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# segment_test_1

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: 0.1207
- Mean Iou: 0.8483
- Mean Accuracy: 0.9024
- Overall Accuracy: 0.9649
- Per Category Iou: [0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934]
- Per Category Accuracy: [0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901]

## 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                                                              |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------:|
| 1.0371        | 0.12  | 20   | 1.0895          | 0.4995   | 0.6430        | 0.8631           | [0.05521318178580855, 0.7743794151988705, 0.347543309879974, 0.8209439082840918]  | [0.08660822249093107, 0.8923169863341743, 0.711708439278363, 0.8812568644974932]   |
| 0.6908        | 0.25  | 40   | 0.5755          | 0.5230   | 0.6242        | 0.8926           | [0.023865281651135446, 0.8147455929204539, 0.3949463345707554, 0.858397589605038] | [0.02690009287979952, 0.9560483225800299, 0.618844995317415, 0.8951707417330758]   |
| 0.7532        | 0.38  | 60   | 0.4352          | 0.5425   | 0.6362        | 0.9073           | [0.04830520945747081, 0.8390003744923147, 0.4011207931866636, 0.881716208435619]  | [0.051771725987066926, 0.9667796831969788, 0.6132110672093029, 0.9128954438801464] |
| 0.4797        | 0.5   | 80   | 0.3928          | 0.5754   | 0.6651        | 0.9187           | [0.095160456358129, 0.8591208204996739, 0.4492600600843336, 0.8980719848369781]   | [0.10522361249846661, 0.9658767606790423, 0.6592313641766187, 0.9299486923858831]  |
| 0.4974        | 0.62  | 100  | 0.3413          | 0.5757   | 0.6564        | 0.9231           | [0.08465830140216431, 0.8655205765788001, 0.4482722747299048, 0.9043341709317356] | [0.08808466081348684, 0.9438250272055575, 0.6388503821434132, 0.9547541614780908]  |
| 0.6012        | 0.75  | 120  | 0.3019          | 0.6256   | 0.7003        | 0.9309           | [0.20118898116264938, 0.8705267913237438, 0.5133711956715865, 0.9173562005725456] | [0.21758188318174648, 0.9550754656203344, 0.6747765579170281, 0.9537939455713355]  |
| 0.4656        | 0.88  | 140  | 0.2558          | 0.6196   | 0.6831        | 0.9354           | [0.18448255342169326, 0.8799348242584422, 0.4896770706399499, 0.9242898593866574] | [0.18675411387413912, 0.957629264808883, 0.6245802352906291, 0.9633061744905276]   |
| 0.3084        | 1.0   | 160  | 0.2720          | 0.6122   | 0.6959        | 0.9306           | [0.17521253603782794, 0.8710845346725691, 0.4824399357269581, 0.9201099745599581] | [0.18717908276816853, 0.9651212567854952, 0.6840404160211124, 0.9471146558242094]  |
| 0.3281        | 1.12  | 180  | 0.2593          | 0.6599   | 0.7300        | 0.9387           | [0.3196354045076814, 0.8866606687882002, 0.5077873946739834, 0.9254476921045761]  | [0.3419137619823704, 0.9647813859946743, 0.6571532354314773, 0.9563192436567626]   |
| 0.5177        | 1.25  | 200  | 0.2349          | 0.7152   | 0.7782        | 0.9445           | [0.5227583225430057, 0.9035693700640127, 0.5129509983263923, 0.9217210470710874]  | [0.5456644410563762, 0.9406031421756604, 0.6513240966564614, 0.9752501443811327]   |
| 0.3315        | 1.38  | 220  | 0.2277          | 0.6762   | 0.7332        | 0.9435           | [0.3379172541018912, 0.8906873024131107, 0.5423203687425245, 0.93385833084482]    | [0.3523035942729965, 0.9655347706627136, 0.650958385533654, 0.9641001079719775]    |
| 0.7584        | 1.5   | 240  | 0.2096          | 0.7006   | 0.7579        | 0.9411           | [0.4065273757133468, 0.8956735509588918, 0.5836574037952176, 0.9166967934457825]  | [0.4186732208261045, 0.9310246402349798, 0.702414928921078, 0.9793378276707719]    |
| 0.4114        | 1.62  | 260  | 0.2266          | 0.7157   | 0.7832        | 0.9389           | [0.46836755859780127, 0.8939419126736278, 0.5910928241310868, 0.9094497536602162] | [0.5292527557261273, 0.9181066362487166, 0.7057063290263436, 0.9796145000506845]   |
