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# segformer-b5-finetuned-segments-crop_crack_early-lr6-350-8

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1193
- Mean Iou: 0.3367
- Mean Accuracy: 0.5109
- Overall Accuracy: 0.4963
- Accuracy Unlabeled: nan
- Accuracy Crack: 0.4266
- Accuracy Potholes: 0.5952
- Iou Unlabeled: 0.0
- Iou Crack: 0.4186
- Iou Potholes: 0.5915

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Crack | Accuracy Potholes | Iou Unlabeled | Iou Crack | Iou Potholes |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-----------------:|:-------------:|:---------:|:------------:|
| 0.3866        | 0.07  | 20   | 0.4031          | 0.0249   | 0.0377        | 0.0385           | nan                | 0.0424         | 0.0331            | 0.0           | 0.0421    | 0.0327       |
| 0.1963        | 0.14  | 40   | 0.2341          | 0.0897   | 0.1376        | 0.1429           | nan                | 0.1684         | 0.1068            | 0.0           | 0.1624    | 0.1066       |
| 0.1268        | 0.21  | 60   | 0.1775          | 0.1088   | 0.1700        | 0.1885           | nan                | 0.2767         | 0.0633            | 0.0           | 0.2632    | 0.0633       |
| 0.0964        | 0.28  | 80   | 0.1500          | 0.2160   | 0.3305        | 0.3313           | nan                | 0.3349         | 0.3261            | 0.0           | 0.3251    | 0.3229       |
| 0.2564        | 0.35  | 100  | 0.1549          | 0.1962   | 0.3067        | 0.3214           | nan                | 0.3914         | 0.2221            | 0.0           | 0.3666    | 0.2219       |
| 0.1414        | 0.42  | 120  | 0.1507          | 0.3000   | 0.4611        | 0.4689           | nan                | 0.5057         | 0.4165            | 0.0           | 0.4954    | 0.4045       |
| 0.1419        | 0.49  | 140  | 0.1390          | 0.2567   | 0.4095        | 0.3786           | nan                | 0.2310         | 0.5880            | 0.0           | 0.2298    | 0.5403       |
| 0.1134        | 0.56  | 160  | 0.1325          | 0.2672   | 0.4087        | 0.4090           | nan                | 0.4106         | 0.4069            | 0.0           | 0.3988    | 0.4027       |
| 0.1312        | 0.63  | 180  | 0.1262          | 0.3163   | 0.4822        | 0.4740           | nan                | 0.4349         | 0.5295            | 0.0           | 0.4230    | 0.5259       |
| 0.1103        | 0.7   | 200  | 0.1376          | 0.1918   | 0.2951        | 0.2923           | nan                | 0.2791         | 0.3110            | 0.0           | 0.2763    | 0.2989       |
| 0.1163        | 0.77  | 220  | 0.1334          | 0.2892   | 0.4453        | 0.4505           | nan                | 0.4751         | 0.4156            | 0.0           | 0.4523    | 0.4153       |
| 0.1068        | 0.84  | 240  | 0.1237          | 0.2900   | 0.4439        | 0.4374           | nan                | 0.4063         | 0.4814            | 0.0           | 0.3947    | 0.4753       |
| 0.2267        | 0.91  | 260  | 0.1322          | 0.3473   | 0.5482        | 0.5329           | nan                | 0.4600         | 0.6363            | 0.0           | 0.4544    | 0.5875       |
| 0.0842        | 0.98  | 280  | 0.1205          | 0.3034   | 0.4680        | 0.4395           | nan                | 0.3034         | 0.6326            | 0.0           | 0.2989    | 0.6114       |
| 0.1425        | 1.05  | 300  | 0.1226          | 0.3565   | 0.5556        | 0.5476           | nan                | 0.5093         | 0.6019            | 0.0           | 0.5023    | 0.5674       |
| 0.0876        | 1.12  | 320  | 0.1088          | 0.3571   | 0.5432        | 0.5247           | nan                | 0.4364         | 0.6500            | 0.0           | 0.4280    | 0.6432       |
| 0.0829        | 1.19  | 340  | 0.1187          | 0.2714   | 0.4112        | 0.4062           | nan                | 0.3819         | 0.4405            | 0.0           | 0.3761    | 0.4382       |
