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nommis
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nommis_upernet

Transformers
PyTorch
upernet
generated_from_trainer
Inference Endpoints
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nommis_upernet

This model is a fine-tuned version of openmmlab/upernet-convnext-tiny on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8298
  • Mean Iou: 0.4359
  • Mean Accuracy: 0.5711
  • Overall Accuracy: 0.8557
  • Per Category Iou: [0.7266627269588516, 0.0, 0.9820990066298799, 0.7737974543977927, 0.8425987582111041, 0.9118247536169393, 0.9726662033819782, 0.8122102873261724, 0.7060892649742824, 0.9323953427902811, 0.7442598387025693, 0.0, 0.7741525763639443, 0.8833402511154924, 0.3458198395181419, 0.48438906694515466, 0.9688899504388846, 0.8158714543367765, 0.0, 0.618927466020724, 0.5458015267175572, nan, 0.5299230769230769, 0.6976306347909488, 0.6288592136049763, 0.9612746327578874, 0.0, 0.7109205064637772, 0.11801534807952739, nan, 0.71105051882148, 0.0, 0.3553369087463204, nan, nan, nan, 0.5561559888579387, 0.9047263216887727, nan, 0.43790174471992654, nan, 0.46764197285261755, 0.5622770723355686, 0.0, 0.10447052116490095, 0.9349260523321957, nan, 0.7546450104589639, nan, 0.13063921718134452, 0.7920258620689655, 0.5972421367338845, 0.0, nan, nan, 0.04073974805682123, nan, 0.0025618035096708082, nan, nan, 0.0, nan, nan, nan, 0.790377628536827, 0.0, 0.030017921146953404, 0.0, nan, 0.7069909121933339, 0.8378582609165003, 0.0, nan, 0.8619144689344714, nan, nan, nan, nan, nan, nan, nan, 0.4185729847494553, 0.4426762609838788, nan, 0.0, 0.26306620209059234, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7372762760550932, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.5035460992907801, nan, nan, nan, nan, 0.2639971651311127, 0.7368421052631579, nan, nan, 0.0, nan, nan, 0.8980218005651999, 0.0867579908675799, nan, 0.2571780870177911, nan, nan, nan, 0.019954648526077097, nan]
  • Per Category Accuracy: [0.9350150831695787, 0.0, 0.9898977608571053, 0.9468311944718657, 0.9858914696034872, 0.9446002971164812, 0.9873030801786974, 0.9766294737495217, 0.8152646602425334, 0.9558830062939652, 0.8404185172744125, nan, 0.9482760503100708, 0.9994129905197969, 0.36869247770581576, 0.6960774166839164, 0.9786686503548523, 0.9150002298533536, nan, 0.707359274069517, 0.5458015267175572, nan, 0.5952648405772055, 0.7334279418904284, 0.7907029586931044, 0.9738813390785549, nan, 0.7393486185061888, 0.11801534807952739, nan, 0.9547442286179193, nan, 0.5238689733071755, nan, nan, nan, 0.735938075930704, 0.9214231354642314, nan, 0.5645996744117212, nan, 0.8795475477780336, 0.5754964953271028, nan, 0.10464452269915295, 0.9510289891411268, nan, 0.8400219148061909, nan, 0.13063921718134452, 0.8251295336787565, 0.6196429701842122, nan, nan, nan, 0.04073974805682123, nan, 0.003250975292587776, nan, nan, 0.0, nan, nan, nan, 0.9195593697963366, nan, 0.04633471645919779, 0.0, nan, 0.7652056143806945, 0.9403319602616913, nan, nan, 0.8951790755116443, nan, nan, nan, nan, nan, nan, nan, 0.4377670179436058, 0.5056108740319266, nan, 0.0, 0.26849217638691325, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7716422129146645, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5035460992907801, nan, nan, nan, nan, 0.29353821907013394, 0.7541684480547243, nan, nan, 0.0, nan, nan, 0.8980218005651999, 0.0867579908675799, nan, 0.2841572596340989, nan, nan, nan, 0.019954648526077097, 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
