segformer-b0-finetuned-batch1w5-15Dec

This model is a fine-tuned version of PushkarA07/segformer-b0-finetuned-batch2w5-15Dec on the PushkarA07/batch1-tiles_W5 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0038
• Mean Iou: 0.9143
• Mean Accuracy: 0.9529
• Overall Accuracy: 0.9985
• Accuracy Abnormality: 0.9066
• Iou Abnormality: 0.8302

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: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Abnormality	Iou Abnormality
0.0085	0.8333	10	0.0101	0.8170	0.8872	0.9963	0.7762	0.6376
0.0097	1.6667	20	0.0078	0.8448	0.8888	0.9971	0.7787	0.6926
0.0102	2.5	30	0.0071	0.8563	0.9028	0.9973	0.8068	0.7153
0.0062	3.3333	40	0.0066	0.8618	0.9005	0.9975	0.8018	0.7262
0.006	4.1667	50	0.0062	0.8693	0.9147	0.9976	0.8304	0.7410
0.0086	5.0	60	0.0060	0.8726	0.9194	0.9976	0.8398	0.7475
0.0056	5.8333	70	0.0056	0.8773	0.9128	0.9978	0.8264	0.7568
0.0044	6.6667	80	0.0056	0.8789	0.9270	0.9978	0.8550	0.7601
0.0045	7.5	90	0.0054	0.8818	0.9239	0.9978	0.8487	0.7658
0.0087	8.3333	100	0.0053	0.8850	0.9347	0.9979	0.8705	0.7721
0.0045	9.1667	110	0.0052	0.8835	0.9150	0.9979	0.8306	0.7692
0.0051	10.0	120	0.0051	0.8888	0.9360	0.9980	0.8730	0.7798
0.0045	10.8333	130	0.0049	0.8904	0.9270	0.9980	0.8547	0.7827
0.0068	11.6667	140	0.0048	0.8904	0.9290	0.9980	0.8589	0.7828
0.0029	12.5	150	0.0048	0.8924	0.9394	0.9980	0.8799	0.7867
0.0051	13.3333	160	0.0048	0.8943	0.9361	0.9981	0.8731	0.7906
0.0038	14.1667	170	0.0047	0.8953	0.9394	0.9981	0.8796	0.7926
0.0075	15.0	180	0.0047	0.8967	0.9416	0.9981	0.8841	0.7952
0.0054	15.8333	190	0.0047	0.8954	0.9315	0.9981	0.8637	0.7928
0.0031	16.6667	200	0.0046	0.8973	0.9373	0.9981	0.8755	0.7965
0.0049	17.5	210	0.0046	0.8970	0.9300	0.9982	0.8606	0.7958
0.0049	18.3333	220	0.0045	0.9001	0.9430	0.9982	0.8870	0.8019
0.0038	19.1667	230	0.0045	0.9002	0.9485	0.9982	0.8979	0.8022
0.0074	20.0	240	0.0045	0.9009	0.9424	0.9982	0.8856	0.8036
0.0048	20.8333	250	0.0045	0.9008	0.9473	0.9982	0.8955	0.8034
0.0058	21.6667	260	0.0045	0.9011	0.9464	0.9982	0.8938	0.8039
0.0051	22.5	270	0.0044	0.9029	0.9421	0.9983	0.8850	0.8075
0.0062	23.3333	280	0.0043	0.9026	0.9379	0.9983	0.8766	0.8070
0.0051	24.1667	290	0.0044	0.9027	0.9440	0.9982	0.8888	0.8071
0.0026	25.0	300	0.0043	0.9043	0.9443	0.9983	0.8894	0.8103
0.007	25.8333	310	0.0043	0.9042	0.9498	0.9983	0.9004	0.8102
0.0041	26.6667	320	0.0043	0.9046	0.9454	0.9983	0.8916	0.8110
0.0045	27.5	330	0.0043	0.9048	0.9427	0.9983	0.8862	0.8114
0.0041	28.3333	340	0.0043	0.9055	0.9490	0.9983	0.8988	0.8128
0.0024	29.1667	350	0.0042	0.9064	0.9485	0.9983	0.8979	0.8145
0.0035	30.0	360	0.0042	0.9061	0.9424	0.9983	0.8856	0.8139
