lpcv_seg

This model is a fine-tuned version of google/deeplabv3_mobilenet_v2_1.0_513 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7747
• Mean Iou: 0.3647
• Mean Accuracy: 0.4742
• Overall Accuracy: 0.7441
• Per Category Iou: [0.7508396000072782, 0.461963906773346, 0.41562431632163865, 0.2643890336606752, 0.20882280410355394, 0.21001420486640948, 0.45776923048905305, 0.6263265430221951, 0.5990132199881534, nan, 0.0, 0.4389678627683, 0.30687238462719907, 0.0]
• Per Category Accuracy: [0.8794950417196551, 0.5122587212045141, 0.47963636761360323, 0.28307093261894156, 0.22726847453969443, 0.817351469679542, 0.5476940642254209, 0.672940072704085, 0.8955934160757268, nan, 0.0, 0.44608425759467085, 0.40330970104326763, 0.0]

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
1.0299	3.12	400	1.1445	0.1503	0.2293	0.6846	[0.7016834423401825, 0.0, 0.28392253175773663, 0.09630649211770352, 0.16489618177561766, 0.05923195228949304, 0.0, 0.47146079872020313, 0.0, nan, 0.0, 0.002537363927640351, 0.17407988046100897, 0.0]	[0.891422563505799, 0.0, 0.7160794493379222, 0.101650104427665, 0.20155241518296468, 0.08566222638071554, 0.0, 0.7801995050268263, 0.0, nan, 0.0, 0.002538359085235112, 0.20133152963007558, 0.0]
1.2946	6.25	800	0.9463	0.1514	0.2217	0.6990	[0.7200410800543089, 0.025577984693804623, 0.272186783532416, 0.03900872477465889, 0.1420562982408946, 0.13672818472813886, 0.0, 0.3747836709762802, 1.4597782434650593e-05, nan, 0.0, 0.0, 0.2576138826730331, 0.0]	[0.9197800777719178, 0.025581056237151574, 0.3936555737035525, 0.03917595636589459, 0.1525480793198657, 0.16772954472705784, 0.0, 0.8514708072975876, 1.4597782434650593e-05, nan, 0.0, 0.0, 0.33275562889985905, 0.0]
0.8869	9.38	1200	0.9005	0.2553	0.3463	0.7230	[0.7311911080696011, 0.1716353741804461, 0.32989770226558307, 0.2484733739307282, 0.20678309581325105, 0.16925674004774066, 0.026560396219635213, 0.43309262889720945, 0.4791539954039438, nan, 0.0, 0.1996331281686418, 0.3234492318785704, 0.0]	[0.8817514590505282, 0.1734977169471996, 0.5536498133348888, 0.26666168042981364, 0.2199222972903758, 0.30495278898499545, 0.026735171458080034, 0.8794234634009936, 0.5647817144933271, nan, 0.0, 0.19998287851911137, 0.43016977002783463, 0.0]
0.8056	12.5	1600	0.7305	0.3106	0.3897	0.7595	[0.7538928589848243, 0.25918801874572633, 0.4203485338620474, 0.2917137053557758, 0.22289503658523782, 0.1500749815626878, 0.08861529960760275, 0.624086456268012, 0.48451939021185103, nan, 0.0, 0.3968018568653773, 0.34541558718052773, 0.0]	[0.9269385933729979, 0.2666068145623601, 0.5690834086799277, 0.323866282726429, 0.23199974868520706, 0.27844356662715314, 0.08945714746397179, 0.7853752936779852, 0.7445501612243971, nan, 0.0, 0.4213536720599331, 0.42861443009823574, 0.0]
0.7534	15.62	2000	0.8091	0.2762	0.3774	0.7408	[0.7450525081446471, 0.08805507548598117, 0.39296675038083556, 0.17888143033371248, 0.23466648108570068, 0.16079006682931019, 0.03714210117676656, 0.6219865494576018, 0.5133743295405404, nan, 0.0, 0.25318448016278294, 0.3644475822818699, 0.0]	[0.8925789949539187, 0.08813412970942185, 0.6256571559820335, 0.18979883934004887, 0.24639981315291482, 0.4060874245051725, 0.03731593695118816, 0.8003825980603533, 0.8838065177476595, nan, 0.0, 0.25812424222515545, 0.47821461707899227, 0.0]
0.909	18.75	2400	0.7351	0.3258	0.4109	0.7698	[0.7635658546626002, 0.40487164496967293, 0.3833268397798514, 0.2752089531995694, 0.2867291055853458, 0.019354003421208514, 0.16890635042927862, 0.5748136637050552, 0.6549805047839075, nan, 0.0, 0.41018196642738697, 0.29410209406700305, 0.0]	[0.936836241285118, 0.4435412622122596, 0.6115797993350055, 0.29433416172859234, 0.3084226650431367, 0.01980599690692518, 0.1729223045848531, 0.8637196946532967, 0.9040471540812156, nan, 0.0, 0.44469064868513, 0.3419148529297193, 0.0]
0.6622	21.88	2800	0.6963	0.3398	0.3986	0.7827	[0.7663761109958838, 0.1332027535954636, 0.4428707076945945, 0.35775923195424675, 0.3733786220955952, 0.24949901778334058, 0.07535404639899677, 0.6174965473057359, 0.5443824880761584, nan, 0.0, 0.4962856002887922, 0.3608184550398603, 0.0]	[0.9493652245312661, 0.13338632341294235, 0.6300075103540804, 0.4173944872618647, 0.41213577281689057, 0.31108969330720987, 0.0759392172499597, 0.6593100168039995, 0.5955570838172228, nan, 0.0, 0.5200351189445205, 0.4777004199984418, 0.0]
1.1202	25.0	3200	1.6352	0.1684	0.2322	0.7026	[0.6963172586859363, 0.011559652438449069, 0.22669839158942193, 0.05922256175168542, 0.06532726727054458, 0.0023358067869460536, 0.0, 0.509361415684211, 0.38727970470408707, nan, 0.0, 0.09125458640751972, 0.13993367687629527, 0.0]	[0.9487412199772098, 0.011559652438449069, 0.24983867321938985, 0.05932148648786475, 0.06540672362680026, 0.003050864548414594, 0.0, 0.7020197466594538, 0.7380590139684559, nan, 0.0, 0.09130527401337467, 0.14901587211649467, 0.0]
0.6529	28.12	3600	0.7747	0.3647	0.4742	0.7441	[0.7508396000072782, 0.461963906773346, 0.41562431632163865, 0.2643890336606752, 0.20882280410355394, 0.21001420486640948, 0.45776923048905305, 0.6263265430221951, 0.5990132199881534, nan, 0.0, 0.4389678627683, 0.30687238462719907, 0.0]	[0.8794950417196551, 0.5122587212045141, 0.47963636761360323, 0.28307093261894156, 0.22726847453969443, 0.817351469679542, 0.5476940642254209, 0.672940072704085, 0.8955934160757268, nan, 0.0, 0.44608425759467085, 0.40330970104326763, 0.0]

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3