0.5977 1.0 34 0.5814 0.5176 0.6841 0.8856 [0.7660843033494732, 0.0, 0.9794685836538216, 0.8789679649951558, 0.8295538836494649, 0.8478427164982764, 0.9928306515200187, 0.8274302155280742, 0.8630769230769231, 0.9538202459242484, 0.7167099129715953, nan, 0.8104229873259775, 0.9305473972756007, 0.7695456327764123, 0.7084858569051581, 0.9721086739780658, 0.8336854877513022, 0.0, 0.5408901755020807, 0.5744274809160306, nan, 0.5888370024460752, 0.6713795470144132, 0.39083198531019103, 0.9339271751304973, 0.0, 0.7944134370074969, 0.8364044137436082, nan, 0.6214711469181896, 0.0, 0.3619014137558697, nan, 0.0, nan, 0.6173685522053327, 0.936768149882904, nan, 0.422684510401484, nan, 0.34025327620967744, 0.6021936864633494, 0.0, 0.37291366404760795, 0.8765082598263452, nan, 0.7353898216842379, nan, 0.8424794213020703, 0.8503079350536977, 0.7414849463073984, nan, nan, nan, 0.11078058067586863, nan, 0.0, nan, nan, 0.9645819690024012, nan, nan, nan, 0.787726577855427, 0.0, 0.0, 0.0, nan, 0.6879327793757324, 0.9058887645928348, nan, nan, 0.8822272915802571, nan, nan, 0.0, nan, nan, nan, nan, 0.5370735833098393, 0.4759898904802022, nan, 0.0, 0.6299303944315545, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.7771374095419682, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0939655172413793, nan, 0.2721329338055026, nan, nan, nan, nan, 0.6528649582493521, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.6389530408006159, nan, nan, nan, nan, 0.4692268129607406, 0.7807167235494881, nan, nan, 0.0, nan, nan, 0.8988800257835791, 0.1324200913242009, nan, 0.7835431115677712, nan, nan, nan, 0.005442176870748299, nan] [0.9011316120938234, 0.0, 0.9851044004777351, 0.9135077328068444, 0.9890922108277707, 0.8664058719421198, 0.9992211380202211, 0.9873323474969753, 0.9296442460583536, 0.9730025916327286, 0.7766207591535103, nan, 0.8793016857367456, 0.9764315693698453, 0.8847959711027786, 0.7712171393086318, 0.9801413377832284, 0.9159885992736634, nan, 0.6896724084896955, 0.5744274809160306, nan, 0.6864253002678649, 0.7306270306606416, 0.42763519816307494, 0.9765219312783118, nan, 0.9012654226472128, 0.9149378814939844, nan, 0.9649140648835554, nan, 0.8567861601569466, nan, nan, nan, 0.7305565794323627, 0.989345509893455, nan, 0.4721030042918455, nan, 0.9326214598204007, 0.6574182242990654, nan, 0.3816979156752641, 0.9793623546203252, nan, 0.7738665936173127, nan, 0.8584318210700216, 0.9617731721358664, 0.8905963157561563, nan, nan, nan, 0.24953095684803, nan, 0.0, nan, nan, 0.9707271528998243, nan, nan, nan, 0.9240425259382605, nan, 0.0, 0.0, nan, 0.7067361624121044, 0.9565665132057184, nan, nan, 0.9381836626676077, nan, nan, nan, nan, nan, nan, nan, 0.5425804614070066, 0.5357989568515884, nan, 0.0, 0.9655049786628734, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.9216537759847881, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1074420896993593, nan, 0.2918492550394391, nan, nan, nan, nan, 0.684973717600145, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6540583136327817, nan, nan, nan, nan, 0.539204097714736, 0.7823856348867038, nan, nan, 0.0, nan, nan, 0.9007670569236981, 0.1324200913242009, nan, 0.8905216037368626, nan, nan, nan, 0.005442176870748299, nan]
