.0153	2.7273	450	0.0299	0.9613	0.9735	0.9906	0.9952	0.9290	0.9963	0.9904	0.9080	0.9855
0.0172	3.0303	500	0.0230	0.9645	0.9776	0.9913	0.9956	0.9417	0.9956	0.9917	0.9153	0.9864
0.0338	3.3333	550	0.0185	0.9723	0.9875	0.9928	0.9972	0.9733	0.9922	0.9913	0.9368	0.9889
0.0168	3.6364	600	0.0231	0.9679	0.9788	0.9922	0.9969	0.9438	0.9958	0.9921	0.9237	0.9878
0.0253	3.9394	650	0.0245	0.9664	0.9772	0.9918	0.9965	0.9388	0.9962	0.9920	0.9202	0.9869
0.0163	4.2424	700	0.0191	0.9689	0.9832	0.9923	0.9961	0.9592	0.9943	0.9917	0.9270	0.9881
0.0133	4.5455	750	0.0173	0.9745	0.9877	0.9932	0.9976	0.9728	0.9928	0.9913	0.9428	0.9895
0.0133	4.8485	800	0.0171	0.9742	0.9876	0.9934	0.9965	0.9721	0.9942	0.9921	0.9405	0.9901
0.0362	5.1515	850	0.0178	0.9725	0.9866	0.9931	0.9973	0.9692	0.9934	0.9918	0.9360	0.9897
0.0142	5.4545	900	0.0208	0.9679	0.9888	0.9919	0.9961	0.9797	0.9904	0.9919	0.9244	0.9874
0.0111	5.7576	950	0.0149	0.9772	0.9882	0.9941	0.9964	0.9727	0.9956	0.9924	0.9478	0.9915
0.0184	6.0606	1000	0.0165	0.9737	0.9822	0.9934	0.9977	0.9525	0.9963	0.9915	0.9388	0.9909
0.0181	6.3636	1050	0.0157	0.9759	0.9853	0.9938	0.9973	0.9628	0.9959	0.9924	0.9443	0.9909
0.0138	6.6667	1100	0.0143	0.9781	0.9907	0.9943	0.9966	0.9811	0.9945	0.9926	0.9501	0.9917
0.0287	6.9697	1150	0.0161	0.9747	0.9875	0.9934	0.9976	0.9714	0.9935	0.9920	0.9420	0.9900
0.0144	7.2727	1200	0.0149	0.9774	0.9894	0.9940	0.9974	0.9771	0.9938	0.9920	0.9493	0.9909
0.012	7.5758	1250	0.0139	0.9783	0.9906	0.9943	0.9971	0.9805	0.9942	0.9929	0.9506	0.9915
0.0098	7.8788	1300	0.0134	0.9793	0.9901	0.9945	0.9976	0.9782	0.9945	0.9927	0.9533	0.9918
0.0105	8.1818	1350	0.0182	0.9780	0.9895	0.9942	0.9971	0.9768	0.9946	0.9926	0.9500	0.9913
0.014	8.4848	1400	0.0141	0.9784	0.9896	0.9943	0.9969	0.9769	0.9948	0.9924	0.9512	0.9916
0.0117	8.7879	1450	0.0154	0.9767	0.9911	0.9938	0.9968	0.9834	0.9930	0.9917	0.9477	0.9908
0.0153	9.0909	1500	0.0143	0.9789	0.9908	0.9945	0.9964	0.9810	0.9951	0.9930	0.9518	0.9919

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Hard

SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Hard

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hasano20/
SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Hard