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# segformer-b5-segments-warehouse1

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the jakka/warehouse_part1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1610
- Mean Iou: 0.6952
- Mean Accuracy: 0.8014
- Overall Accuracy: 0.9648
- Per Category Iou: [0.0, 0.47153295365063086, 0.9293854681828234, 0.9766069961659746, 0.927007550222462, 0.9649404794739765, 0.9824606440795911, 0.8340592613982738, 0.9706739467997174, 0.653761891900003, 0.0, 0.8080046149867717, 0.75033588410538, 0.6921465280057791, 0.7522124809345331, 0.7548461579766955, 0.3057219434101416, 0.5087799410519325, 0.84829211455404, 0.7730356409704979]
- Per Category Accuracy: [nan, 0.9722884260421271, 0.9720560851996344, 0.9881427437833682, 0.9650114633107388, 0.9828538231066912, 0.9897027752946145, 0.9071521422402136, 0.9848998109819413, 0.6895634832705517, 0.0, 0.8704126720181029, 0.8207667731629393, 0.7189631369929214, 0.8238982104266324, 0.8620090549531412, 0.3522998155172771, 0.5387075151368637, 0.9081104400345125, 0.8794092789466661]

## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                     | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                               |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.1656        | 1.0   | 787   | 0.1917          | 0.5943   | 0.6937        | 0.9348           | [0.0, 0.8760430595457738, 0.8113714411434076, 0.9533787339343942, 0.8499988352439646, 0.9330256290984922, 0.964368918196211, 0.6984009498117659, 0.9341093239597545, 0.288411561596369, 0.0, 0.6496866199024376, 0.4510074387900882, 0.5206343319728309, 0.6377305875444397, 0.5391733301507737, 0.1395685713288422, 0.390702947845805, 0.6999919374344916, 0.548023343373494]       | [nan, 0.9502542152644661, 0.9516900451328754, 0.9788975544390225, 0.921821413759201, 0.9534230318615367, 0.9778020069070933, 0.8108538425970355, 0.970571911491369, 0.2993067645848501, 0.0, 0.7454496363566233, 0.5849840255591054, 0.5858306866277158, 0.7137540570947559, 0.6925710548100606, 0.16576498144808574, 0.4165357186026834, 0.8142326593390103, 0.6474578532983408]   |
| 0.0948        | 2.0   | 1574  | 0.2058          | 0.6310   | 0.7305        | 0.9442           | [0.0, 0.904077233776714, 0.8616556242304713, 0.9604692135700761, 0.8306854004041632, 0.9459690932012119, 0.9714777936344227, 0.7463801249809481, 0.9197830038961162, 0.4759644364074744, 0.0, 0.7133768631713745, 0.4878118726699168, 0.5403469048526253, 0.6267211124010835, 0.6280780328151242, 0.11116434156063161, 0.4757211293446132, 0.7386220435315599, 0.6814722192019137]   | [nan, 0.9530795697109564, 0.9481439135801821, 0.9753750826203033, 0.9328161802391284, 0.9783733696392768, 0.9831560736299451, 0.8544532947139754, 0.9700176894451403, 0.5598936405938401, 0.0, 0.8212854589792271, 0.5434504792332269, 0.5765256977221256, 0.7602586827898242, 0.745275787709383, 0.12024542420662065, 0.5128732019823522, 0.8080522939565592, 0.8363729371469241]  |
| 0.0595        | 3.0   | 2361  | 0.1363          | 0.6578   | 0.7540        | 0.9494           | [0.0, 0.9109388123768081, 0.8466263269727539, 0.965583073696094, 0.8848508600101197, 0.9507919193853351, 0.9742807972055659, 0.7672266040033193, 0.9571650494933543, 0.5580972230045627, 0.0, 0.7572676505482382, 0.5338298840118263, 0.5743160573368553, 0.6964399439112182, 0.6369583059750492, 0.19255896751223853, 0.49017131449756574, 0.7563405327946686, 0.7018448645266491]  | [nan, 0.9587813659877967, 0.9568298005631468, 0.9842947615263231, 0.9380059570384915, 0.9734457175747111, 0.9839202800499454, 0.863077218359317, 0.9757816512090675, 0.6272609287455287, 0.0, 0.8589569413670591, 0.5999361022364217, 0.6161844118746441, 0.7983763527021668, 0.793146442915981, 0.2242190576871256, 0.5288397085810358, 0.8216978654762351, 0.8232729860771318]    |
