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PushkarA07/
segformer-b0-finetuned-batch3-26May-2

Name: segformer-b0-finetuned-batch3-26May-2
Rating: 2.5 (8 reviews)

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PushkarA07/segformer-b0-finetuned-batch2w5-15Dec

finetune

other

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segformer-b0-finetuned-batch3-26May-2

This model is a fine-tuned version of PushkarA07/segformer-b0-finetuned-batch2w5-15Dec on the PushkarA07/batch3-tiles_third dataset. It achieves the following results on the evaluation set:

Loss: 0.0007
Mean Iou: 0.9173
Mean Accuracy: 0.9515
Overall Accuracy: 0.9997
Accuracy Abnormality: 0.9030
Iou Abnormality: 0.8348

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: 42
optimizer: Use OptimizerNames.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
0.0012	0.7143	10	0.0017	0.8437	0.8917	0.9994	0.7835	0.6879
0.0012	1.4286	20	0.0013	0.8539	0.8779	0.9995	0.7559	0.7082
0.001	2.1429	30	0.0012	0.8684	0.8944	0.9996	0.7889	0.7372
0.0006	2.8571	40	0.0011	0.8746	0.8991	0.9996	0.7983	0.7496
0.001	3.5714	50	0.0010	0.8839	0.9185	0.9996	0.8371	0.7681
0.0012	4.2857	60	0.0010	0.8867	0.9189	0.9996	0.8380	0.7737
0.0022	5.0	70	0.0010	0.8901	0.9211	0.9996	0.8423	0.7806
0.0017	5.7143	80	0.0009	0.8913	0.9254	0.9996	0.8510	0.7829
0.0016	6.4286	90	0.0009	0.8921	0.9237	0.9996	0.8475	0.7846
0.001	7.1429	100	0.0009	0.8946	0.9278	0.9996	0.8557	0.7895
0.0012	7.8571	110	0.0009	0.8935	0.9226	0.9996	0.8453	0.7873
0.0011	8.5714	120	0.0009	0.8963	0.9314	0.9996	0.8629	0.7929
0.001	9.2857	130	0.0009	0.8980	0.9325	0.9996	0.8652	0.7963
0.0006	10.0	140	0.0009	0.8978	0.9303	0.9996	0.8608	0.7959
0.001	10.7143	150	0.0009	0.8996	0.9366	0.9997	0.8732	0.7995
0.001	11.4286	160	0.0009	0.9016	0.9463	0.9997	0.8928	0.8036
0.0004	12.1429	170	0.0009	0.9019	0.9494	0.9997	0.8990	0.8042
0.0002	12.8571	180	0.0009	0.9004	0.9341	0.9997	0.8683	0.8012
0.0011	13.5714	190	0.0009	0.9026	0.9488	0.9997	0.8977	0.8055
0.0005	14.2857	200	0.0008	0.9014	0.9385	0.9997	0.8772	0.8031
0.0007	15.0	210	0.0008	0.9013	0.9354	0.9997	0.8709	0.8028
0.0013	15.7143	220	0.0008	0.9047	0.9445	0.9997	0.8892	0.8098
0.0004	16.4286	230	0.0008	0.9015	0.9334	0.9997	0.8670	0.8034
0.0009	17.1429	240	0.0008	0.9057	0.9500	0.9997	0.9002	0.8117
0.0016	17.8571	250	0.0008	0.9060	0.9451	0.9997	0.8904	0.8124
0.0011	18.5714	260	0.0008	0.9052	0.9432	0.9997	0.8865	0.8107