| 0.11          | 1.26  | 360  | 0.1169          | 0.3105   | 0.4726        | 0.4702           | nan                | 0.4589         | 0.4863            | 0.0           | 0.4505    | 0.4811       |
| 0.1219        | 1.33  | 380  | 0.1144          | 0.3198   | 0.4932        | 0.4804           | nan                | 0.4191         | 0.5674            | 0.0           | 0.4160    | 0.5435       |
| 0.1124        | 1.4   | 400  | 0.1099          | 0.2951   | 0.4479        | 0.4354           | nan                | 0.3758         | 0.5199            | 0.0           | 0.3699    | 0.5154       |
| 0.0729        | 1.47  | 420  | 0.1063          | 0.3745   | 0.5697        | 0.5436           | nan                | 0.4191         | 0.7203            | 0.0           | 0.4124    | 0.7110       |
| 0.1154        | 1.54  | 440  | 0.1079          | 0.3125   | 0.4810        | 0.4525           | nan                | 0.3165         | 0.6455            | 0.0           | 0.3137    | 0.6238       |
| 0.1261        | 1.61  | 460  | 0.1114          | 0.3406   | 0.5243        | 0.5091           | nan                | 0.4368         | 0.6117            | 0.0           | 0.4284    | 0.5932       |
| 0.1003        | 1.68  | 480  | 0.1178          | 0.2676   | 0.4071        | 0.3937           | nan                | 0.3300         | 0.4841            | 0.0           | 0.3190    | 0.4839       |
| 0.0915        | 1.75  | 500  | 0.1035          | 0.3572   | 0.5527        | 0.5296           | nan                | 0.4197         | 0.6857            | 0.0           | 0.4166    | 0.6549       |
| 0.0883        | 1.82  | 520  | 0.1084          | 0.3561   | 0.5422        | 0.5307           | nan                | 0.4761         | 0.6083            | 0.0           | 0.4651    | 0.6033       |
| 0.0766        | 1.89  | 540  | 0.1062          | 0.3534   | 0.5374        | 0.5258           | nan                | 0.4704         | 0.6045            | 0.0           | 0.4586    | 0.6016       |
| 0.1108        | 1.96  | 560  | 0.1169          | 0.2802   | 0.4267        | 0.4250           | nan                | 0.4166         | 0.4368            | 0.0           | 0.4047    | 0.4359       |
| 0.1647        | 2.03  | 580  | 0.1126          | 0.2839   | 0.4346        | 0.4198           | nan                | 0.3493         | 0.5199            | 0.0           | 0.3466    | 0.5052       |
| 0.0811        | 2.1   | 600  | 0.1055          | 0.3803   | 0.5800        | 0.5670           | nan                | 0.5049         | 0.6551            | 0.0           | 0.4972    | 0.6436       |
| 0.0809        | 2.17  | 620  | 0.1157          | 0.3494   | 0.5339        | 0.5334           | nan                | 0.5312         | 0.5365            | 0.0           | 0.5222    | 0.5261       |
| 0.1387        | 2.24  | 640  | 0.1167          | 0.2710   | 0.4118        | 0.3985           | nan                | 0.3352         | 0.4884            | 0.0           | 0.3322    | 0.4807       |
| 0.1004        | 2.31  | 660  | 0.1048          | 0.3148   | 0.4783        | 0.4523           | nan                | 0.3282         | 0.6285            | 0.0           | 0.3253    | 0.6190       |
| 0.0807        | 2.38  | 680  | 0.1032          | 0.3410   | 0.5201        | 0.4996           | nan                | 0.4020         | 0.6381            | 0.0           | 0.3971    | 0.6259       |
| 0.0494        | 2.45  | 700  | 0.1098          | 0.3095   | 0.4701        | 0.4515           | nan                | 0.3629         | 0.5773            | 0.0           | 0.3567    | 0.5719       |
| 0.1155        | 2.52  | 720  | 0.1125          | 0.3477   | 0.5306        | 0.5182           | nan                | 0.4593         | 0.6018            | 0.0           | 0.4465    | 0.5966       |
| 0.0935        | 2.59  | 740  | 0.1141          | 0.3113   | 0.4756        | 0.4651           | nan                | 0.4150         | 0.5362            | 0.0           | 0.4060    | 0.5278       |
