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# segformer-b5-finetuned-segments-crop_crack_early-lr8-350-8

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: 0.1041
- Mean Iou: 0.3648
- Mean Accuracy: 0.5529
- Overall Accuracy: 0.5298
- Accuracy Unlabeled: nan
- Accuracy Crack: 0.4190
- Accuracy Potholes: 0.6869
- Iou Unlabeled: 0.0
- Iou Crack: 0.4137
- