segmentation_test

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

  • Loss: 0.1208
  • Mean Iou: 0.8483
  • Mean Accuracy: 0.8963
  • Overall Accuracy: 0.9648
  • Per Category Iou: [0.896665866220625, 0.9313625624392157, 0.6184457649558176, 0.946788356429534]
  • Per Category Accuracy: [0.9488350594956452, 0.9690753342283529, 0.6948783145659281, 0.9725778146880949]

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
1.0467 0.12 20 0.9970 0.4693 0.5892 0.8499 [0.016665768511477424, 0.7530905304669141, 0.32197766779593034, 0.785573865597306] [0.018291186933739902, 0.9736110066215201, 0.5554138836791528, 0.8094802372221535]
0.7279 0.25 40 0.5469 0.4852 0.5502 0.8940 [0.01804951884861912, 0.8184740635118999, 0.24534751963740423, 0.8590701787560616] [0.018703012459912727, 0.9597806760541293, 0.3156729455312819, 0.9066840327022103]
0.7806 0.38 60 0.4260 0.5242 0.6073 0.9049 [0.01807194500339603, 0.8366012049171444, 0.3657984221980414, 0.8765049443865006] [0.018709584143841017, 0.9630852108788323, 0.5316352476333795, 0.9157347069923247]
0.5022 0.5 80 0.3776 0.5673 0.6442 0.9155 [0.13278193831665586, 0.8571556379842626, 0.39241878205971326, 0.8868105485330777] [0.13725838108756988, 0.9454001415642639, 0.5529329784947034, 0.9412691031815928]
0.4512 0.62 100 0.3027 0.6147 0.6726 0.9272 [0.23169125963788326, 0.8698512434242224, 0.4538044468559813, 0.9035720315308671] [0.2351282792702802, 0.939292533650652, 0.5538645534494219, 0.9621157857635089]
0.5998 0.75 120 0.2814 0.6595 0.7168 0.9328 [0.3932326160264655, 0.873639653244202, 0.45899785777309354, 0.9119874363843206] [0.40385626412912046, 0.9319378939772671, 0.5617421687163787, 0.9697880271073833]
0.6628 0.88 140 0.2398 0.6224 0.6878 0.9322 [0.23667963537424397, 0.8731766506602201, 0.4613980137799232, 0.9182017221798935] [0.23933415698438568, 0.9457139193910193, 0.6013402818446667, 0.9646299239546201]
0.2891 1.0 160 0.2509 0.6993 0.8005 0.9365 [0.5407886628522588, 0.8871068137852166, 0.4540803897685749, 0.9151364246964913] [0.6209277465257698, 0.962521364442151, 0.6770795495822739, 0.9413993019486105]
0.3733 1.12 180 0.2347 0.7473 0.8210 0.9448 [0.6897757129718597, 0.8989510444856402, 0.478662489496792, 0.9219419098227718] [0.7343878695476929, 0.9509685390377233, 0.6376642920153895, 0.9610472258827709]
0.6024 1.25 200 0.2099 0.7563 0.8150 0.9486 [0.7054491243993367, 0.9058582674210225, 0.4870865705869561, 0.9266736934314719] [0.7287186968788882, 0.9590880866678464, 0.6088175916934154, 0.9633364922034189]
0.3624 1.38 220 0.2075 0.7570 0.8244 0.9477 [0.6637631174949002, 0.8996125590304884, 0.5362553031346449, 0.9285364229598405] [0.6913981038501306, 0.971192970317068, 0.6830841308553933, 0.9520221263504635]
0.6915 1.5 240 0.1703 0.7974 0.8482 0.9540 [0.8079702418058335, 0.9109995197988137, 0.5379447825240211, 0.9325512132433632] [0.8631205860189615, 0.9445488751680811, 0.6074684141727886, 0.977596233163672]