| 0.0757        | 2.66  | 760  | 0.1029          | 0.3753   | 0.5740        | 0.5574           | nan                | 0.4780         | 0.6701            | 0.0           | 0.4717    | 0.6542       |
| 0.0705        | 2.73  | 780  | 0.1052          | 0.3040   | 0.4633        | 0.4343           | nan                | 0.2960         | 0.6306            | 0.0           | 0.2945    | 0.6173       |
| 0.0712        | 2.8   | 800  | 0.1069          | 0.3359   | 0.5107        | 0.5001           | nan                | 0.4495         | 0.5719            | 0.0           | 0.4379    | 0.5697       |
| 0.1333        | 2.87  | 820  | 0.1026          | 0.3495   | 0.5316        | 0.5068           | nan                | 0.3885         | 0.6746            | 0.0           | 0.3841    | 0.6643       |
| 0.1269        | 2.94  | 840  | 0.1371          | 0.2208   | 0.3346        | 0.3209           | nan                | 0.2555         | 0.4137            | 0.0           | 0.2489    | 0.4135       |
| 0.1034        | 3.01  | 860  | 0.1012          | 0.3639   | 0.5571        | 0.5373           | nan                | 0.4427         | 0.6715            | 0.0           | 0.4375    | 0.6541       |
| 0.112         | 3.08  | 880  | 0.1007          | 0.3550   | 0.5445        | 0.5165           | nan                | 0.3829         | 0.7061            | 0.0           | 0.3812    | 0.6838       |
| 0.1031        | 3.15  | 900  | 0.1030          | 0.3416   | 0.5183        | 0.4942           | nan                | 0.3794         | 0.6572            | 0.0           | 0.3732    | 0.6517       |
| 0.1174        | 3.22  | 920  | 0.1041          | 0.3561   | 0.5413        | 0.5249           | nan                | 0.4469         | 0.6357            | 0.0           | 0.4392    | 0.6291       |
| 0.1096        | 3.29  | 940  | 0.1132          | 0.3231   | 0.4887        | 0.4733           | nan                | 0.3995         | 0.5779            | 0.0           | 0.3936    | 0.5756       |
| 0.1089        | 3.36  | 960  | 0.1065          | 0.3735   | 0.5676        | 0.5456           | nan                | 0.4406         | 0.6946            | 0.0           | 0.4323    | 0.6882       |
| 0.1207        | 3.43  | 980  | 0.1005          | 0.3610   | 0.5493        | 0.5254           | nan                | 0.4116         | 0.6869            | 0.0           | 0.4067    | 0.6764       |
| 0.0988        | 3.5   | 1000 | 0.1075          | 0.3235   | 0.4943        | 0.4702           | nan                | 0.3550         | 0.6336            | 0.0           | 0.3524    | 0.6181       |
| 0.1012        | 3.57  | 1020 | 0.1108          | 0.3094   | 0.4702        | 0.4488           | nan                | 0.3468         | 0.5936            | 0.0           | 0.3438    | 0.5844       |
| 0.1522        | 3.64  | 1040 | 0.1074          | 0.3352   | 0.5083        | 0.4894           | nan                | 0.3991         | 0.6175            | 0.0           | 0.3929    | 0.6126       |
| 0.0507        | 3.71  | 1060 | 0.1031          | 0.3508   | 0.5334        | 0.5140           | nan                | 0.4214         | 0.6455            | 0.0           | 0.4152    | 0.6374       |
| 0.0974        | 3.78  | 1080 | 0.1108          | 0.3120   | 0.4726        | 0.4498           | nan                | 0.3409         | 0.6043            | 0.0           | 0.3337    | 0.6024       |
| 0.0432        | 3.85  | 1100 | 0.1029          | 0.3276   | 0.4984        | 0.4708           | nan                | 0.3391         | 0.6577            | 0.0           | 0.3346    | 0.6482       |
| 0.0878        | 3.92  | 1120 | 0.1203          | 0.2935   | 0.4450        | 0.4284           | nan                | 0.3489         | 0.5410            | 0.0           | 0.3410    | 0.5396       |
| 0.0645        | 3.99  | 1140 | 0.1114          | 0.3547   | 0.5413        | 0.5229           | nan                | 0.4352         | 0.6473            | 0.0           | 0.4275    | 0.6365       |