| 0.0863        | 4.0   | 3148  | 0.1706          | 0.6597   | 0.7678        | 0.9537           | [0.0, 0.5911845175607978, 0.8922572171811833, 0.9657396689703207, 0.8726664918778465, 0.948172990516989, 0.9741643734457509, 0.7832072821045744, 0.9578631876788363, 0.5869565217391305, 0.0, 0.7602876424039574, 0.5747447162194254, 0.6642950791717092, 0.6978602093118107, 0.7122118073263809, 0.21745086578505152, 0.5091171801864137, 0.763416879968237, 0.7220314268720861]    | [nan, 0.9656626144746107, 0.9588916966191391, 0.9766109980050623, 0.9234167566678667, 0.9783156758536367, 0.9891284919047324, 0.8876447135391675, 0.9773653302095363, 0.6623721946123896, 0.0, 0.8391697702425289, 0.6185942492012779, 0.6961703584876796, 0.8060121894956657, 0.8277923697200732, 0.24677155234956366, 0.5498060503499884, 0.8475353565667555, 0.8369956852453183] |
| 0.0849        | 5.0   | 3935  | 0.1529          | 0.6489   | 0.7616        | 0.9535           | [0.0, 0.34717493700692625, 0.9200786785121082, 0.9707860061715432, 0.9064316496153364, 0.9571373496125165, 0.9765647396031262, 0.7914886053951578, 0.9636858999629485, 0.5253852888123762, 0.0, 0.7668434757450091, 0.6228696113699357, 0.5646135260344276, 0.7194371537530142, 0.7276571750775304, 0.13134474327628362, 0.5398065590178835, 0.8087983436006237, 0.7371620697069805] | [nan, 0.9673995855258336, 0.9622823082917784, 0.9832096263122092, 0.9590923200613435, 0.9794833291868915, 0.9849481430590119, 0.8741570190973889, 0.9814726613968338, 0.5661042702035389, 0.0, 0.8519369313384734, 0.674888178913738, 0.5955861885708164, 0.7973710835377057, 0.8440933293815855, 0.139191177994735, 0.5807830511082053, 0.8902258318640507, 0.8387304835194164]    |
| 0.0652        | 6.0   | 4722  | 0.1776          | 0.6701   | 0.7802        | 0.9598           | [0.0, 0.442020662403383, 0.9221209597093164, 0.9723970198449976, 0.9094898951877407, 0.958969887541612, 0.9774286126326331, 0.8043337900190548, 0.9641322534475246, 0.524194500874002, 0.0, 0.7732021981650511, 0.6714277552419585, 0.6791383524722951, 0.7265590222386986, 0.7252668038047013, 0.25612624095650144, 0.512317443386938, 0.8223912256195354, 0.7602526763224181]      | [nan, 0.9667776521571092, 0.968306375662177, 0.9871287057126554, 0.9515142073239339, 0.9800501491032743, 0.9870913605013194, 0.8911998464531551, 0.9789458602211063, 0.5619638504637396, 0.0, 0.8429926328466184, 0.750926517571885, 0.7091730161871252, 0.8058454540303847, 0.8431735260151052, 0.2957320232987169, 0.5489159698031933, 0.8944742469145065, 0.8592366887593968]    |
| 0.0516        | 7.0   | 5509  | 0.2204          | 0.6782   | 0.7854        | 0.9562           | [0.0, 0.5972965874238374, 0.9024890361234837, 0.9727685140940331, 0.915582953759141, 0.9598962357171329, 0.9798718588278901, 0.8112726586102719, 0.9047252363294271, 0.6408527982442389, 0.0, 0.7886848740988032, 0.676712646342877, 0.5672950158399087, 0.7336613818739761, 0.7298649456617311, 0.3028603088856569, 0.5060868673401364, 0.8269845785168136, 0.7471687598272396]     | [nan, 0.9698273468544609, 0.9632905651879291, 0.9861640741314249, 0.9551792854314081, 0.9817079843391511, 0.9899518141518776, 0.8996100259110301, 0.9832172012468946, 0.6987812984710835, 0.0, 0.8565569379384828, 0.7460702875399361, 0.593452450290354, 0.8111955580377016, 0.848355084979611, 0.3625810998486827, 0.5422458600265925, 0.8997261507296395, 0.834927271918509]     |
| 0.1051        | 8.0   | 6296  | 0.1860          | 0.6731   | 0.7789        | 0.9575           | [0.0, 0.44805540920356957, 0.9045125103512419, 0.9742941726927242, 0.9171717803896707, 0.9608739687771942, 0.9806696534895757, 0.8165927346840907, 0.9677688538979997, 0.6195552331193943, 0.0, 0.795984684169727, 0.6862710467443778, 0.573071397774824, 0.7390593444665892, 0.746059006435751, 0.2037963564144674, 0.5303406505500898, 0.8387988518436741, 0.7590468131997875]     | [nan, 0.9709112878685233, 0.966379770128131, 0.9872427322752713, 0.9529925896087971, 0.9834568092767589, 0.9900317817435064, 0.8913394344939497, 0.9851288999243455, 0.6704124592447216, 0.0, 0.871338387626268, 0.7448562300319489, 0.5994265432176736, 0.8121846392929121, 0.8435414473616973, 0.2212134402918558, 0.5609595288067426, 0.8906947518475448, 0.8579244695520661]    |