0.0007	19.2857	270	0.0008	0.9069	0.9476	0.9997	0.8953	0.8141
0.0007	20.0	280	0.0008	0.9073	0.9488	0.9997	0.8977	0.8150
0.001	20.7143	290	0.0008	0.9033	0.9329	0.9997	0.8660	0.8068
0.0006	21.4286	300	0.0008	0.9079	0.9492	0.9997	0.8985	0.8162
0.0009	22.1429	310	0.0008	0.9070	0.9494	0.9997	0.8990	0.8143
0.0007	22.8571	320	0.0008	0.9070	0.9438	0.9997	0.8877	0.8142
0.0006	23.5714	330	0.0008	0.9071	0.9458	0.9997	0.8918	0.8146
0.001	24.2857	340	0.0008	0.9088	0.9455	0.9997	0.8912	0.8179
0.0006	25.0	350	0.0008	0.9105	0.9477	0.9997	0.8955	0.8214
0.0009	25.7143	360	0.0008	0.9090	0.9477	0.9997	0.8955	0.8184
0.001	26.4286	370	0.0008	0.9096	0.9521	0.9997	0.9043	0.8196
0.0012	27.1429	380	0.0008	0.9089	0.9465	0.9997	0.8931	0.8181
0.0006	27.8571	390	0.0008	0.9100	0.9487	0.9997	0.8976	0.8203
0.0006	28.5714	400	0.0008	0.9097	0.9484	0.9997	0.8970	0.8198
0.0004	29.2857	410	0.0008	0.9088	0.9565	0.9997	0.9131	0.8179
0.0013	30.0	420	0.0008	0.9073	0.9413	0.9997	0.8828	0.8150
0.0007	30.7143	430	0.0008	0.9086	0.9441	0.9997	0.8883	0.8176
0.0011	31.4286	440	0.0008	0.9109	0.9575	0.9997	0.9151	0.8221
0.0004	32.1429	450	0.0008	0.9112	0.9525	0.9997	0.9051	0.8227
0.0011	32.8571	460	0.0008	0.9118	0.9469	0.9997	0.8939	0.8239
0.0006	33.5714	470	0.0008	0.9112	0.9559	0.9997	0.9119	0.8228
0.0004	34.2857	480	0.0008	0.9104	0.9535	0.9997	0.9072	0.8210
0.0006	35.0	490	0.0008	0.9107	0.9450	0.9997	0.8902	0.8218
0.0011	35.7143	500	0.0008	0.9128	0.9509	0.9997	0.9019	0.8258
0.0004	36.4286	510	0.0008	0.9118	0.9502	0.9997	0.9005	0.8239
0.0007	37.1429	520	0.0008	0.9135	0.9534	0.9997	0.9070	0.8273
0.0005	37.8571	530	0.0008	0.9106	0.9422	0.9997	0.8845	0.8216
0.0011	38.5714	540	0.0008	0.9125	0.9501	0.9997	0.9004	0.8252
0.0006	39.2857	550	0.0008	0.9130	0.9553	0.9997	0.9107	0.8264
0.001	40.0	560	0.0008	0.9110	0.9454	0.9997	0.8909	0.8224
0.001	40.7143	570	0.0008	0.9135	0.9546	0.9997	0.9094	0.8272
0.0009	41.4286	580	0.0008	0.9131	0.9529	0.9997	0.9060	0.8265
0.0007	42.1429	590	0.0008	0.9112	0.9479	0.9997	0.8959	0.8227
0.0005	42.8571	600	0.0007	0.9131	0.9514	0.9997	0.9029	0.8265
0.0005	43.5714	610	0.0008	0.9110	0.9435	0.9997	0.8871	0.8224
0.0005	44.2857	620	0.0008	0.9126	0.9575	0.9997	0.9152	0.8255
0.0003	45.0	630	0.0007	0.9121	0.9480	0.9997	0.8962	0.8244
0.0003	45.7143	640	0.0008	0.9109	0.9432	0.9997	0.8865	0.8221
0.0006	46.4286	650	0.0007	0.9139	0.9519	0.9997	0.9039	0.8281
0.0003	47.1429	660	0.0008	0.9132	0.9547	0.9997	0.9096	0.8267
