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lora_segformer

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lora_segformer

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4393
  • Mean Iou: 0.3682
  • Mean Accuracy: 0.5707
  • Overall Accuracy: 0.8396
  • Per Category Iou: [0.6438135421351039, 0.9050984072652993, 0.9749698684406963, 0.7369247110674255, 0.752654357505371, 0.8027859009457442, 0.5003933394519471, 0.6087506208515826, 0.7086873558198924, 0.0, 0.5266673119736714, 0.6564246536171983, 0.8532161335841957, 0.9411694700095208, 0.44680635049557726, 0.6368607368445408, 0.7191841188567591, 0.8702582894594784, 0.8936646895801497, 0.4566314362463284, 0.5366785367432826, nan, 0.9618790668147913, 0.0, nan, 0.9237170739775875, 0.0, nan, 0.0, 0.0, nan, 0.9728922153107986, 0.0, 0.0, nan, nan, 0.3453142950927294, nan, 0.0, 0.9417587233463799, nan, nan, 0.5209098524389458, 0.2924377427461732, 0.0, nan, nan, 0.1969524675915397, nan, nan, 0.857620930940067, nan, nan, nan, nan, nan, nan, 0.45423584504044273, nan, 0.0, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4432651787107851, nan, nan, nan, nan, nan, 0.3351013020964255, nan, 0.0, nan, nan, 0.7692795267272343, nan, nan, nan, 0.8001664342584679, 0.0, nan, nan, nan, nan, nan, 0.12577983497685652, nan, 0.0, nan, 0.8418795086761552, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7059779664078021, nan, 0.42956026058631924, nan, 0.0, 0.0, 0.0, nan, nan, 0.017543859649122806, 0.32014218009478673, 0.0, 0.0, 0.0, nan, nan, nan, 0.6160586916649684, nan, 0.02803347280334728, nan, nan, 0.3776207302709069, nan, nan]
  • Per Category Accuracy: [0.7197732663811959, 0.9539751388649803, 0.9812404259841538, 0.8428192733510058, 0.8449438987731134, 0.8896694090217593, 0.9736590343771925, 0.9186301631212439, 0.8542672739340398, nan, 0.7893896895250991, 0.751301657195561, 0.9366714807852123, 0.9824016563146998, 0.7211741330219792, 0.7214271985462424, 0.9486271036315324, 0.9383752542303241, 0.9309670282229777, 0.6021451981328831, 0.5816638012701308, nan, 0.9796657291565641, nan, nan, 0.9592377885131508, nan, nan, 0.0, 0.0, nan, 0.9877390687137204, 0.0, nan, nan, nan, 0.6032110091743119, nan, nan, 0.9756551961674547, nan, nan, 0.6600261267145656, 0.3362227475702653, nan, nan, nan, 0.1969524675915397, nan, nan, 0.9168331552292895, nan, nan, nan, nan, nan, nan, 0.5525634386328327, nan, nan, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5317854133508099, nan, nan, nan, nan, nan, 0.8805268233398064, nan, nan, nan, nan, 0.965910598222576, nan, nan, nan, 0.9506212424849699, nan, nan, nan, nan, nan, nan, 0.12616067823980623, nan, 0.0, nan, 0.8465560057508822, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7475616752725186, nan, 0.503916698509744, nan, 0.0, nan, 0.0, nan, nan, 0.01847826086956522, 0.3420253164556962, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.02803347280334728, nan, nan, 0.5647348951911221, nan, nan]

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: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