| 0.073         | 4.06  | 1160 | 0.1088          | 0.3599   | 0.5455        | 0.5284           | nan                | 0.4466         | 0.6445            | 0.0           | 0.4388    | 0.6408       |
| 0.1115        | 4.13  | 1180 | 0.1039          | 0.3405   | 0.5211        | 0.4964           | nan                | 0.3784         | 0.6638            | 0.0           | 0.3748    | 0.6468       |
| 0.0932        | 4.2   | 1200 | 0.1090          | 0.3421   | 0.5193        | 0.5024           | nan                | 0.4219         | 0.6167            | 0.0           | 0.4123    | 0.6141       |
| 0.0625        | 4.27  | 1220 | 0.1057          | 0.3567   | 0.5440        | 0.5180           | nan                | 0.3941         | 0.6939            | 0.0           | 0.3869    | 0.6832       |
| 0.092         | 4.34  | 1240 | 0.1145          | 0.3060   | 0.4637        | 0.4485           | nan                | 0.3761         | 0.5512            | 0.0           | 0.3687    | 0.5492       |
| 0.0605        | 4.41  | 1260 | 0.0981          | 0.3715   | 0.5661        | 0.5389           | nan                | 0.4089         | 0.7232            | 0.0           | 0.4032    | 0.7113       |
| 0.1164        | 4.48  | 1280 | 0.1272          | 0.3301   | 0.5069        | 0.5158           | nan                | 0.5586         | 0.4551            | 0.0           | 0.5355    | 0.4547       |
| 0.1143        | 4.55  | 1300 | 0.1199          | 0.3206   | 0.4898        | 0.4868           | nan                | 0.4726         | 0.5071            | 0.0           | 0.4619    | 0.4999       |
| 0.0704        | 4.62  | 1320 | 0.1187          | 0.3217   | 0.4909        | 0.4857           | nan                | 0.4609         | 0.5210            | 0.0           | 0.4449    | 0.5202       |
| 0.0756        | 4.69  | 1340 | 0.1096          | 0.3272   | 0.4973        | 0.4868           | nan                | 0.4368         | 0.5578            | 0.0           | 0.4274    | 0.5544       |
| 0.0925        | 4.76  | 1360 | 0.1049          | 0.3392   | 0.5157        | 0.4883           | nan                | 0.3577         | 0.6736            | 0.0           | 0.3529    | 0.6646       |
| 0.091         | 4.83  | 1380 | 0.1020          | 0.3688   | 0.5637        | 0.5415           | nan                | 0.4355         | 0.6920            | 0.0           | 0.4294    | 0.6769       |
| 0.0721        | 4.9   | 1400 | 0.1086          | 0.3267   | 0.4972        | 0.4690           | nan                | 0.3342         | 0.6602            | 0.0           | 0.3302    | 0.6500       |
| 0.065         | 4.97  | 1420 | 0.1184          | 0.3388   | 0.5174        | 0.5071           | nan                | 0.4581         | 0.5767            | 0.0           | 0.4442    | 0.5723       |
| 0.0982        | 5.03  | 1440 | 0.1124          | 0.3311   | 0.5042        | 0.4801           | nan                | 0.3654         | 0.6430            | 0.0           | 0.3587    | 0.6347       |
| 0.0564        | 5.1   | 1460 | 0.1055          | 0.3691   | 0.5663        | 0.5456           | nan                | 0.4470         | 0.6855            | 0.0           | 0.4386    | 0.6688       |
| 0.0704        | 5.17  | 1480 | 0.1111          | 0.3261   | 0.4949        | 0.4716           | nan                | 0.3605         | 0.6293            | 0.0           | 0.3529    | 0.6253       |
| 0.1318        | 5.24  | 1500 | 0.1102          | 0.3438   | 0.5233        | 0.5123           | nan                | 0.4599         | 0.5867            | 0.0           | 0.4472    | 0.5842       |
| 0.0679        | 5.31  | 1520 | 0.1099          | 0.3395   | 0.5171        | 0.5042           | nan                | 0.4423         | 0.5920            | 0.0           | 0.4304    | 0.5880       |
| 0.0831        | 5.38  | 1540 | 0.1080          | 0.3761   | 0.5723        | 0.5595           | nan                | 0.4983         | 0.6463            | 0.0           | 0.4865    | 0.6417       |