0.3712 1.62 260 0.1817 0.7999 0.8580 0.9529 [0.8010650098967221, 0.9085553502562573, 0.560168977421881, 0.9298580626286841] [0.8874336259923242, 0.9404067164614021, 0.6274738009987867, 0.9766288563247306]
0.2538 1.75 280 0.1751 0.8150 0.8836 0.9566 [0.8004213101386246, 0.9180591806306987, 0.6065608191143141, 0.9348731824073968] [0.8772891365683543, 0.9692263290457869, 0.7281184904037895, 0.9597196634547871]
0.2284 1.88 300 0.1840 0.8126 0.9042 0.9567 [0.8009792424281661, 0.9194293484770127, 0.5939762508191415, 0.9359433726281363] [0.9485721921385136, 0.9659392778315237, 0.7435920245322974, 0.9586582645062351]
0.256 2.0 320 0.1616 0.8176 0.8884 0.9579 [0.809949528422126, 0.9207851938667173, 0.6025298091308882, 0.937268997247811] [0.8700449503180695, 0.9619340740732891, 0.7553170196520308, 0.9663523606431447]
0.4148 2.12 340 0.1642 0.8206 0.8719 0.9582 [0.7932048029906043, 0.9191135080588984, 0.6322722187829223, 0.937770246889229] [0.8329324606137076, 0.9742867746553344, 0.7192549377179132, 0.9612368510870202]
0.2985 2.25 360 0.1486 0.8375 0.8977 0.9613 [0.8586827106799612, 0.9252188318786676, 0.6245207917181071, 0.9415713272802116] [0.9194727757040464, 0.969478781784409, 0.7362456788018513, 0.9657594912576178]
0.1704 2.38 380 0.1467 0.8290 0.9022 0.9601 [0.8357054588123389, 0.9237357509430961, 0.6162257083130753, 0.9403938047508744] [0.9392075425407006, 0.9538586328240481, 0.742260144159371, 0.9735591413949309]
0.1934 2.5 400 0.1474 0.8291 0.8797 0.9599 [0.8571529421535651, 0.9213274361346364, 0.5977020515839274, 0.9403603679518928] [0.9043119008814818, 0.9737894189189619, 0.6774254925362808, 0.9630940434994081]
0.1912 2.62 420 0.1433 0.8310 0.9069 0.9606 [0.8355815564504465, 0.9245037970991156, 0.6227678339008111, 0.9410534282338662] [0.9617834673956854, 0.9520821920420963, 0.7389588599697052, 0.9748254174032972]
0.1921 2.75 440 0.1339 0.8286 0.8766 0.9608 [0.8511074855171785, 0.9240321564072764, 0.5977901602255353, 0.941451956991874] [0.9004806091512889, 0.9682032729318744, 0.6685767659770047, 0.9691355452863861]
0.139 2.88 460 0.1375 0.8324 0.8971 0.9615 [0.8342633614251669, 0.9255032673803006, 0.6270579504122933, 0.9426635896658996] [0.9409074181168182, 0.9556867297277537, 0.7161216829630508, 0.9756464136284629]
0.1557 3.0 480 0.1368 0.8347 0.8932 0.9603 [0.8546089056727355, 0.9225367596519559, 0.6212372144507865, 0.9405009897594654] [0.9146294446488968, 0.9608790971251646, 0.7266902402079611, 0.9707082533928404]
0.2095 3.12 500 0.1299 0.8431 0.9034 0.9629 [0.8774828339164978, 0.9283559261158525, 0.6227224847390085, 0.9439444287208694] [0.9498317648914358, 0.9668135908051043, 0.727485909002177, 0.9695951469339585]
0.1639 3.25 520 0.1300 0.8384 0.8794 0.9628 [0.8790845930676439, 0.9277390439822828, 0.6029040896735798, 0.9440283579425929] [0.9080161400557278, 0.9708537617842183, 0.6681270401367957, 0.9706067913536859]