0.578 1.0 34 0.4335 0.4257 0.6028 0.8404 [0.6418663414548549, 0.9074896632653997, 0.9748859857913768, 0.7452536974490729, 0.7725095836297798, 0.7895563784996465, 0.8048589812977329, 0.5835084591054788, 0.697607921158562, nan, 0.4623885262480463, 0.8163175164058556, 0.8420685621738349, 0.9439050269263879, 0.44604931475096343, 0.6465933007665364, 0.4895793655112369, 0.8706154885621441, 0.8845340235714567, 0.41549986836121683, 0.4928002043400985, nan, 0.9610218428320362, 0.0, nan, 0.9377188361788492, nan, 0.0, 0.0, 0.0, nan, 0.964612649417461, 0.0, nan, nan, nan, 0.348677327753699, nan, 0.0, 0.9272350843256967, nan, nan, 0.55854970518258, 0.4112942317014057, nan, nan, nan, 0.25881282692745056, nan, nan, 0.8393401015228427, nan, nan, nan, nan, nan, nan, 0.39275766016713093, nan, nan, nan, nan, nan, 0.40651188350048084, nan, nan, 0.0, nan, 0.25857991578704503, 0.0, nan, 0.5728976234003657, nan, nan, nan, nan, nan, 0.363462959372429, nan, 0.0, nan, nan, 0.7479575163398693, nan, nan, nan, 0.7846516007532957, 0.0, nan, nan, nan, nan, nan, 0.09929364278506558, nan, 0.0, nan, 0.9310013453776668, nan, nan, 0.4705128205128205, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7383895756765787, nan, 0.438504855953644, nan, 0.05760041327650781, nan, 0.0, nan, nan, 0.003260869565217391, 0.5562637362637363, 0.0, 0.0, 0.0, nan, nan, nan, 0.759592795614722, nan, 0.14393305439330545, nan, nan, 0.3608919902912621, nan, nan] [0.7211903514987216, 0.9599807007377846, 0.9831424027206697, 0.8425726812180201, 0.847612056811959, 0.8857605833502127, 0.8536258689967472, 0.7491993840367398, 0.8626896756917584, nan, 0.810748944127414, 0.9622593793698492, 0.9061166200046796, 0.9761697722567287, 0.6306765808394272, 0.7273680348063113, 0.49806244464127547, 0.9265145964157804, 0.908221358804166, 0.5747012281921409, 0.541548249769014, nan, 0.989946012349271, nan, nan, 0.9746537842190016, nan, nan, 0.0, 0.0, nan, 0.991783912024658, 0.0, nan, nan, nan, 0.6688646788990825, nan, nan, 0.9605942950183173, nan, nan, 0.8816949706074461, 0.4457578145521408, nan, nan, nan, 0.25881282692745056, nan, nan, 0.9218975049946568, nan, nan, nan, nan, nan, nan, 0.43811496633868463, nan, nan, nan, nan, nan, 0.40651188350048084, nan, nan, nan, nan, 0.8542301829268293, 0.0, nan, 0.9388634023031551, nan, nan, nan, nan, nan, 0.9162604525819363, nan, nan, nan, nan, 0.9714816288632445, nan, nan, nan, 0.8906346025384102, nan, nan, nan, nan, nan, nan, 0.09931368591037545, nan, 0.0, nan, 0.94967978042086, nan, nan, 0.4705128205128205, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8452859055268693, nan, 0.5132785632403516, nan, 0.058808016877637134, nan, 0.0, nan, nan, 0.003260869565217391, 0.640759493670886, nan, 0.0, nan, nan, nan, nan, 0.9350899742930592, nan, 0.14393305439330545, nan, nan, 0.4190593623392637, nan, nan]
0.4993 2.0 68 0.4278 0.4096 0.5930 0.8430 [0.6486897398675289, 0.9074343772576632, 0.9744601102660768, 0.7465532772015864, 0.7681879849069403, 0.7897608024986333, 0.7944699903194579, 0.6158370888921195, 0.6883525108587976, 0.0, 0.49035256796369914, 0.8199290959736643, 0.8454046065889448, 0.9456680469703335, 0.44078245406046235, 0.6456171665825928, 0.5266565939995683, 0.8698475329451425, 0.8893031409023717, 0.4024576802507837, 0.4831831409500525, nan, 0.9610213356899382, 0.0, nan, 0.9382585970022829, 0.0, 0.0, 0.0, 0.0, nan, 0.9632908109127779, 0.0, nan, nan, nan, 0.34785257386536705, nan, 0.0, 0.923812410031063, nan, nan, 0.5771766740528563, 0.3727795846885164, nan, nan, nan, 0.2733682055947237, nan, nan, 0.8165573770491803, nan, nan, nan, nan, nan, nan, 0.39990880072959417, nan, nan, nan, nan, nan, 0.38466822365709574, nan, nan, nan, nan, 0.24839483753810235, 0.0, nan, 0.5752045311516677, nan, nan, nan, nan, nan, 0.3594674325315685, nan, 0.0, nan, nan, 0.7540509856538342, nan, nan, nan, 0.7936648010961195, 