0.3877 2.0 68 0.5653 0.5115 0.6736 0.8881 [0.7745904386481587, 0.0, 0.9789408111569841, 0.8753753290873106, 0.8137283660897624, 0.8606358919781828, 0.9912883864576656, 0.8626208644389962, 0.8686241243432574, 0.8709963765479707, 0.7330345710627401, nan, 0.7518398031828488, 0.8077829907833004, 0.7834587648110668, 0.655588979611326, 0.96953066142463, 0.8082511400996925, 0.0, 0.5366617596391768, 0.5741444866920152, nan, 0.7404975559932881, 0.641149743184512, 0.6693374504219128, 0.9340814207302859, 0.0, 0.8023946664247812, 0.8043774390692879, nan, 0.6004671381301498, 0.0, 0.2654998516760605, nan, 0.0, nan, 0.5679130204189871, 0.9107158737216541, nan, 0.4105938294813668, nan, 0.610498022333428, 0.6516751401381827, 0.0, 0.43044476926289416, 0.8916293368467282, nan, 0.7386666666666667, nan, 0.4900889453621347, 0.8868197580755263, 0.5626931037585866, nan, nan, nan, 0.06622084488664166, nan, 0.42113618324073027, nan, nan, 0.028778558875219684, nan, nan, nan, 0.8092801091777551, 0.0, 0.023059434881455017, 0.0, nan, 0.7287710012923873, 0.8587532621270414, nan, nan, 0.8706548100242523, nan, nan, nan, 0.0, nan, nan, nan, 0.44710860366713684, 0.4930229525299948, nan, 0.0, 0.6689463019250254, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.7789842777966294, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.16337923334573876, nan, 0.4213075060532688, nan, nan, nan, nan, 0.6239366041253933, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6232449297971919, nan, nan, nan, nan, 0.5280971659919028, 0.754588134869825, nan, nan, 0.0, nan, nan, 0.971455531965086, 0.1689497716894977, nan, 0.7648934232100565, nan, nan, nan, 0.02312925170068027, nan] [0.9037006593999012, 0.0, 0.9862443044824484, 0.9343731490621915, 0.9930299648339616, 0.8757017600259892, 0.9999706089818952, 0.971448662399305, 0.9121726469855744, 0.8863976305072195, 0.7834757834757835, nan, 0.9575836317582321, 0.9955093774764463, 0.8768349405671685, 0.710515421237839, 0.9767184704155693, 0.8758562037420126, nan, 0.6587972931405721, 0.5763358778625954, nan, 0.8770413894409401, 0.7272285917018336, 0.9021382955822909, 0.9739586785990498, nan, 0.873842992759135, 0.8576279170183118, nan, 0.9412183463846232, nan, 0.26603650199155815, nan, nan, nan, 0.7893844452635459, 0.9742199391171994, nan, 0.5495042178481575, nan, 0.8040812802210454, 0.7299941588785047, nan, 0.49048253545255543, 0.9789573167203502, nan, 0.7967401725790988, nan, 0.490151226331173, 0.934887737478411, 0.5937361524340065, nan, nan, nan, 0.10881801125703565, nan, 0.60370611183355, nan, nan, 0.028778558875219684, nan, nan, nan, 0.9114896887408735, nan, 0.04910096818810512, 0.0, nan, 0.809994801499357, 0.9667736854858251, nan, nan, 0.9263188073394495, nan, nan, nan, nan, nan, nan, nan, 0.4514383366562233, 0.5975185712027817, nan, 0.0, 0.9391891891891891, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.8848318215689801, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.21636274026614097, nan, 0.5083260297984225, nan, nan, nan, nan, 0.6469699716029242, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6296296296296297, nan, nan, nan, nan, 0.642434988179669, 0.7558785805899957, nan, nan, 0.0, nan, nan, 0.9974969721437222, 0.1689497716894977, nan, 0.8171467497080576, nan, nan, nan, 0.02312925170068027, nan]
