
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# segment_test

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1260
- Mean Iou: 0.8447
- Mean Accuracy: 0.9060
- Overall Accuracy: 0.9643
- Per Category Iou: [0.8581731584290526, 0.9316708017224354, 0.6429457606853357, 0.9461543989839276]
- Per Category Accuracy: [0.9425328146084153, 0.9716290009653072, 0.7414471782174548, 0.9684613946705924]

## 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                                                               |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------:|
| 1.0598        | 0.12  | 20   | 1.0566          | 0.4321   | 0.5515        | 0.8279           | [0.006337257377741027, 0.7300264416674316, 0.23785776348718268, 0.7540601698014678] | [0.008091933477034156, 0.9660296098187956, 0.4518657444815921, 0.779822911077032]   |
| 0.7539        | 0.25  | 40   | 0.6128          | 0.4529   | 0.5206        | 0.8749           | [0.00885438037222906, 0.7887343476637405, 0.18630294846181414, 0.827538656414587]   | [0.009217881990081138, 0.9747549910409742, 0.23250084632472676, 0.8657848800171862] |
| 0.8122        | 0.38  | 60   | 0.4928          | 0.4920   | 0.5507        | 0.8982           | [0.02270113673871408, 0.8263637783329851, 0.2554015068180403, 0.8635887381305902]   | [0.023653681019224364, 0.9630036206967801, 0.30407150146655104, 0.9122355221296055] |
| 0.5643        | 0.5   | 80   | 0.4269          | 0.5063   | 0.5663        | 0.9129           | [0.007592876375505398, 0.8532772644930355, 0.27466317854807065, 0.8898355351518092] | [0.007684489073480188, 0.9441805272844986, 0.3632055074118278, 0.9501073674832299]  |
| 0.4774        | 0.62  | 100  | 0.3648          | 0.5490   | 0.6034        | 0.9218           | [0.054267747505144744, 0.8633878614911209, 0.37650159164096025, 0.9020084353341656] | [0.055975413139862956, 0.9428365409577416, 0.45329399467742054, 0.961363980883101]  |
| 0.6967        | 0.75  | 120  | 0.3155          | 0.6158   | 0.6710        | 0.9325           | [0.19913477919641662, 0.8733583185910088, 0.47153849968591927, 0.9190236996330312]  | [0.20509568371799589, 0.9502280021743253, 0.5638919570734214, 0.9648268960892941]   |
| 0.4543        | 0.88  | 140  | 0.2964          | 0.6272   | 0.7016        | 0.9310           | [0.2268179599804783, 0.8694887954672872, 0.4961026114602021, 0.9165896188501754]    | [0.2321184480311235, 0.9344494411072529, 0.6716976656263668, 0.9680582434884342]    |
| 0.2924        | 1.0   | 160  | 0.2722          | 0.6820   | 0.7592        | 0.9384           | [0.40849985639476094, 0.8843766461027478, 0.5120505100078259, 0.9231923075849184]   | [0.4548831992709812, 0.9611082383832005, 0.667739089824088, 0.9532322308907735]     |
| 0.3467        | 1.12  | 180  | 0.2517          | 0.6815   | 0.7430        | 0.9414           | [0.40527347045732925, 0.8910632241964158, 0.5029203059015287, 0.9266932255262942]   | [0.447198710197501, 0.9557005046935547, 0.6038508392823166, 0.9651288642296557]     |
| 0.5212        | 1.25  | 200  | 0.2396          | 0.6752   | 0.7408        | 0.9427           | [0.3668495492266549, 0.8948194469733646, 0.5099936563424119, 0.9292469056689828]    | [0.3789189141825701, 0.965308543339751, 0.6575510698285852, 0.9614078764674099]     |
| 0.3599        | 1.38  | 220  | 0.2108          | 0.6717   | 0.7162        | 0.9446           | [0.3204229923658836, 0.891874970369172, 0.5386909667961237, 0.9357227289520356]     | [0.32437612813907435, 0.9667832593900233, 0.605627503453252, 0.9680877242092517]    |
| 0.7198        | 1.5   | 240  | 0.2018          | 0.7692   | 0.8618        | 0.9527           | [0.6678205541462868, 0.9118050318864169, 0.5610132418656683, 0.9363469668607539]    | [0.8350594956451641, 0.9497174012785288, 0.6927260551877853, 0.9696466684460499]    |
| 0.3682        | 1.62  | 260  | 0.1931          | 0.7812   | 0.8479        | 0.9538           | [0.6890403931363558, 0.9109837638365132, 0.5887742209415688, 0.93605613049878]      | [0.7620327532726986, 0.9497927662356516, 0.7058595323345466, 0.9739980042388998]    |
