segformer-b0-finetuned-batch3-19May

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

• Loss: 0.0057
• Mean Iou: 0.7819
• Mean Accuracy: 0.8573
• Overall Accuracy: 0.9984
• Accuracy Abnormality: 0.7153
• Iou Abnormality: 0.5654

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
0.005	0.9091	10	0.0059	0.7411	0.8270	0.9980	0.6550	0.4843
0.0038	1.8182	20	0.0061	0.7456	0.8639	0.9978	0.7293	0.4935
0.004	2.7273	30	0.0053	0.7557	0.8153	0.9983	0.6312	0.5132
0.0041	3.6364	40	0.0053	0.7559	0.8268	0.9982	0.6544	0.5137
0.0051	4.5455	50	0.0051	0.7659	0.8514	0.9982	0.7037	0.5335
0.0022	5.4545	60	0.0049	0.7669	0.8381	0.9983	0.6770	0.5356
0.0023	6.3636	70	0.0052	0.7667	0.8571	0.9982	0.7153	0.5353
0.0016	7.2727	80	0.0052	0.7679	0.8263	0.9984	0.6533	0.5375
0.0025	8.1818	90	0.0054	0.7687	0.8682	0.9982	0.7374	0.5392
0.0045	9.0909	100	0.0060	0.7622	0.8849	0.9980	0.7712	0.5264
0.0048	10.0	110	0.0054	0.7609	0.8045	0.9984	0.6094	0.5235
0.0035	10.9091	120	0.0054	0.7626	0.8587	0.9981	0.7185	0.5271
0.0033	11.8182	130	0.0051	0.7700	0.8375	0.9983	0.6758	0.5417
0.0022	12.7273	140	0.0052	0.7691	0.8476	0.9983	0.6961	0.5400
0.0039	13.6364	150	0.0055	0.7639	0.8710	0.9981	0.7432	0.5296
0.0034	14.5455	160	0.0051	0.7740	0.8457	0.9984	0.6922	0.5497
0.0039	15.4545	170	0.0053	0.7718	0.8653	0.9982	0.7316	0.5453
0.0024	16.3636	180	0.0053	0.7733	0.8620	0.9983	0.7248	0.5484
0.0023	17.2727	190	0.0051	0.7756	0.8424	0.9984	0.6855	0.5527
0.0022	18.1818	200	0.0053	0.7745	0.8748	0.9982	0.7507	0.5507
0.0041	19.0909	210	0.0051	0.7755	0.8470	0.9984	0.6947	0.5527
0.0025	20.0	220	0.0052	0.7791	0.8638	0.9983	0.7284	0.5599
0.0029	20.9091	230	0.0052	0.7760	0.8554	0.9983	0.7117	0.5537
0.0007	21.8182	240	0.0052	0.7760	0.8506	0.9984	0.7020	0.5537
0.0026	22.7273	250	0.0052	0.7782	0.8627	0.9983	0.7263	0.5581
0.0026	23.6364	260	0.0051	0.7791	0.8471	0.9984	0.6950	0.5597
0.0015	24.5455	270	0.0053	0.7754	0.8665	0.9983	0.7339	0.5525
0.0044	25.4545	280	0.0053	0.7773	0.8562	0.9983	0.7133	0.5563
0.0023	26.3636	290	0.0053	0.7790	0.8606	0.9984	0.7220	0.5597
0.0021	27.2727	300	0.0055	0.7769	0.8735	0.9983	0.7480	0.5556
0.0024	28.1818	310	0.0051	0.7809	0.8514	0.9984	0.7035	0.5633
0.0017	29.0909	320	0.0055	0.7786	0.8643	0.9983	0.7294	0.5589
0.003	30.0	330	0.0053	0.7777	0.8528	0.9984	0.7063	0.5570
0.0012	30.9091	340	0.0052	0.7760	0.8360	0.9984	0.6727	0.5535