0.0, nan, nan, nan, nan, nan, 0.0621719822365765, nan, 0.0, nan, 0.9265348060621629, nan, nan, 0.22948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.74375, nan, 0.43235921851912656, nan, 0.06743400475415989, nan, 0.0, nan, nan, 0.0, 0.5388962765957447, 0.0, 0.0, 0.0, nan, nan, nan, 0.767913593256059, nan, 0.16778242677824268, nan, nan, 0.34077017114914426, nan, nan] [0.7329041003049813, 0.9592775869133545, 0.9832450141823699, 0.865578276683626, 0.8444545422769059, 0.8903158520765906, 0.8374896358186109, 0.7504667420721985, 0.8269745725859977, nan, 0.7873907856981656, 0.9633453333730029, 0.9138945143253645, 0.9787370600414078, 0.6092709805608472, 0.72680016075204, 0.5403011514614703, 0.9329585219374233, 0.9171253575090389, 0.5312675869831496, 0.5223676362232904, nan, 0.9843532796689619, nan, nan, 0.9750939345142243, nan, nan, 0.0, 0.0, nan, 0.9930333407877722, 0.0, nan, nan, nan, 0.6548165137614679, nan, nan, 0.9544885242821806, nan, nan, 0.8719382756368387, 0.39138429209351194, nan, nan, nan, 0.2733682055947237, nan, nan, 0.9257073827997956, nan, nan, nan, nan, nan, nan, 0.45416882444329365, nan, nan, nan, nan, nan, 0.38466822365709574, nan, nan, nan, nan, 0.7298018292682927, 0.0, nan, 0.94129763130793, nan, nan, nan, nan, nan, 0.9126936947647921, nan, nan, nan, nan, 0.969094044302958, nan, nan, nan, 0.913186372745491, nan, nan, nan, nan, nan, nan, 0.0621719822365765, nan, 0.0, nan, 0.9428832832309502, nan, nan, 0.22948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8420348058902276, nan, 0.5031524646541842, nan, 0.07107067510548523, nan, 0.0, nan, nan, 0.0, 0.6155696202531645, nan, 0.0, nan, nan, nan, nan, 0.9366966580976864, nan, 0.16778242677824268, nan, nan, 0.3928131055134754, nan, nan]
0.5557 3.0 102 0.4295 0.4108 0.5965 0.8426 [0.6488481958660854, 0.9080819839680278, 0.9758335149269993, 0.7487195591075382, 0.7730225898142805, 0.7979049791304281, 0.7225942715845869, 0.6050706012315568, 0.706028681486982, 0.0, 0.46621396588091624, 0.7937648646911358, 0.8453008164057253, 0.9442843996318969, 0.440574681330026, 0.6488939041305216, 0.5999384046812443, 0.8700610867687978, 0.8900720325712496, 0.42334788648541377, 0.44026873139800715, nan, 0.9611629148221406, 0.0, nan, 0.941291888263541, 0.0, 0.0, 0.0, 0.0, nan, 0.9647750436291921, 0.0, nan, nan, nan, 0.3419296116504854, nan, 0.0, 0.9283184373539918, nan, nan, 0.5892088342186189, 0.3618648248643315, nan, nan, nan, 0.23402319763475096, nan, nan, 0.8253955072042519, nan, nan, nan, nan, nan, nan, 0.4057246706042708, nan, nan, nan, nan, nan, 0.4295919769199066, nan, 0.0, nan, nan, 0.22031930333817126, 0.0, nan, 0.5898237132244561, nan, nan, nan, nan, nan, 0.36696012766096026, nan, 0.0, nan, nan, 0.7479600163198694, nan, nan, nan, 0.788396860986547, 0.0, nan, nan, nan, nan, nan, 0.07426841574167507, nan, 0.0, nan, 0.9270973963355834, nan, nan, 0.32051282051282054, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7571858288770054, nan, 0.43562898740389333, nan, 0.040375466118040375, nan, 0.0, nan, nan, 0.008695652173913044, 0.5902710811100397, 0.0, 0.0, 0.0, nan, nan, nan, 0.7472052845528455, nan, 0.15523012552301255, nan, nan, 0.34555555555555556, nan, nan] [0.7270681741166323, 0.9607613995359449, 0.9846522570856879, 0.8646172926359611, 0.8428699593368054, 0.8955218734781653, 0.8946361375087697, 0.7538872460173615, 0.8704369120911817, nan, 0.812360963342683, 0.9184939454345303, 0.9139187193906778, 0.9772463768115942, 