| 0.3358        | 1.75  | 280  | 0.1947          | 0.7916   | 0.8837        | 0.9550           | [0.7285153761152414, 0.9167544021441286, 0.5844378827851311, 0.9366799980170186]    | [0.8390747945253492, 0.9631461386121828, 0.7712946420849488, 0.9611668227501886]    |
| 0.2334        | 1.88  | 300  | 0.2014          | 0.7843   | 0.8673        | 0.9522           | [0.7115100993908304, 0.9111127309647635, 0.5831612483285399, 0.9312333415206432]    | [0.850726390130207, 0.9751160540868734, 0.6929311499390893, 0.9502407282203037]     |
| 0.2996        | 2.0   | 320  | 0.1701          | 0.8027   | 0.8896        | 0.9568           | [0.7526564111480359, 0.9206156591940219, 0.5997533073741746, 0.9377288799677301]    | [0.8344833780207841, 0.964968540097336, 0.7965929561072522, 0.9622910891033862]     |
| 0.4622        | 2.12  | 340  | 0.1897          | 0.7614   | 0.8286        | 0.9509           | [0.5700844122547072, 0.9050700733042791, 0.6361100555452245, 0.9344921716585569]    | [0.6064108967281776, 0.975059629707727, 0.777640224269875, 0.9551298779200561]      |
| 0.338         | 2.25  | 360  | 0.1713          | 0.8093   | 0.8947        | 0.9579           | [0.7434863865211815, 0.9179570569529378, 0.6345544665942896, 0.9411654222120547]    | [0.9167652419255911, 0.9513808608505936, 0.7380297560360868, 0.9725677707832107]    |
| 0.1828        | 2.38  | 380  | 0.1613          | 0.8102   | 0.8854        | 0.9583           | [0.7563041981028708, 0.923073161283024, 0.6226932638283688, 0.9387242789473116]     | [0.8467351874244257, 0.9575329724998702, 0.7662636431252486, 0.9708901596701879]    |
| 0.1848        | 2.5   | 400  | 0.1523          | 0.8332   | 0.8882        | 0.9617           | [0.8272877802882616, 0.9256725856927198, 0.6369852470922355, 0.9430524306296677]    | [0.8886931987452464, 0.9707900525673887, 0.7260329485953481, 0.9672511041320438]    |
| 0.2053        | 2.62  | 420  | 0.1450          | 0.8382   | 0.9129        | 0.9626           | [0.8318187789064231, 0.9285519737454524, 0.6480460535396757, 0.9442850494628351]    | [0.9293894467518357, 0.9631609731907377, 0.7888883123172988, 0.970120219961517]     |
| 0.1596        | 2.75  | 440  | 0.1376          | 0.8327   | 0.8953        | 0.9624           | [0.8243205167317957, 0.928415735271021, 0.6344793824796972, 0.9436346449664342]     | [0.9292602036345793, 0.9591271598881472, 0.717386845766276, 0.9752671632199643]     |
| 0.137         | 2.88  | 460  | 0.1472          | 0.8260   | 0.9081        | 0.9616           | [0.7858947726139123, 0.9279875195406946, 0.6464381724674384, 0.9437068326674424]    | [0.9422546133221177, 0.9608167124242775, 0.7587838622553998, 0.9706571968763456]    |
| 0.1679        | 3.0   | 480  | 0.1471          | 0.8331   | 0.9126        | 0.9624           | [0.8265760388185004, 0.9295336697547381, 0.6321998556258659, 0.9441039056088667]    | [0.9403510155442231, 0.9637791247810575, 0.7769013889609604, 0.9693229385117723]    |
| 0.1736        | 3.12  | 500  | 0.1322          | 0.8402   | 0.8960        | 0.9634           | [0.8539363341788129, 0.9291877911278092, 0.632669557547822, 0.9451061440414477]     | [0.9248133641764366, 0.9646743651065282, 0.721404726074956, 0.9731695680841902]     |
| 0.234         | 3.25  | 520  | 0.1355          | 0.8448   | 0.9076        | 0.9632           | [0.8526502893205608, 0.9289085404109714, 0.6534740415947964, 0.944330630190735]     | [0.9257553055394915, 0.9662036512136275, 0.7685765188748946, 0.9699754203327694]    |
| 0.1183        | 3.38  | 540  | 0.1256          | 0.8402   | 0.8954        | 0.9637           | [0.8579969655846054, 0.9294856863546612, 0.6276158375419669, 0.9457592749372996]    | [0.9389994392163048, 0.9661690813475308, 0.7035145333106, 0.9727224283186038]       |
| 0.2085        | 3.5   | 560  | 0.1279          | 0.8480   | 0.9079        | 0.9642           | [0.8653295827282605, 0.9310186709909459, 0.6502631789997088, 0.9455883129741319]    | [0.9333061703730964, 0.9630336872086728, 0.7613438401150507, 0.9739094690773278]    |