| 0.2829        | 1.75  | 280  | 0.1932          | 0.7515   | 0.8111        | 0.9512           | [0.5553894238891705, 0.9038650415073118, 0.6093098813145624, 0.9374419595745119]  | [0.5760913376443579, 0.9716101928389251, 0.7359022068689445, 0.960860855647697]    |
| 0.2271        | 1.88  | 300  | 0.1864          | 0.7921   | 0.8488        | 0.9557           | [0.72749813882911, 0.9128831336642784, 0.5892135224784095, 0.9387835557133554]    | [0.7834695862467799, 0.9582075484693364, 0.682108077520874, 0.9715436644814973]    |
| 0.2779        | 2.0   | 320  | 0.1712          | 0.8180   | 0.8901        | 0.9587           | [0.8078135832010197, 0.9212119018178146, 0.6029921836595201, 0.9400085237549578]  | [0.8704261079859104, 0.9606214787743671, 0.7607928024097398, 0.9685803405441751]   |
| 0.4342        | 2.12  | 340  | 0.1708          | 0.7761   | 0.8320        | 0.9548           | [0.6155968922409166, 0.9114185374153494, 0.6376315789473684, 0.939658585661896]   | [0.640631845504092, 0.963783230780479, 0.7543977998028125, 0.9693606961542074]     |
| 0.3321        | 2.25  | 360  | 0.1498          | 0.8358   | 0.8902        | 0.9617           | [0.8466817196643333, 0.9257548699292617, 0.6282106201058927, 0.9425208804748949]  | [0.9010895851953105, 0.9624874568340255, 0.7247603727288228, 0.9726055284256458]   |
| 0.2023        | 2.38  | 380  | 0.1489          | 0.8266   | 0.8892        | 0.9595           | [0.8245918770829803, 0.9218535979694774, 0.6204584829387099, 0.9396094918519875]  | [0.8953634579324606, 0.9540908204687515, 0.7327368288397814, 0.9745457690515671]   |
| 0.1612        | 2.5   | 400  | 0.1474          | 0.8319   | 0.8833        | 0.9614           | [0.8386504767201818, 0.9250884504372539, 0.6212118602838925, 0.9425717867382386]  | [0.881909030369942, 0.9701526954959041, 0.7130847980310904, 0.9679183728130094]    |
| 0.2084        | 2.62  | 420  | 0.1456          | 0.8373   | 0.9156        | 0.9617           | [0.8428293939804881, 0.9267780719611362, 0.6367325390207981, 0.9430187935498365]  | [0.9257859733978234, 0.9552186457937082, 0.807640891445572, 0.9735885291166293]    |
| 0.164         | 2.75  | 440  | 0.1284          | 0.8345   | 0.8808        | 0.9617           | [0.8596578459811758, 0.9255998505860551, 0.6105607176615363, 0.9421695191953943]  | [0.9127433713614777, 0.9537728041909803, 0.6767583168392675, 0.9800493639325235]   |
| 0.1297        | 2.88  | 460  | 0.1385          | 0.8449   | 0.9054        | 0.9635           | [0.8647724581323368, 0.9296026133121402, 0.6405698157260865, 0.9447385612148987]  | [0.9357968385819182, 0.9633411073589047, 0.749796758514521, 0.9727959906219689]    |
| 0.1775        | 3.0   | 480  | 0.1483          | 0.8312   | 0.9131        | 0.9615           | [0.8303105795421561, 0.9283098352535407, 0.6238329800678668, 0.9422278557852194]  | [0.9462020048017104, 0.9651012565947649, 0.7745366217682133, 0.9665813244748572]   |
| 0.2044        | 3.12  | 500  | 0.1337          | 0.8371   | 0.8944        | 0.9626           | [0.8455606368699817, 0.9275108345488315, 0.6310811541832109, 0.9440912228533584]  | [0.9218363913569213, 0.9599956449915814, 0.7206757748504414, 0.9752020638364555]   |
| 0.1668        | 3.25  | 520  | 0.1329          | 0.8441   | 0.9025        | 0.9629           | [0.8655859764078333, 0.9282151151180292, 0.6390094540302755, 0.9437038916825506]  | [0.9213698017980128, 0.962867990264278, 0.7534242174893931, 0.9724944874772036]    |
| 0.1279        | 3.38  | 540  | 0.1288          | 0.8347   | 0.8822        | 0.9624           | [0.8622657989592432, 0.9265309615800243, 0.6059068424468966, 0.9439998815395132]  | [0.9212317964355187, 0.9664261698919513, 0.6689078828044113, 0.9723242060897683]   |
| 0.2441        | 3.5   | 560  | 0.1289          | 0.8456   | 0.9119        | 0.9632           | [0.8699084528348765, 0.9296467606481729, 0.6388408116225611, 0.9439747399023978]  | [0.9510540981020977, 0.9645672117667877, 0.7615761160984553, 0.9703859184453523]   |
