SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Hard

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0143
• Mean Iou: 0.9789
• Mean Accuracy: 0.9908
• Overall Accuracy: 0.9945
• Accuracy Background: 0.9964
• Accuracy Melt: 0.9810
• Accuracy Substrate: 0.9951
• Iou Background: 0.9930
• Iou Melt: 0.9518
• Iou Substrate: 0.9919

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 25

Training results

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