0.6294065835023249, 0.7234365990459716, 0.6470327723649247, 0.931465667778318, 0.92018779342723, 0.5649683848114675, 0.477492017823936, nan, 0.9896873888727249, nan, nan, 0.9785292538915727, nan, nan, 0.0, 0.0, nan, 0.9917304345289605, 0.0, nan, nan, nan, 0.6462155963302753, nan, nan, 0.9642890691048001, nan, nan, 0.8603853690398432, 0.38534278959810875, nan, nan, nan, 0.23402319763475096, nan, nan, 0.9235701342749617, nan, nan, nan, nan, nan, nan, 0.4624546866908338, nan, nan, nan, nan, nan, 0.4295919769199066, nan, nan, nan, nan, 0.5785060975609756, 0.0, nan, 0.936616421683363, nan, nan, nan, nan, nan, 0.8931161574129116, nan, nan, nan, nan, 0.9726754211433877, nan, nan, nan, 0.9019639278557114, nan, nan, nan, nan, nan, nan, 0.0742834073475979, nan, 0.0, nan, 0.9424258266893216, nan, nan, 0.32051282051282054, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8665136737425894, nan, 0.5087886893389377, nan, 0.04140295358649789, nan, 0.0, nan, nan, 0.008695652173913044, 0.6973417721518987, nan, 0.0, nan, nan, nan, nan, 0.9450514138817481, nan, 0.15523012552301255, nan, nan, 0.3834771886559803, nan, nan]
0.5656 4.0 136 0.4284 0.4146 0.5902 0.8437 [0.6476246062769682, 0.9061586328384159, 0.9741115368551038, 0.748082221211202, 0.7698932724473, 0.7981297789598201, 0.8191450714458707, 0.6238647776566874, 0.7023130069089817, 0.0, 0.48409688902646647, 0.8236723580752691, 0.8456922768066265, 0.9445086705202312, 0.44901894547194776, 0.6451439125750347, 0.5809504110864057, 0.8704905119025509, 0.8907759081370645, 0.4316634060573042, 0.46811044937736873, nan, 0.962008183821215, 0.0, nan, 0.9375762715370136, nan, 0.0, 0.0, 0.0, nan, 0.9650466423205737, 0.0, nan, nan, nan, 0.33824658375556577, nan, 0.0, 0.930601134215501, nan, nan, 0.5895336317787726, 0.3320630081300813, nan, nan, nan, 0.23675233113486469, nan, nan, 0.8433471799922916, nan, nan, nan, nan, nan, nan, 0.4053431598341778, nan, nan, nan, nan, nan, 0.41379310344827586, nan, nan, 0.0, nan, 0.22594519597641347, 0.0, nan, 0.6024539877300613, nan, nan, nan, nan, nan, 0.3582138557513407, nan, 0.0, nan, nan, 0.7567399419328079, nan, nan, nan, 0.7907672879492996, 0.0, nan, nan, nan, nan, nan, 0.058336697618086394, nan, 0.0, nan, 0.9077321255257198, nan, nan, 0.10384615384615385, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.740983606557377, nan, 0.43253447143795143, nan, 0.020117570215545395, nan, 0.0, nan, nan, 0.002173913043478261, 0.5118289353958144, nan, 0.0, 0.0, nan, nan, nan, 0.7517875383043923, nan, 0.17196652719665273, nan, nan, 0.37844396859386153, nan, nan] [0.7274008810572687, 0.9603346821804287, 0.9828968681516012, 0.8586374334110582, 0.8446875691798618, 0.889785768771629, 0.8628739077747305, 0.7957100611875009, 0.856155448241137, nan, 0.8067188960892414, 0.9630626878105382, 0.9176785728693492, 0.9810766045548655, 0.6534545975951986, 0.7323827995317224, 0.6219552701505757, 0.9307808722924898, 0.9220090658895904, 0.5770516767636641, 0.5055962948668468, nan, 0.9880386642097436, nan, nan, 0.9732474503488996, nan, nan, 0.0, 0.0, nan, 0.9917984967962119, 0.0, nan, nan, nan, 0.6315940366972477, nan, nan, 0.9633966872279801, nan, nan, 0.8494039843239712, 0.34331494615182556, nan, nan, nan, 0.23675233113486469, nan, nan, 0.9149746782511732, nan, nan, nan, nan, nan, nan, 0.4557224236147074, nan, nan, nan, nan, nan, 0.41379310344827586, nan, nan, nan, nan, 0.6206173780487805, 0.0, nan, 0.9193895702649565, nan, nan, nan, nan, nan, 0.9079776483176792, nan, nan, nan, nan, 0.9680328956094972, nan, nan, nan, 0.9035136940547762, nan, nan, nan, nan, nan, nan, 0.058336697618086394, nan, 0.0, nan, 0.9168082603581231, nan, nan, 0.10384615384615385, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8211895199847007, nan, 0.5034390523500191, nan, 0.02030590717299578, nan, 0.0, nan, nan, 0.002173913043478261, 0.569620253164557, nan, 0.0, nan, nan, nan, nan, 0.9460154241645244, nan, 0.17196652719665273, nan, nan, 0.46697199224942754, nan, nan]
