segformer-finetuned-biofilm_MRCNNv1_train80_val20

This model is a fine-tuned version of nvidia/mit-b0 on the heroza/biofilm_MRCNNv1_train80_val20 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0785
• Mean Iou: 0.8728
• Mean Accuracy: 0.9695
• Overall Accuracy: 0.9773
• Accuracy Background: 0.9788
• Accuracy Biofilm: 0.9603
• Iou Background: 0.9755
• Iou Biofilm: 0.7702

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: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: polynomial
• num_epochs: 50.0

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Biofilm	Iou Background	Iou Biofilm
0.1517	1.0	280	0.1319	0.8108	0.8974	0.9664	0.9794	0.8154	0.9641	0.6575
0.0728	2.0	560	0.0636	0.8857	0.9582	0.9807	0.9849	0.9314	0.9791	0.7923
0.0555	3.0	840	0.0526	0.8863	0.9417	0.9815	0.9889	0.8944	0.9800	0.7925
0.045	4.0	1120	0.0522	0.8763	0.9225	0.9802	0.9911	0.8539	0.9788	0.7738
0.048	5.0	1400	0.0415	0.9085	0.9742	0.9848	0.9868	0.9616	0.9835	0.8335
0.0425	6.0	1680	0.0468	0.9041	0.9790	0.9837	0.9846	0.9733	0.9824	0.8258
0.0438	7.0	1960	0.0461	0.9001	0.9717	0.9832	0.9853	0.9581	0.9818	0.8185
0.0406	8.0	2240	0.0488	0.8936	0.9701	0.9819	0.9841	0.9561	0.9804	0.8068
0.0354	9.0	2520	0.0434	0.9004	0.9648	0.9835	0.9870	0.9426	0.9821	0.8187
0.0411	10.0	2800	0.0467	0.9021	0.9680	0.9837	0.9867	0.9494	0.9824	0.8219
0.0385	11.0	3080	0.0846	0.8493	0.9697	0.9716	0.9720	0.9674	0.9692	0.7294
0.0327	12.0	3360	0.0574	0.8709	0.9643	0.9771	0.9795	0.9491	0.9753	0.7666
0.0333	13.0	3640	0.0654	0.8649	0.9645	0.9757	0.9778	0.9513	0.9737	0.7561
0.0356	14.0	3920	0.0823	0.8472	0.9703	0.9710	0.9712	0.9694	0.9686	0.7259
0.0277	15.0	4200	0.0657	0.8634	0.9761	0.9748	0.9745	0.9777	0.9727	0.7542
0.0328	16.0	4480	0.0575	0.8785	0.9668	0.9787	0.9810	0.9526	0.9770	0.7800
0.0362	17.0	4760	0.0595	0.8750	0.9696	0.9778	0.9794	0.9599	0.9760	0.7741
0.0301	18.0	5040	0.0610	0.8701	0.9755	0.9764	0.9766	0.9744	0.9744	0.7659
0.0284	19.0	5320	0.0562	0.8874	0.9748	0.9804	0.9814	0.9682	0.9787	0.7961
0.0345	20.0	5600	0.0612	0.8704	0.9696	0.9767	0.9781	0.9611	0.9748	0.7659
0.0292	21.0	5880	0.0689	0.8639	0.9728	0.9751	0.9755	0.9701	0.9730	0.7549
0.0287	22.0	6160	0.0559	0.8821	0.9657	0.9796	0.9822	0.9493	0.9779	0.7862
0.0307	23.0	6440	0.0683	0.8637	0.9716	0.9751	0.9757	0.9674	0.9730	0.7545
0.0303	24.0	6720	0.0692	0.8702	0.9627	0.9770	0.9797	0.9457	0.9752	0.7653
0.0257	25.0	7000	0.0594	0.8783	0.9709	0.9785	0.9799	0.9619	0.9767	0.7798
0.0341	26.0	7280	0.0762	0.8619	0.9746	0.9745	0.9745	0.9747	0.9723	0.7515
0.025	27.0	7560	0.0675	0.8696	0.9751	0.9763	0.9765	0.9736	0.9743	0.7649
0.0281	28.0	7840	0.0661	0.8641	0.9694	0.9753	0.9764	0.9625	0.9732	0.7550
0.0285	29.0	8120	0.0796	0.8592	0.9737	0.9739	0.9739	0.9736	0.9717	0.7467
0.0263	30.0	8400	0.0760	0.8627	0.9712	0.9749	0.9755	0.9668	0.9728	0.7527
0.0252	31.0	8680	0.0615	0.8800	0.9642	0.9792	0.9820	0.9464	0.9775	0.7825
0.0245	32.0	8960	0.0647	0.8665	0.9642	0.9761	0.9783	0.9501	0.9742	0.7589
0.0241	33.0	9240	0.0638	0.8749	0.9668	0.9779	0.9800	0.9535	0.9761	0.7736
0.0249	34.0	9520	0.0803	0.8610	0.9709	0.9744	0.9751	0.9667	0.9723	0.7497
0.0213	35.0	9800	0.0754	0.8687	0.9633	0.9767	0.9792	0.9474	0.9748	0.7627
0.0233	36.0	10080	0.0675	0.8743	0.9612	0.9780	0.9812	0.9411	0.9763	0.7723
0.0214	37.0	10360	0.0695	0.8758	0.9714	0.9779	0.9791	0.9637	0.9761	0.7755
0.0231	38.0	10640	0.0704	0.8695	0.9621	0.9769	0.9797	0.9444	0.9750	0.7640
0.0231	39.0	10920	0.0780	0.8636	0.9646	0.9754	0.9774	0.9518	0.9734	0.7539
0.0221	40.0	11200	0.0726	0.8709	0.9666	0.9770	0.9790	0.9542	0.9751	0.7666
0.0227	41.0	11480	0.0829	0.8618	0.9627	0.9751	0.9774	0.9480	0.9730	0.7505
0.0241	42.0	11760	0.0701	0.8763	0.9679	0.9782	0.9801	0.9557	0.9764	0.7762
0.0206	43.0	12040	0.0782	0.8666	0.9670	0.9760	0.9777	0.9563	0.9740	0.7593
0.023	44.0	12320	0.0809	0.8656	0.9654	0.9758	0.9778	0.9530	0.9739	0.7573
0.0223	45.0	12600	0.0805	0.8688	0.9660	0.9765	0.9785	0.9535	0.9746	0.7630
0.0224	46.0	12880	0.0748	0.8719	0.9682	0.9772	0.9789	0.9576	0.9753	0.7685
0.0233	47.0	13160	0.0796	0.8697	0.9669	0.9767	0.9786	0.9552	0.9748	0.7645
0.019	48.0	13440	0.0772	0.8729	0.9681	0.9774	0.9792	0.9569	0.9756	0.7703
0.0215	49.0	13720	0.0783	0.8720	0.9682	0.9772	0.9789	0.9575	0.9753	0.7686
0.0186	50.0	14000	0.0785	0.8728	0.9695	0.9773	0.9788	0.9603	0.9755	0.7702

Framework versions

• Transformers 4.38.0.dev0
• Pytorch 2.0.0+cu117
• Datasets 2.14.4
• Tokenizers 0.15.1