| 0.0823        | 5.45  | 1560 | 0.1017          | 0.3696   | 0.5625        | 0.5362           | nan                | 0.4109         | 0.7141            | 0.0           | 0.4065    | 0.7023       |
| 0.1767        | 5.52  | 1580 | 0.1148          | 0.3141   | 0.4770        | 0.4594           | nan                | 0.3753         | 0.5786            | 0.0           | 0.3666    | 0.5757       |
| 0.0619        | 5.59  | 1600 | 0.1028          | 0.3645   | 0.5625        | 0.5413           | nan                | 0.4401         | 0.6849            | 0.0           | 0.4374    | 0.6562       |
| 0.0953        | 5.66  | 1620 | 0.1057          | 0.3654   | 0.5571        | 0.5394           | nan                | 0.4551         | 0.6590            | 0.0           | 0.4427    | 0.6536       |
| 0.0784        | 5.73  | 1640 | 0.1070          | 0.3438   | 0.5230        | 0.5007           | nan                | 0.3942         | 0.6517            | 0.0           | 0.3864    | 0.6450       |
| 0.0718        | 5.8   | 1660 | 0.1099          | 0.3749   | 0.5724        | 0.5603           | nan                | 0.5025         | 0.6423            | 0.0           | 0.4845    | 0.6401       |
| 0.0947        | 5.87  | 1680 | 0.1047          | 0.3769   | 0.5769        | 0.5516           | nan                | 0.4309         | 0.7229            | 0.0           | 0.4250    | 0.7056       |
| 0.089         | 5.94  | 1700 | 0.1036          | 0.3603   | 0.5478        | 0.5247           | nan                | 0.4146         | 0.6810            | 0.0           | 0.4086    | 0.6722       |
| 0.0513        | 6.01  | 1720 | 0.1097          | 0.3378   | 0.5147        | 0.4878           | nan                | 0.3592         | 0.6703            | 0.0           | 0.3508    | 0.6626       |
| 0.0876        | 6.08  | 1740 | 0.1042          | 0.3437   | 0.5247        | 0.5025           | nan                | 0.3969         | 0.6524            | 0.0           | 0.3894    | 0.6417       |
| 0.0839        | 6.15  | 1760 | 0.1095          | 0.3352   | 0.5097        | 0.4910           | nan                | 0.4016         | 0.6178            | 0.0           | 0.3906    | 0.6150       |
| 0.0465        | 6.22  | 1780 | 0.1077          | 0.3488   | 0.5317        | 0.5106           | nan                | 0.4101         | 0.6533            | 0.0           | 0.4028    | 0.6437       |
| 0.0644        | 6.29  | 1800 | 0.1066          | 0.3895   | 0.5951        | 0.5790           | nan                | 0.5024         | 0.6877            | 0.0           | 0.4961    | 0.6725       |
| 0.0583        | 6.36  | 1820 | 0.1108          | 0.3356   | 0.5102        | 0.4873           | nan                | 0.3776         | 0.6429            | 0.0           | 0.3724    | 0.6344       |
| 0.0589        | 6.43  | 1840 | 0.1112          | 0.3365   | 0.5132        | 0.4847           | nan                | 0.3488         | 0.6776            | 0.0           | 0.3437    | 0.6659       |
| 0.0518        | 6.5   | 1860 | 0.1183          | 0.3093   | 0.4700        | 0.4432           | nan                | 0.3152         | 0.6247            | 0.0           | 0.3121    | 0.6158       |
| 0.0965        | 6.57  | 1880 | 0.1084          | 0.3805   | 0.5787        | 0.5669           | nan                | 0.5101         | 0.6473            | 0.0           | 0.4999    | 0.6415       |
| 0.064         | 6.64  | 1900 | 0.1244          | 0.3459   | 0.5278        | 0.5182           | nan                | 0.4723         | 0.5832            | 0.0           | 0.4568    | 0.5810       |
| 0.0831        | 6.71  | 1920 | 0.1108          | 0.3398   | 0.5184        | 0.4937           | nan                | 0.3758         | 0.6610            | 0.0           | 0.3732    | 0.6461       |