0.1139 3.38 540 0.1235 0.8355 0.8815 0.9632 [0.8830324246537594, 0.928376002336871, 0.5856632431257472, 0.9450535716038398] [0.9476412035820059, 0.9631465359669655, 0.639146904675419, 0.9760456588476107]
0.2574 3.5 560 0.1264 0.8453 0.9043 0.9638 [0.878600355282667, 0.9295598939555374, 0.6275502561490758, 0.945452356561073] [0.9566728878607854, 0.9609682370480884, 0.7246467057582204, 0.9750175535837675]
0.2927 3.62 580 0.1309 0.8446 0.8981 0.9633 [0.8866855291248271, 0.9282894217693001, 0.6190576519555571, 0.944518781436582] [0.9452994935422253, 0.9598107425660218, 0.7115206416747593, 0.9759016962109369]
0.4659 3.75 600 0.1202 0.8484 0.9041 0.9641 [0.8833081778126276, 0.9297489957164383, 0.6347412766855173, 0.9456458562459887] [0.9525152024954875, 0.9567847534440064, 0.7285978684971991, 0.9785108795019711]
0.1478 3.88 620 0.1207 0.8503 0.9115 0.9644 [0.8839341322345905, 0.930666904641919, 0.640631018371209, 0.9461269754236669] [0.9544604209382612, 0.9586230491204685, 0.7562115292902486, 0.9766407602120007]
0.2361 4.0 640 0.1206 0.8509 0.9104 0.9639 [0.8843630597662286, 0.9290525728561428, 0.6450059707401802, 0.9453487498415234] [0.9602916951439637, 0.9520253703081671, 0.7487638716947004, 0.9805396552894644]
0.22 4.12 660 0.1193 0.8448 0.8875 0.9643 [0.8981674375904806, 0.9304938750380576, 0.6046215297860035, 0.9459224283816728] [0.9472622364754745, 0.9670648514793784, 0.6612946667951598, 0.9744785306883144]
0.209 4.25 680 0.1181 0.8503 0.9019 0.9645 [0.8927503260337429, 0.9310490279930463, 0.6313541481285349, 0.9459033573690366] [0.9507189422217549, 0.9618490401497869, 0.7188299220887047, 0.9762163122315232]
0.2477 4.38 700 0.1210 0.8483 0.8967 0.9646 [0.8965441720351905, 0.9309945711672964, 0.619250454020142, 0.9463799993792809] [0.9505327445104533, 0.9683300291075624, 0.6951229456548329, 0.9726875536488669]
0.2449 4.5 720 0.1218 0.8479 0.8985 0.9641 [0.8961950935913541, 0.9301026110955253, 0.6193945084735495, 0.9457735746871611] [0.9454922629374551, 0.9726557657238586, 0.7074187466486777, 0.9685915004384908]
0.1402 4.62 740 0.1205 0.8513 0.9089 0.9646 [0.8883404276098935, 0.9311559198709245, 0.6392782798591807, 0.9462383840511697] [0.9563946865744879, 0.9610148600092611, 0.7421637743364691, 0.9758302728873157]
0.1993 4.75 760 0.1156 0.8513 0.9002 0.9650 [0.8973513861058572, 0.9315816840805964, 0.6294303742460324, 0.9468717796656311] [0.9446992797434415, 0.9648566185002029, 0.7158152763466448, 0.9752565613203643]
0.1458 4.88 780 0.1162 0.8539 0.9091 0.9651 [0.8940502189002697, 0.932053937919347, 0.6428052076917813, 0.9468840079361927] [0.9550781592275205, 0.9630270646289607, 0.7427543483793808, 0.9753759721895434]
0.0971 5.0 800 0.1208 0.8483 0.8963 0.9648 [0.896665866220625, 0.9313625624392157, 0.6184457649558176, 0.946788356429534] [0.9488350594956452, 0.9690753342283529, 0.6948783145659281, 0.9725778146880949]

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Tokenizers 0.13.2
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