| 0.0619        | 9.0   | 7083  | 0.2919          | 0.6996   | 0.7903        | 0.9579           | [0.0, 0.934913158921961, 0.9053172937262943, 0.9749731654503406, 0.8705131863049136, 0.9625421596476281, 0.9801264786114002, 0.8223383305806123, 0.9066864104553713, 0.6468175775129386, 0.0, 0.7950479182280621, 0.7176821075997429, 0.5689160215594734, 0.7424713897302829, 0.7480081111150989, 0.3071719253739231, 0.5035704204000125, 0.8359422295252097, 0.7696666024282135]    | [nan, 0.9682325320018036, 0.9702179964865137, 0.9871538608460199, 0.9606411126417358, 0.9816951395784177, 0.9890656141613147, 0.9035010425481796, 0.9836680314909386, 0.689949669209585, 0.0, 0.8547140781629688, 0.7850479233226837, 0.5903872774743949, 0.8138309496636962, 0.8520138583707216, 0.3614203096822337, 0.5292682658813446, 0.9065161120906329, 0.8882611983452693]   |
| 0.081         | 10.0  | 7870  | 0.2470          | 0.6804   | 0.7921        | 0.9583           | [0.0, 0.4404433924045006, 0.9318621565838054, 0.9751204660574527, 0.8701648407446415, 0.9625333515302946, 0.9811772580795882, 0.8257730976318673, 0.9694596723226286, 0.6262599628453287, 0.0, 0.8035308913444122, 0.7247258740455824, 0.5731919576321138, 0.7446832704519876, 0.7540709586972932, 0.2964031339031339, 0.5176075672651548, 0.8402309249924604, 0.7699341552529259]   | [nan, 0.9683524762943433, 0.9703483634609842, 0.9874040565137937, 0.9560906426120769, 0.9828287794111833, 0.9897414692905638, 0.9071739528715878, 0.9809845681174846, 0.6616061536513564, 0.0, 0.8707555296507566, 0.8066453674121405, 0.5982298533423343, 0.8269010675926151, 0.8575633386818196, 0.3450448769769707, 0.5489928903442743, 0.9145158870090407, 0.8764289844757795]  |
| 0.0595        | 11.0  | 8657  | 0.1520          | 0.6754   | 0.7803        | 0.9583           | [0.0, 0.43998949915443775, 0.9316636729918347, 0.974311900634481, 0.90408659589869, 0.9621039259469353, 0.9814528086580536, 0.8173484866921386, 0.9299168519752622, 0.5981595278841879, 0.0, 0.79896542666047, 0.7130791649318979, 0.5767892232828117, 0.7434904893608313, 0.7476740572849074, 0.2818679619421856, 0.5013427236914975, 0.8417679322268942, 0.7636900967723242]       | [nan, 0.9604694708457627, 0.9682111157218825, 0.9850226034689381, 0.9629913194164226, 0.9838887233262218, 0.9906282066977372, 0.8790295141463755, 0.9828138682520776, 0.6217973473457631, 0.0, 0.8472869246956067, 0.7660702875399361, 0.601589754313674, 0.8233235396482367, 0.8360910400932068, 0.3211657649814481, 0.5272243772183335, 0.8880687999399782, 0.8793425559361239]   |
| 0.0607        | 12.0  | 9444  | 0.1907          | 0.6792   | 0.7814        | 0.9611           | [0.0, 0.4394265102382861, 0.9325678358934418, 0.9751503005414947, 0.9213536629526586, 0.9630218995457999, 0.9808145244188059, 0.8160516650442948, 0.9402095421968347, 0.5678403556289702, 0.0, 0.7897903639847522, 0.717973174366617, 0.6351749265433101, 0.7451406149738536, 0.7539060338307724, 0.2810049109433409, 0.5169863186167534, 0.8447414560224139, 0.7628612943763745]    | [nan, 0.964392093449931, 0.9699039597844642, 0.9860071181495944, 0.9689476561441872, 0.9817555601847723, 0.9915172012546744, 0.8703445207331861, 0.9829836512368835, 0.5919660662847014, 0.0, 0.8320126171608817, 0.7695846645367412, 0.6606869598697208, 0.8177192854656857, 0.8353858575122385, 0.31786995004456603, 0.541465665967056, 0.8991915819484563, 0.8640852275254659]   |
