segformer-finetuned-biofilm2_train

This model is a fine-tuned version of nvidia/mit-b0 on the heroza/biofilm2_train dataset. It achieves the following results on the evaluation set:

Loss: 0.0761
Mean Iou: 0.8665
Mean Accuracy: 0.9765
Overall Accuracy: 0.9745
Accuracy Background: 0.9741
Accuracy Biofilm: 0.9789
Iou Background: 0.9722
Iou Biofilm: 0.7608

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
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Biofilm	Iou Background	Iou Biofilm
0.1611	1.0	298	0.1220	0.8393	0.9547	0.9687	0.9714	0.9379	0.9660	0.7126
0.07	2.0	596	0.0682	0.8795	0.9359	0.9795	0.9881	0.8837	0.9779	0.7811
0.0542	3.0	894	0.0564	0.8862	0.9735	0.9793	0.9805	0.9666	0.9775	0.7948
0.0508	4.0	1192	0.0517	0.8888	0.9728	0.9799	0.9814	0.9643	0.9782	0.7993
0.0491	5.0	1490	0.0479	0.8999	0.9727	0.9824	0.9843	0.9611	0.9809	0.8190
0.0496	6.0	1788	0.0665	0.8733	0.9728	0.9764	0.9770	0.9686	0.9743	0.7724
0.047	7.0	2086	0.0475	0.8936	0.9744	0.9810	0.9823	0.9664	0.9793	0.8079
0.0403	8.0	2384	0.0513	0.8897	0.9699	0.9803	0.9823	0.9575	0.9786	0.8008
0.0336	9.0	2682	0.0597	0.8736	0.9790	0.9761	0.9756	0.9824	0.9740	0.7732
0.036	10.0	2980	0.0602	0.8789	0.9781	0.9774	0.9773	0.9789	0.9755	0.7824
0.0335	11.0	3278	0.0519	0.8849	0.9670	0.9793	0.9818	0.9522	0.9775	0.7923
0.0364	12.0	3576	0.0684	0.8718	0.9810	0.9756	0.9745	0.9874	0.9734	0.7702
0.0423	13.0	3874	0.0637	0.8767	0.9742	0.9771	0.9777	0.9707	0.9751	0.7783
0.0354	14.0	4172	0.0618	0.8773	0.9692	0.9775	0.9791	0.9593	0.9755	0.7790
0.0335	15.0	4470	0.0547	0.8788	0.9686	0.9778	0.9797	0.9574	0.9759	0.7816
0.0318	16.0	4768	0.0567	0.8841	0.9744	0.9788	0.9797	0.9691	0.9770	0.7913
0.0296	17.0	5066	0.0653	0.8678	0.9741	0.9749	0.9751	0.9732	0.9727	0.7628
0.0291	18.0	5364	0.0591	0.8757	0.9718	0.9770	0.9780	0.9657	0.9750	0.7765
0.0311	19.0	5662	0.0716	0.8682	0.9753	0.9750	0.9749	0.9756	0.9728	0.7637
0.0322	20.0	5960	0.0837	0.8506	0.9773	0.9703	0.9690	0.9857	0.9677	0.7335
0.0317	21.0	6258	0.0728	0.8673	0.9749	0.9748	0.9747	0.9751	0.9726	0.7621
0.0318	22.0	6556	0.0571	0.8796	0.9764	0.9777	0.9779	0.9748	0.9757	0.7835
0.0288	23.0	6854	0.0734	0.8689	0.9798	0.9749	0.9739	0.9858	0.9727	0.7651
0.0271	24.0	7152	0.0763	0.8615	0.9757	0.9733	0.9728	0.9785	0.9709	0.7521
0.0236	25.0	7450	0.0615	0.8789	0.9761	0.9775	0.9778	0.9744	0.9756	0.7823
0.025	26.0	7748	0.0694	0.8684	0.9768	0.9750	0.9746	0.9790	0.9727	0.7640
0.0269	27.0	8046	0.0672	0.8700	0.9688	0.9757	0.9771	0.9605	0.9736	0.7664
0.0286	28.0	8344	0.0717	0.8695	0.9761	0.9753	0.9751	0.9771	0.9731	0.7659
0.0255	29.0	8642	0.0680	0.8696	0.9757	0.9753	0.9752	0.9761	0.9731	0.7661
0.0255	30.0	8940	0.0701	0.8691	0.9756	0.9752	0.9751	0.9762	0.9730	0.7651
0.0223	31.0	9238	0.0715	0.8687	0.9746	0.9751	0.9752	0.9740	0.9730	0.7644
0.0226	32.0	9536	0.0757	0.8667	0.9770	0.9745	0.9740	0.9799	0.9723	0.7612
0.022	33.0	9834	0.0773	0.8661	0.9766	0.9744	0.9739	0.9793	0.9721	0.7601
0.0217	33.56	10000	0.0761	0.8665	0.9765	0.9745	0.9741	0.9789	0.9722	0.7608

Framework versions

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

heroza
/

segformer-finetuned-biofilm2_train

segformer-finetuned-biofilm2_train

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for heroza/segformer-finetuned-biofilm2_train

Evaluation results