0.003	30.8333	370	0.0042	0.9063	0.9523	0.9983	0.9056	0.8142
0.0054	31.6667	380	0.0042	0.9074	0.9447	0.9983	0.8902	0.8165
0.0054	32.5	390	0.0042	0.9064	0.9480	0.9983	0.8969	0.8144
0.0041	33.3333	400	0.0042	0.9053	0.9471	0.9983	0.8951	0.8123
0.0059	34.1667	410	0.0041	0.9075	0.9439	0.9983	0.8886	0.8166
0.0027	35.0	420	0.0042	0.9066	0.9452	0.9983	0.8912	0.8149
0.0052	35.8333	430	0.0042	0.9074	0.9474	0.9983	0.8956	0.8165
0.0042	36.6667	440	0.0041	0.9070	0.9457	0.9983	0.8922	0.8156
0.0037	37.5	450	0.0041	0.9076	0.9457	0.9983	0.8922	0.8170
0.0033	38.3333	460	0.0041	0.9084	0.9481	0.9984	0.8970	0.8185
0.0031	39.1667	470	0.0041	0.9085	0.9471	0.9984	0.8949	0.8187
0.0037	40.0	480	0.0042	0.9071	0.9543	0.9983	0.9096	0.8159
0.0048	40.8333	490	0.0041	0.9088	0.9500	0.9984	0.9008	0.8192
0.0042	41.6667	500	0.0041	0.9086	0.9474	0.9984	0.8957	0.8188
0.0024	42.5	510	0.0040	0.9095	0.9470	0.9984	0.8948	0.8206
0.0047	43.3333	520	0.0040	0.9091	0.9511	0.9984	0.9031	0.8198
0.0054	44.1667	530	0.0041	0.9080	0.9438	0.9984	0.8884	0.8176
0.0053	45.0	540	0.0041	0.9084	0.9460	0.9984	0.8928	0.8185
0.0033	45.8333	550	0.0041	0.9094	0.9515	0.9984	0.9038	0.8205
0.0044	46.6667	560	0.0042	0.9076	0.9580	0.9983	0.9171	0.8169
0.0021	47.5	570	0.0040	0.9095	0.9501	0.9984	0.9010	0.8206
0.0035	48.3333	580	0.0040	0.9092	0.9529	0.9983	0.9067	0.8200
0.0038	49.1667	590	0.0040	0.9109	0.9505	0.9984	0.9019	0.8234
0.004	50.0	600	0.0041	0.9103	0.9563	0.9984	0.9134	0.8223
0.0044	50.8333	610	0.0040	0.9106	0.9464	0.9984	0.8936	0.8229
0.0026	51.6667	620	0.0040	0.9104	0.9554	0.9984	0.9116	0.8225
0.0062	52.5	630	0.0040	0.9114	0.9510	0.9984	0.9027	0.8244
0.0023	53.3333	640	0.0040	0.9114	0.9470	0.9984	0.8948	0.8244
0.0029	54.1667	650	0.0040	0.9113	0.9508	0.9984	0.9024	0.8242
0.0042	55.0	660	0.0040	0.9116	0.9528	0.9984	0.9064	0.8248
0.0044	55.8333	670	0.0039	0.9121	0.9519	0.9984	0.9045	0.8258
0.0016	56.6667	680	0.0040	0.9116	0.9514	0.9984	0.9035	0.8248
0.0044	57.5	690	0.0039	0.9116	0.9533	0.9984	0.9075	0.8248
0.0031	58.3333	700	0.0039	0.9118	0.9497	0.9984	0.9002	0.8253
0.0038	59.1667	710	0.0039	0.9119	0.9509	0.9984	0.9025	0.8254
0.0042	60.0	720	0.0040	0.9117	0.9535	0.9984	0.9078	0.8250
0.0045	60.8333	730	0.0039	0.9119	0.9512	0.9984	0.9032	0.8254
0.0039	61.6667	740	0.0039	0.9122	0.9507	0.9984	0.9022	0.8260
0.0022	62.5	750	0.0040	0.9117	0.9562	0.9984	0.9134	0.8250
0.0039	63.3333	760	0.0039	0.9126	0.9502	0.9984	0.9012	0.8268
0.0031	64.1667	770	0.0039	0.9115	0.9507	0.9984	0.9021	0.8245
0.0037	65.0	780	0.0040	0.9118	0.9533	0.9984	0.9074	0.8252
0.0046	65.8333	790	0.0039	0.9123	0.9489	0.9984	0.8986	0.8261