| 0.054         | 13.0  | 10231 | 0.1756          | 0.6845   | 0.7854        | 0.9633           | [0.0, 0.44063089620853896, 0.9319015227980866, 0.9747420439658205, 0.9230841377589553, 0.9626774348954341, 0.9806204202647846, 0.824089995398513, 0.9682449901582629, 0.6269069221957562, 0.0, 0.7878031759942226, 0.7230044147476434, 0.6870255399578931, 0.7273836360818303, 0.7465091396254238, 0.25750268946841265, 0.5202245077135331, 0.8455619310735664, 0.7623883906475817]  | [nan, 0.9684613146338701, 0.9659761462687484, 0.985573907589379, 0.969242630837417, 0.9846717514218756, 0.9904148523034052, 0.8905935109009535, 0.9873657317056209, 0.6548320724256909, 0.0, 0.8321711888159841, 0.7743769968051119, 0.7167465941354711, 0.7672955669410517, 0.8485288256155018, 0.28777231930020936, 0.5469380130325374, 0.8955527628765427, 0.8564788043236511]   |
| 0.0908        | 14.0  | 11018 | 0.1677          | 0.6922   | 0.7956        | 0.9641           | [0.0, 0.4710389646938612, 0.9277225664822271, 0.9753445134184554, 0.9250469473155007, 0.9640090632546157, 0.9817333061419466, 0.8297056239192101, 0.970059681920668, 0.647379308685926, 0.0, 0.79693329490141, 0.7458423929012165, 0.6895638439061885, 0.7486849253355593, 0.7520096317485606, 0.30687537928818764, 0.49287677819238446, 0.848826224760963, 0.7700556938025832]      | [nan, 0.9666066204807101, 0.9697912533607226, 0.9863864033340946, 0.9658514745108883, 0.9826761492096202, 0.9913739259863396, 0.9020659030037601, 0.9838249561044068, 0.6815485423063531, 0.0, 0.8412997732853904, 0.8109904153354632, 0.7185046709734403, 0.8232134618653327, 0.8490091673735526, 0.35638330949567815, 0.5181697306682197, 0.9016768578609746, 0.8671989680174369] |
| 0.0584        | 15.0  | 11805 | 0.1610          | 0.6952   | 0.8014        | 0.9648           | [0.0, 0.47153295365063086, 0.9293854681828234, 0.9766069961659746, 0.927007550222462, 0.9649404794739765, 0.9824606440795911, 0.8340592613982738, 0.9706739467997174, 0.653761891900003, 0.0, 0.8080046149867717, 0.75033588410538, 0.6921465280057791, 0.7522124809345331, 0.7548461579766955, 0.3057219434101416, 0.5087799410519325, 0.84829211455404, 0.7730356409704979]        | [nan, 0.9722884260421271, 0.9720560851996344, 0.9881427437833682, 0.9650114633107388, 0.9828538231066912, 0.9897027752946145, 0.9071521422402136, 0.9848998109819413, 0.6895634832705517, 0.0, 0.8704126720181029, 0.8207667731629393, 0.7189631369929214, 0.8238982104266324, 0.8620090549531412, 0.3522998155172771, 0.5387075151368637, 0.9081104400345125, 0.8794092789466661]  |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b5-segments-warehouse1", "results": []}]}
transformers pytorch segformer vision image-segmentation generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

segformer-b5-segments-warehouse1
================================


This model is a fine-tuned version of nvidia/mit-b5 on the jakka/warehouse\_part1 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1610
* Mean Iou: 0.6952
* Mean Accuracy: 0.8014
* Overall Accuracy: 0.9648
* Per Category Iou: [0.0, 0.47153295365063086, 0.9293854681828234, 0.9766069961659746, 0.927007550222462, 0.9649404794739765, 0.9824606440795911, 0.8340592613982738, 0.9706739467997174, 0.653761891900003, 0.0, 0.8080046149867717, 0.75033588410538, 0.6921465280057791, 0.7522124809345331, 0.7548461579766955, 0.3057219434101416, 0.5087799410519325, 0.84829211455404, 0.7730356409704979]
* Per Category Accuracy: [nan, 0.9722884260421271, 0.9720560851996344, 0.9881427437833682, 0.9650114633107388, 0.9828538231066912, 0.9897027752946145, 0.9071521422402136, 0.9848998109819413, 0.6895634832705517, 0.0, 0.8704126720181029, 0.8207667731629393, 0.7189631369929214, 0.8238982104266324, 0.8620090549531412, 0.3522998155172771, 0.5387075151368637, 0.9081104400345125, 0.8794092789466661]


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: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu102
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu102
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu102
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu102
* Datasets 2.2.2
* Tokenizers 0.12.1