0.0012	47.8571	670	0.0008	0.9114	0.9444	0.9997	0.8888	0.8230
0.0008	48.5714	680	0.0007	0.9138	0.9546	0.9997	0.9093	0.8279
0.001	49.2857	690	0.0007	0.9136	0.9512	0.9997	0.9025	0.8275
0.0009	50.0	700	0.0007	0.9127	0.9490	0.9997	0.8982	0.8258
0.0006	50.7143	710	0.0007	0.9143	0.9527	0.9997	0.9055	0.8289
0.0011	51.4286	720	0.0007	0.9127	0.9475	0.9997	0.8951	0.8257
0.0003	52.1429	730	0.0007	0.9138	0.9500	0.9997	0.9002	0.8280
0.0005	52.8571	740	0.0007	0.9141	0.9541	0.9997	0.9083	0.8285
0.0011	53.5714	750	0.0007	0.9146	0.9526	0.9997	0.9052	0.8295
0.0005	54.2857	760	0.0007	0.9139	0.9509	0.9997	0.9019	0.8281
0.0005	55.0	770	0.0007	0.9134	0.9468	0.9997	0.8937	0.8270
0.0009	55.7143	780	0.0007	0.9150	0.9528	0.9997	0.9058	0.8302
0.0011	56.4286	790	0.0007	0.9133	0.9461	0.9997	0.8924	0.8268
0.0015	57.1429	800	0.0007	0.9143	0.9507	0.9997	0.9016	0.8289
0.0009	57.8571	810	0.0007	0.9148	0.9509	0.9997	0.9019	0.8299
0.0006	58.5714	820	0.0007	0.9146	0.9507	0.9997	0.9015	0.8294
0.0003	59.2857	830	0.0007	0.9152	0.9530	0.9997	0.9062	0.8307
0.0006	60.0	840	0.0007	0.9144	0.9487	0.9997	0.8974	0.8292
0.0006	60.7143	850	0.0007	0.9149	0.9529	0.9997	0.9060	0.8300
0.0006	61.4286	860	0.0007	0.9159	0.9556	0.9997	0.9115	0.8320
0.0004	62.1429	870	0.0007	0.9143	0.9499	0.9997	0.8999	0.8288
0.0008	62.8571	880	0.0007	0.9150	0.9537	0.9997	0.9076	0.8303
0.0008	63.5714	890	0.0007	0.9154	0.9493	0.9997	0.8987	0.8311
0.0006	64.2857	900	0.0007	0.9158	0.9572	0.9997	0.9146	0.8319
0.0013	65.0	910	0.0007	0.9150	0.9509	0.9997	0.9020	0.8304
0.0008	65.7143	920	0.0007	0.9148	0.9487	0.9997	0.8974	0.8300
0.0009	66.4286	930	0.0007	0.9164	0.9555	0.9997	0.9111	0.8332
0.0007	67.1429	940	0.0007	0.9167	0.9521	0.9997	0.9043	0.8337
0.0005	67.8571	950	0.0007	0.9163	0.9540	0.9997	0.9082	0.8328
0.0009	68.5714	960	0.0007	0.9157	0.9489	0.9997	0.8979	0.8316
0.001	69.2857	970	0.0007	0.9160	0.9548	0.9997	0.9098	0.8322
0.0006	70.0	980	0.0007	0.9156	0.9492	0.9997	0.8985	0.8315
0.001	70.7143	990	0.0007	0.9160	0.9507	0.9997	0.9015	0.8323
0.0006	71.4286	1000	0.0007	0.9154	0.9484	0.9997	0.8970	0.8310
0.0014	72.1429	1010	0.0007	0.9165	0.9534	0.9997	0.9068	0.8332
0.0008	72.8571	1020	0.0007	0.9165	0.9513	0.9997	0.9028	0.8333
0.0007	73.5714	1030	0.0007	0.9167	0.9530	0.9997	0.9061	0.8338
0.0008	74.2857	1040	0.0007	0.9159	0.9526	0.9997	0.9052	0.8321
0.0006	75.0	1050	0.0007	0.9154	0.9503	0.9997	0.9007	0.8312
0.0007	75.7143	1060	0.0007	0.9165	0.9545	0.9997	0.9091	0.8332