0.003	31.8182	350	0.0054	0.7813	0.8648	0.9984	0.7305	0.5642
0.0043	32.7273	360	0.0052	0.7813	0.8465	0.9984	0.6937	0.5642
0.0032	33.6364	370	0.0052	0.7835	0.8587	0.9984	0.7181	0.5685
0.0025	34.5455	380	0.0053	0.7837	0.8637	0.9984	0.7281	0.5691
0.0017	35.4545	390	0.0051	0.7825	0.8380	0.9985	0.6766	0.5666
0.0024	36.3636	400	0.0055	0.7788	0.8704	0.9983	0.7418	0.5594
0.0026	37.2727	410	0.0053	0.7813	0.8533	0.9984	0.7073	0.5641
0.0023	38.1818	420	0.0051	0.7845	0.8646	0.9984	0.7300	0.5706
0.0023	39.0909	430	0.0053	0.7805	0.8648	0.9984	0.7304	0.5627
0.0021	40.0	440	0.0053	0.7823	0.8541	0.9984	0.7089	0.5662
0.0027	40.9091	450	0.0053	0.7834	0.8583	0.9984	0.7174	0.5683
0.004	41.8182	460	0.0052	0.7854	0.8545	0.9985	0.7096	0.5724
0.003	42.7273	470	0.0053	0.7826	0.8466	0.9985	0.6939	0.5668
0.0035	43.6364	480	0.0054	0.7815	0.8637	0.9984	0.7282	0.5646
0.0033	44.5455	490	0.0053	0.7802	0.8560	0.9984	0.7127	0.5620
0.0027	45.4545	500	0.0051	0.7828	0.8489	0.9985	0.6985	0.5672
0.0032	46.3636	510	0.0053	0.7836	0.8605	0.9984	0.7218	0.5687
0.0034	47.2727	520	0.0054	0.7830	0.8521	0.9984	0.7049	0.5675
0.0017	48.1818	530	0.0054	0.7833	0.8595	0.9984	0.7198	0.5681
0.003	49.0909	540	0.0054	0.7809	0.8509	0.9984	0.7024	0.5633
0.0013	50.0	550	0.0053	0.7841	0.8533	0.9985	0.7073	0.5697
0.0026	50.9091	560	0.0054	0.7828	0.8589	0.9984	0.7186	0.5671
0.0013	51.8182	570	0.0054	0.7831	0.8552	0.9984	0.7111	0.5677
0.0019	52.7273	580	0.0055	0.7808	0.8645	0.9984	0.7298	0.5632
0.0024	53.6364	590	0.0054	0.7828	0.8550	0.9984	0.7107	0.5671
0.0024	54.5455	600	0.0054	0.7837	0.8593	0.9984	0.7193	0.5690
0.0025	55.4545	610	0.0055	0.7818	0.8566	0.9984	0.7140	0.5653
0.0018	56.3636	620	0.0054	0.7846	0.8509	0.9985	0.7025	0.5707
0.0027	57.2727	630	0.0054	0.7830	0.8571	0.9984	0.7149	0.5675
0.0017	58.1818	640	0.0054	0.7833	0.8575	0.9984	0.7158	0.5682
0.0038	59.0909	650	0.0054	0.7855	0.8585	0.9984	0.7178	0.5725
0.0018	60.0	660	0.0058	0.7780	0.8628	0.9983	0.7266	0.5576
0.0023	60.9091	670	0.0056	0.7809	0.8534	0.9984	0.7075	0.5634
0.002	61.8182	680	0.0055	0.7841	0.8549	0.9984	0.7105	0.5698
0.0038	62.7273	690	0.0055	0.7822	0.8562	0.9984	0.7132	0.5661
0.0024	63.6364	700	0.0055	0.7813	0.8556	0.9984	0.7120	0.5642
0.0027	64.5455	710	0.0055	0.7828	0.8597	0.9984	0.7202	0.5672
0.0019	65.4545	720	0.0056	0.7813	0.8562	0.9984	0.7131	0.5642
0.0018	66.3636	730	0.0056	0.7818	0.8595	0.9984	0.7199	0.5653
