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# segmentation_test

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/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

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segmentation_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 

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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* 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 1.13.1+cu116
* 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 1.13.1+cu116
* 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 1.13.1+cu116
* Tokenizers 0.13.2