0.4101 3.0 102 0.5502 0.5382 0.6674 0.8963 [0.7958455697387357, 0.0, 0.9820871902914288, 0.8030802022937557, 0.8371248703365883, 0.9183902585062349, 0.9863315504920932, 0.8710058516235025, 0.8509945750452079, 0.944313498662425, 0.7662497981033249, nan, 0.8065968433037127, 0.921303721423329, 0.7567498334955685, 0.7070750647109577, 0.9728956002648703, 0.8345390701019588, 0.0, 0.5426217652422728, 0.5534351145038168, nan, 0.6401022479512819, 0.6534273115980759, 0.6927213010541929, 0.9289094999575516, 0.0, 0.8205992316663236, 0.26806628575767355, nan, 0.6130116669962429, 0.0, 0.35706356484386315, nan, 0.0, nan, 0.5835685890418851, 0.9075444807135304, nan, 0.49014084507042255, nan, 0.7023757041391134, 0.6868935356020227, 0.0, 0.48340644246532855, 0.879012987012987, nan, 0.7453178748885209, nan, 0.6842372666073924, 0.9122432574770629, 0.6738749622470552, nan, nan, nan, 0.0, nan, 0.3693246852346433, nan, nan, 0.44881370826010547, nan, nan, nan, 0.8036529680365296, 0.0, 0.002489110143123833, 0.0, nan, 0.8096110796782366, 0.8681201444359339, nan, nan, 0.8841417283120376, nan, nan, nan, nan, nan, nan, nan, 0.4528036066497605, 0.4262782401902497, nan, 0.0, 0.6705547652916074, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.8083911152896933, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.17670682730923695, nan, 0.29631665750412317, nan, nan, nan, nan, 0.6214306400231683, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6314589665653495, nan, nan, nan, nan, 0.48512911843276935, 0.708066581306018, nan, nan, 0.0, nan, nan, 0.9647892809846538, 0.0045662100456621, nan, 0.7339694996878623, nan, nan, nan, 0.009977324263038548, nan] [0.9054244502089015, 0.0, 0.9895839670580551, 0.9347943402435012, 0.9856387795068332, 0.9550535181907704, 1.0, 0.9758745850697497, 0.9240885840724918, 0.967108478341355, 0.8262979136341582, nan, 0.9320573849244476, 0.9947462651521822, 0.8121020859219511, 0.7633512730283585, 0.9821719375138221, 0.9068174504666023, nan, 0.6635266071977853, 0.5534351145038168, nan, 0.7356778709064201, 0.7305149942114502, 0.9383027577794255, 0.9671086067837807, nan, 0.9453131945747844, 0.2771682597004966, nan, 0.9457947727041595, nan, 0.43980738362760835, nan, nan, nan, 0.7467010689273866, 0.9389269406392694, nan, 0.592274678111588, nan, 0.8254087036610638, 0.7339369158878505, nan, 0.5191776910631007, 0.9790923293536752, nan, 0.8012601013559786, nan, 0.6845850806964036, 0.9473517559009788, 0.7062100398809901, nan, nan, nan, 0.0, nan, 0.4720416124837451, nan, nan, 0.44881370826010547, nan, nan, nan, 0.9242987062892276, nan, 0.0027662517289073307, 0.0, nan, 0.9004760732167775, 0.9612006299975769, nan, nan, 0.9278184544812985, nan, nan, nan, nan, nan, nan, nan, 0.45770435773283963, 0.45329540066382173, nan, 0.0, 0.8381934566145093, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.8837526016907779, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2385411532774766, nan, 0.3149284253578732, nan, nan, nan, nan, 0.6482387771131654, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6548463356973995, nan, nan, nan, nan, 0.5366430260047281, 0.7092774690038478, nan, nan, 0.0, nan, nan, 1.0, 0.0045662100456621, nan, 0.8008952899961075, nan, nan, nan, 0.009977324263038548, nan]
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Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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Finetuned from

Dataset used to train nommis/nommis_upernet

Evaluation results

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