0.0034	67.2727	740	0.0055	0.7825	0.8560	0.9984	0.7127	0.5666
0.0021	68.1818	750	0.0056	0.7818	0.8540	0.9984	0.7087	0.5652
0.0014	69.0909	760	0.0056	0.7818	0.8542	0.9984	0.7091	0.5652
0.002	70.0	770	0.0056	0.7819	0.8600	0.9984	0.7207	0.5654
0.0009	70.9091	780	0.0057	0.7814	0.8586	0.9984	0.7181	0.5645
0.0014	71.8182	790	0.0054	0.7836	0.8604	0.9984	0.7216	0.5688
0.0014	72.7273	800	0.0057	0.7799	0.8571	0.9984	0.7150	0.5615
0.0036	73.6364	810	0.0058	0.7796	0.8603	0.9984	0.7214	0.5607
0.0024	74.5455	820	0.0058	0.7787	0.8648	0.9983	0.7304	0.5590
0.0036	75.4545	830	0.0058	0.7786	0.8580	0.9984	0.7168	0.5589
0.0016	76.3636	840	0.0057	0.7811	0.8626	0.9984	0.7260	0.5639
0.0023	77.2727	850	0.0057	0.7810	0.8586	0.9984	0.7181	0.5637
0.0018	78.1818	860	0.0057	0.7808	0.8576	0.9984	0.7161	0.5633
0.0019	79.0909	870	0.0058	0.7797	0.8637	0.9983	0.7283	0.5610
0.0022	80.0	880	0.0057	0.7817	0.8526	0.9984	0.7058	0.5650
0.0023	80.9091	890	0.0059	0.7796	0.8617	0.9984	0.7243	0.5608
0.0019	81.8182	900	0.0058	0.7803	0.8572	0.9984	0.7152	0.5622
0.003	82.7273	910	0.0058	0.7802	0.8557	0.9984	0.7121	0.5619
0.0024	83.6364	920	0.0058	0.7809	0.8611	0.9984	0.7230	0.5634
0.0014	84.5455	930	0.0058	0.7817	0.8581	0.9984	0.7169	0.5650
0.0017	85.4545	940	0.0058	0.7815	0.8587	0.9984	0.7181	0.5645
0.0031	86.3636	950	0.0058	0.7804	0.8589	0.9984	0.7187	0.5624
0.003	87.2727	960	0.0058	0.7809	0.8568	0.9984	0.7143	0.5634
0.0007	88.1818	970	0.0058	0.7806	0.8594	0.9984	0.7195	0.5629
0.0024	89.0909	980	0.0057	0.7821	0.8577	0.9984	0.7162	0.5657
0.0039	90.0	990	0.0057	0.7826	0.8596	0.9984	0.7199	0.5669
0.0016	90.9091	1000	0.0057	0.7826	0.8578	0.9984	0.7163	0.5669
0.0024	91.8182	1010	0.0057	0.7822	0.8574	0.9984	0.7156	0.5659
0.0027	92.7273	1020	0.0057	0.7817	0.8593	0.9984	0.7195	0.5651
0.0018	93.6364	1030	0.0057	0.7821	0.8591	0.9984	0.7190	0.5658
0.0012	94.5455	1040	0.0057	0.7821	0.8581	0.9984	0.7169	0.5659
0.0017	95.4545	1050	0.0057	0.7816	0.8587	0.9984	0.7181	0.5648
0.0022	96.3636	1060	0.0057	0.7815	0.8582	0.9984	0.7172	0.5647
0.0019	97.2727	1070	0.0058	0.7818	0.8580	0.9984	0.7168	0.5652
0.0041	98.1818	1080	0.0057	0.7816	0.8575	0.9984	0.7157	0.5648
0.0034	99.0909	1090	0.0057	0.7815	0.8566	0.9984	0.7139	0.5646
0.0016	100.0	1100	0.0057	0.7819	0.8573	0.9984	0.7153	0.5654

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1