0.0026	66.6667	800	0.0039	0.9127	0.9532	0.9984	0.9073	0.8269
0.0039	67.5	810	0.0039	0.9121	0.9469	0.9984	0.8946	0.8258
0.0025	68.3333	820	0.0039	0.9121	0.9541	0.9984	0.9091	0.8259
0.0044	69.1667	830	0.0039	0.9127	0.9531	0.9984	0.9069	0.8270
0.0049	70.0	840	0.0039	0.9123	0.9546	0.9984	0.9100	0.8263
0.0038	70.8333	850	0.0039	0.9129	0.9527	0.9984	0.9062	0.8273
0.0053	71.6667	860	0.0039	0.9131	0.9534	0.9984	0.9077	0.8278
0.0049	72.5	870	0.0039	0.9128	0.9538	0.9984	0.9083	0.8272
0.003	73.3333	880	0.0039	0.9130	0.9503	0.9984	0.9012	0.8276
0.0025	74.1667	890	0.0039	0.9124	0.9583	0.9984	0.9176	0.8264
0.0035	75.0	900	0.0039	0.9131	0.9509	0.9984	0.9026	0.8278
0.0028	75.8333	910	0.0039	0.9128	0.9559	0.9984	0.9127	0.8272
0.0027	76.6667	920	0.0039	0.9128	0.9528	0.9984	0.9064	0.8272
0.0033	77.5	930	0.0039	0.9133	0.9539	0.9984	0.9086	0.8282
0.0033	78.3333	940	0.0039	0.9135	0.9529	0.9984	0.9065	0.8285
0.0056	79.1667	950	0.0039	0.9134	0.9529	0.9984	0.9067	0.8283
0.0063	80.0	960	0.0039	0.9132	0.9495	0.9984	0.8996	0.8279
0.0057	80.8333	970	0.0039	0.9130	0.9563	0.9984	0.9134	0.8276
0.0021	81.6667	980	0.0039	0.9136	0.9511	0.9985	0.9029	0.8287
0.0043	82.5	990	0.0039	0.9130	0.9563	0.9984	0.9136	0.8275
0.0048	83.3333	1000	0.0039	0.9137	0.9525	0.9984	0.9057	0.8289
0.0043	84.1667	1010	0.0039	0.9133	0.9514	0.9984	0.9035	0.8282
0.0037	85.0	1020	0.0039	0.9137	0.9542	0.9984	0.9092	0.8289
0.0042	85.8333	1030	0.0038	0.9137	0.9501	0.9985	0.9010	0.8290
0.0039	86.6667	1040	0.0039	0.9138	0.9550	0.9984	0.9108	0.8292
0.0027	87.5	1050	0.0038	0.9139	0.9517	0.9985	0.9041	0.8294
0.0034	88.3333	1060	0.0038	0.9138	0.9526	0.9985	0.9060	0.8291
0.0037	89.1667	1070	0.0039	0.9137	0.9550	0.9984	0.9109	0.8289
0.0029	90.0	1080	0.0038	0.9141	0.9509	0.9985	0.9025	0.8297
0.0038	90.8333	1090	0.0038	0.9139	0.9535	0.9985	0.9078	0.8294
0.0066	91.6667	1100	0.0039	0.9138	0.9545	0.9984	0.9097	0.8292
0.0037	92.5	1110	0.0039	0.9138	0.9547	0.9984	0.9102	0.8292
0.0053	93.3333	1120	0.0038	0.9143	0.9518	0.9985	0.9044	0.8301
0.0039	94.1667	1130	0.0038	0.9141	0.9523	0.9985	0.9054	0.8298
0.0049	95.0	1140	0.0038	0.9143	0.9520	0.9985	0.9047	0.8302
0.004	95.8333	1150	0.0038	0.9142	0.9535	0.9985	0.9077	0.8300
0.0033	96.6667	1160	0.0038	0.9142	0.9536	0.9985	0.9080	0.8300
0.0037	97.5	1170	0.0038	0.9141	0.9536	0.9985	0.9079	0.8298
0.0036	98.3333	1180	0.0038	0.9140	0.9536	0.9985	0.9080	0.8295
0.0042	99.1667	1190	0.0038	0.9141	0.9538	0.9985	0.9085	0.8298
0.0035	100.0	1200	0.0038	0.9143	0.9529	0.9985	0.9066	0.8302

Framework versions

• Transformers 4.46.3
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.20.3