0.0011	76.4286	1070	0.0007	0.9168	0.9543	0.9997	0.9087	0.8338
0.0009	77.1429	1080	0.0007	0.9158	0.9527	0.9997	0.9055	0.8320
0.0005	77.8571	1090	0.0007	0.9168	0.9511	0.9997	0.9023	0.8338
0.0005	78.5714	1100	0.0007	0.9162	0.9502	0.9997	0.9005	0.8328
0.0009	79.2857	1110	0.0007	0.9174	0.9533	0.9997	0.9068	0.8350
0.0004	80.0	1120	0.0007	0.9162	0.9495	0.9997	0.8990	0.8327
0.0002	80.7143	1130	0.0007	0.9165	0.9507	0.9997	0.9014	0.8332
0.0005	81.4286	1140	0.0007	0.9164	0.9499	0.9997	0.8999	0.8332
0.0009	82.1429	1150	0.0007	0.9170	0.9543	0.9997	0.9087	0.8342
0.0009	82.8571	1160	0.0007	0.9165	0.9523	0.9997	0.9048	0.8334
0.0006	83.5714	1170	0.0007	0.9165	0.9519	0.9997	0.9039	0.8332
0.0008	84.2857	1180	0.0007	0.9161	0.9515	0.9997	0.9032	0.8325
0.0006	85.0	1190	0.0007	0.9169	0.9525	0.9997	0.9051	0.8340
0.0005	85.7143	1200	0.0007	0.9167	0.9518	0.9997	0.9037	0.8337
0.0002	86.4286	1210	0.0007	0.9167	0.9519	0.9997	0.9040	0.8337
0.0004	87.1429	1220	0.0007	0.9167	0.9518	0.9997	0.9037	0.8337
0.0009	87.8571	1230	0.0007	0.9169	0.9520	0.9997	0.9042	0.8340
0.0011	88.5714	1240	0.0007	0.9171	0.9526	0.9997	0.9053	0.8345
0.0006	89.2857	1250	0.0007	0.9171	0.9518	0.9997	0.9037	0.8346
0.0007	90.0	1260	0.0007	0.9174	0.9551	0.9997	0.9104	0.8351
0.0005	90.7143	1270	0.0007	0.9168	0.9534	0.9997	0.9069	0.8340
0.0007	91.4286	1280	0.0007	0.9169	0.9519	0.9997	0.9040	0.8341
0.0009	92.1429	1290	0.0007	0.9175	0.9526	0.9997	0.9052	0.8352
0.0009	92.8571	1300	0.0007	0.9177	0.9532	0.9997	0.9066	0.8356
0.0007	93.5714	1310	0.0007	0.9174	0.9525	0.9997	0.9051	0.8351
0.0007	94.2857	1320	0.0007	0.9170	0.9518	0.9997	0.9037	0.8343
0.0015	95.0	1330	0.0007	0.9173	0.9535	0.9997	0.9071	0.8349
0.0005	95.7143	1340	0.0007	0.9176	0.9534	0.9997	0.9069	0.8355
0.0007	96.4286	1350	0.0007	0.9174	0.9525	0.9997	0.9051	0.8351
0.001	97.1429	1360	0.0007	0.9175	0.9527	0.9997	0.9056	0.8353
0.001	97.8571	1370	0.0007	0.9175	0.9526	0.9997	0.9052	0.8354
0.0007	98.5714	1380	0.0007	0.9173	0.9518	0.9997	0.9037	0.8349
0.0006	99.2857	1390	0.0007	0.9175	0.9514	0.9997	0.9029	0.8352
0.0011	100.0	1400	0.0007	0.9173	0.9515	0.9997	0.9030	0.8348

Framework versions

Transformers 4.52.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

PushkarA07/segformer-b0-finetuned-batch3-26May-2

segformer-b0-finetuned-batch3-26May-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

PushkarA07/
segformer-b0-finetuned-batch3-26May-2