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0.5577 50.0 1700 0.4393 0.3682 0.5707 0.8396 [0.6438135421351039, 0.9050984072652993, 0.9749698684406963, 0.7369247110674255, 0.752654357505371, 0.8027859009457442, 0.5003933394519471, 0.6087506208515826, 0.7086873558198924, 0.0, 0.5266673119736714, 0.6564246536171983, 0.8532161335841957, 0.9411694700095208, 0.44680635049557726, 0.6368607368445408, 0.7191841188567591, 0.8702582894594784, 0.8936646895801497, 0.4566314362463284, 0.5366785367432826, nan, 0.9618790668147913, 0.0, nan, 0.9237170739775875, 0.0, nan, 0.0, 0.0, nan, 0.9728922153107986, 0.0, 0.0, nan, nan, 0.3453142950927294, nan, 0.0, 0.9417587233463799, nan, nan, 0.5209098524389458, 0.2924377427461732, 0.0, nan, nan, 0.1969524675915397, nan, nan, 0.857620930940067, nan, nan, nan, nan, nan, nan, 0.45423584504044273, nan, 0.0, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4432651787107851, nan, nan, nan, nan, nan, 0.3351013020964255, nan, 0.0, nan, nan, 0.7692795267272343, nan, nan, nan, 0.8001664342584679, 0.0, nan, nan, nan, nan, nan, 0.12577983497685652, nan, 0.0, nan, 0.8418795086761552, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7059779664078021, nan, 0.42956026058631924, nan, 0.0, 0.0, 0.0, nan, nan, 0.017543859649122806, 0.32014218009478673, 0.0, 0.0, 0.0, nan, nan, nan, 0.6160586916649684, nan, 0.02803347280334728, nan, nan, 0.3776207302709069, nan, nan] [0.7197732663811959, 0.9539751388649803, 0.9812404259841538, 0.8428192733510058, 0.8449438987731134, 0.8896694090217593, 0.9736590343771925, 0.9186301631212439, 0.8542672739340398, nan, 0.7893896895250991, 0.751301657195561, 0.9366714807852123, 0.9824016563146998, 0.7211741330219792, 0.7214271985462424, 0.9486271036315324, 0.9383752542303241, 0.9309670282229777, 0.6021451981328831, 0.5816638012701308, nan, 0.9796657291565641, nan, nan, 0.9592377885131508, nan, nan, 0.0, 0.0, nan, 0.9877390687137204, 0.0, nan, nan, nan, 0.6032110091743119, nan, nan, 0.9756551961674547, nan, nan, 0.6600261267145656, 0.3362227475702653, nan, nan, nan, 0.1969524675915397, nan, nan, 0.9168331552292895, nan, nan, nan, nan, nan, nan, 0.5525634386328327, nan, nan, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5317854133508099, nan, nan, nan, nan, nan, 0.8805268233398064, nan, nan, nan, nan, 0.965910598222576, nan, nan, nan, 0.9506212424849699, nan, nan, nan, nan, nan, nan, 0.12616067823980623, nan, 0.0, nan, 0.8465560057508822, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7475616752725186, nan, 0.503916698509744, nan, 0.0, nan, 0.0, nan, nan, 0.01847826086956522, 0.3420253164556962, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.02803347280334728, nan, nan, 0.5647348951911221, nan, nan]

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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Dataset used to train nommis/lora_segformer

Evaluation results

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