| 0.3477        | 3.62  | 580  | 0.1419          | 0.8392   | 0.9182        | 0.9627           | [0.8356838761929862, 0.9295750569194587, 0.6477355195833494, 0.9437915808167578]    | [0.9515710705711231, 0.9679427406460035, 0.7873562792352684, 0.9660274217203163]    |
| 0.4279        | 3.75  | 600  | 0.1241          | 0.8453   | 0.8990        | 0.9639           | [0.8543157504970579, 0.9297143070002641, 0.6517228913538011, 0.9456141839889123]    | [0.9299589926922874, 0.955372819449404, 0.7305771563983385, 0.9801914665868114]     |
| 0.1622        | 3.88  | 620  | 0.1255          | 0.8438   | 0.9206        | 0.9638           | [0.8381374979278546, 0.9308920069470188, 0.6604220534808366, 0.9458239210474509]    | [0.9524867251984649, 0.95692011897332, 0.7977592780664754, 0.9753784831657644]      |
| 0.1982        | 4.0   | 640  | 0.1229          | 0.8484   | 0.9091        | 0.9647           | [0.8568106859830862, 0.9316640237424656, 0.6584440596013207, 0.9466997405108393]    | [0.9452293955803235, 0.9595748462766798, 0.754422510013813, 0.9770213126081696]     |
| 0.2273        | 4.12  | 660  | 0.1237          | 0.8444   | 0.8986        | 0.9640           | [0.8592317269149002, 0.9306231098333563, 0.6421928363458029, 0.9456102319021257]    | [0.9358165536337031, 0.9691394407999653, 0.7183357178686949, 0.970921035377795]     |
| 0.2371        | 4.25  | 680  | 0.1216          | 0.8460   | 0.9096        | 0.9645           | [0.8583380957371707, 0.9321090417297068, 0.6472432759450742, 0.9464390728899327]    | [0.9508328514098453, 0.961341618092252, 0.7507901089967407, 0.9753970829896241]     |
| 0.2474        | 4.38  | 700  | 0.1283          | 0.8411   | 0.9093        | 0.9635           | [0.8497493402399663, 0.9309167522466655, 0.6387399097291454, 0.9448643127623003]    | [0.9486861013266039, 0.9710814460747176, 0.7504342819583336, 0.9668204252105732]    |
| 0.2696        | 4.5   | 720  | 0.1273          | 0.8430   | 0.9049        | 0.9633           | [0.8574838830140721, 0.929602014003094, 0.6403298755318946, 0.9444960387864841]     | [0.9355734013283564, 0.9743545898715855, 0.7448102379346218, 0.9649164542411783]    |
| 0.1506        | 4.62  | 740  | 0.1255          | 0.8479   | 0.9182        | 0.9646           | [0.8525455460809918, 0.9327217587877741, 0.6600164613831294, 0.9464177410883513]    | [0.9496433766188248, 0.9639090597950073, 0.7866569802639545, 0.972458775815393]     |
| 0.2033        | 4.75  | 760  | 0.1209          | 0.8501   | 0.9107        | 0.9650           | [0.8658943552605436, 0.9327154832962604, 0.6546122598692601, 0.9470460950988947]    | [0.935571210767047, 0.9689885784341254, 0.76733359526157, 0.9708881136895634]       |
| 0.1636        | 4.88  | 780  | 0.1203          | 0.8483   | 0.9157        | 0.9648           | [0.8518065574480739, 0.9329685900413733, 0.661433964801116, 0.9468031896359194]     | [0.9475010076582023, 0.9648201943117868, 0.7779515729284813, 0.9726341721543897]    |
| 0.1047        | 5.0   | 800  | 0.1260          | 0.8447   | 0.9060        | 0.9643           | [0.8581731584290526, 0.9316708017224354, 0.6429457606853357, 0.9461543989839276]    | [0.9425328146084153, 0.9716290009653072, 0.7414471782174548, 0.9684613946705924]    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segment_test", "results": []}]}
transformers pytorch tensorboard segformer generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 

segment\_test
=============


This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1260
* Mean Iou: 0.8447
* Mean Accuracy: 0.9060
* Overall Accuracy: 0.9643
* Per Category Iou: [0.8581731584290526, 0.9316708017224354, 0.6429457606853357, 0.9461543989839276]
* Per Category Accuracy: [0.9425328146084153, 0.9716290009653072, 0.7414471782174548, 0.9684613946705924]


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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2