| 0.261         | 3.62  | 580  | 0.1373          | 0.8408   | 0.9059        | 0.9626           | [0.8702262323466258, 0.9285138712580321, 0.6209427648568083, 0.9435318648195579]  | [0.9490453533813504, 0.9671653822394067, 0.7391515996155091, 0.9684196380660275]   |
| 0.5753        | 3.75  | 600  | 0.1251          | 0.8458   | 0.8983        | 0.9627           | [0.8760502510695999, 0.9269758886426209, 0.6368816580927279, 0.9433603638703384]  | [0.9298275590137217, 0.9542444643180703, 0.7302238003810314, 0.9788558132354487]   |
| 0.1992        | 3.88  | 620  | 0.1289          | 0.8480   | 0.9126        | 0.9636           | [0.8658569991741146, 0.9291059762753285, 0.6517419325899511, 0.9451333803283866]  | [0.9278188142929744, 0.9559565336252214, 0.7898717787151185, 0.9769391943858292]   |
| 0.2911        | 4.0   | 640  | 0.1263          | 0.8451   | 0.8990        | 0.9623           | [0.8676994568324171, 0.9257222374094188, 0.6441978583675714, 0.9428325001046762]  | [0.9276851900530992, 0.9481842740751965, 0.7380742344158877, 0.9821508650313081]   |
| 0.2322        | 4.12  | 660  | 0.1234          | 0.8448   | 0.8923        | 0.9638           | [0.8776973476081853, 0.929274732930499, 0.626829659547631, 0.9455449914778657]    | [0.9237794192383857, 0.968169497775343, 0.7048637108312268, 0.972200331262863]     |
| 0.2316        | 4.25  | 680  | 0.1228          | 0.8493   | 0.9069        | 0.9642           | [0.8763959224746586, 0.9306747334769754, 0.64476604812217, 0.9454498311627832]    | [0.9386358060389394, 0.9577756238205185, 0.753671319599398, 0.9776486846669562]    |
| 0.2397        | 4.38  | 700  | 0.1262          | 0.8442   | 0.8963        | 0.9640           | [0.8718388103686404, 0.9303958268346455, 0.6286213954621487, 0.94588759466782]    | [0.9306095894011882, 0.9690709633257429, 0.7141201558720109, 0.971342786383813]    |
| 0.2163        | 4.5   | 720  | 0.1273          | 0.8438   | 0.8975        | 0.9635           | [0.8711635054714977, 0.9292374082869599, 0.6295386570878305, 0.945216509372582]   | [0.9251331861276133, 0.9722796031962159, 0.724520683682118, 0.9680362026971605]    |
| 0.1596        | 4.62  | 740  | 0.1207          | 0.8515   | 0.9108        | 0.9646           | [0.8777356406779111, 0.9314006904528226, 0.6507271627753556, 0.9461335840246559]  | [0.9470848010094106, 0.9579392015394054, 0.7604592145612331, 0.9777115520716019]   |
| 0.2282        | 4.75  | 760  | 0.1192          | 0.8504   | 0.9024        | 0.9650           | [0.8835435013923162, 0.9318821516960537, 0.6391197674041569, 0.947006215721923]   | [0.9404013984543399, 0.9611932723067028, 0.7305820984405386, 0.9775071400073841]   |
| 0.1647        | 4.88  | 780  | 0.1197          | 0.8518   | 0.9100        | 0.9647           | [0.8779367174977694, 0.9313115604274004, 0.6518294433470307, 0.9462412590163557]  | [0.9526751134710758, 0.9568937611060662, 0.7517933435633607, 0.9786714889809993]   |
| 0.1087        | 5.0   | 800  | 0.1207          | 0.8483   | 0.9024        | 0.9649           | [0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934]   | [0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901]    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segment_test_1", "results": []}]}
transformers pytorch segformer generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 

segment\_test\_1
================


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: 0.1207
* Mean Iou: 0.8483
* Mean Accuracy: 0.9024
* Overall Accuracy: 0.9649
* Per Category Iou: [0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934]
* Per Category Accuracy: [0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901]


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+cpu
* 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 1.13.1+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2