| 0.0686        | 6.78  | 1940 | 0.1194          | 0.3305   | 0.5019        | 0.4972           | nan                | 0.4748         | 0.5289            | 0.0           | 0.4654    | 0.5262       |
| 0.0984        | 6.85  | 1960 | 0.1129          | 0.3478   | 0.5284        | 0.5120           | nan                | 0.4336         | 0.6232            | 0.0           | 0.4241    | 0.6194       |
| 0.074         | 6.92  | 1980 | 0.1088          | 0.3607   | 0.5506        | 0.5286           | nan                | 0.4237         | 0.6775            | 0.0           | 0.4150    | 0.6671       |
| 0.0869        | 6.99  | 2000 | 0.1031          | 0.3727   | 0.5668        | 0.5473           | nan                | 0.4539         | 0.6798            | 0.0           | 0.4444    | 0.6737       |
| 0.0792        | 7.06  | 2020 | 0.0991          | 0.3862   | 0.5870        | 0.5678           | nan                | 0.4761         | 0.6979            | 0.0           | 0.4696    | 0.6889       |
| 0.0606        | 7.13  | 2040 | 0.1031          | 0.3684   | 0.5601        | 0.5351           | nan                | 0.4156         | 0.7045            | 0.0           | 0.4102    | 0.6950       |
| 0.073         | 7.2   | 2060 | 0.1069          | 0.3700   | 0.5620        | 0.5433           | nan                | 0.4540         | 0.6701            | 0.0           | 0.4460    | 0.6641       |
| 0.0598        | 7.27  | 2080 | 0.1148          | 0.3471   | 0.5265        | 0.5150           | nan                | 0.4598         | 0.5933            | 0.0           | 0.4517    | 0.5897       |
| 0.0789        | 7.34  | 2100 | 0.1110          | 0.3479   | 0.5341        | 0.5128           | nan                | 0.4108         | 0.6574            | 0.0           | 0.4055    | 0.6381       |
| 0.0653        | 7.41  | 2120 | 0.1201          | 0.3077   | 0.4662        | 0.4359           | nan                | 0.2910         | 0.6415            | 0.0           | 0.2886    | 0.6343       |
| 0.0682        | 7.48  | 2140 | 0.1061          | 0.3568   | 0.5426        | 0.5170           | nan                | 0.3946         | 0.6907            | 0.0           | 0.3902    | 0.6802       |
| 0.0768        | 7.55  | 2160 | 0.1121          | 0.3427   | 0.5196        | 0.4971           | nan                | 0.3900         | 0.6492            | 0.0           | 0.3843    | 0.6438       |
| 0.0551        | 7.62  | 2180 | 0.1213          | 0.3079   | 0.4665        | 0.4370           | nan                | 0.2965         | 0.6365            | 0.0           | 0.2935    | 0.6300       |
| 0.0698        | 7.69  | 2200 | 0.1057          | 0.3757   | 0.5708        | 0.5441           | nan                | 0.4163         | 0.7253            | 0.0           | 0.4094    | 0.7176       |
| 0.0444        | 7.76  | 2220 | 0.1080          | 0.3623   | 0.5489        | 0.5312           | nan                | 0.4466         | 0.6512            | 0.0           | 0.4398    | 0.6471       |
| 0.0566        | 7.83  | 2240 | 0.1119          | 0.3388   | 0.5143        | 0.4900           | nan                | 0.3737         | 0.6549            | 0.0           | 0.3690    | 0.6475       |
| 0.0581        | 7.9   | 2260 | 0.1193          | 0.3367   | 0.5109        | 0.4963           | nan                | 0.4266         | 0.5952            | 0.0           | 0.4186    | 0.5915       |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segformer-b5-finetuned-segments-crop_crack_early-lr6-350-8", "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 

segformer-b5-finetuned-segments-crop\_crack\_early-lr6-350-8
============================================================


This model is a fine-tuned version of nvidia/mit-b5 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1193
* Mean Iou: 0.3367
* Mean Accuracy: 0.5109
* Overall Accuracy: 0.4963
* Accuracy Unlabeled: nan
* Accuracy Crack: 0.4266
* Accuracy Potholes: 0.5952
* Iou Unlabeled: 0.0
* Iou Crack: 0.4186
* Iou Potholes: 0.5915


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3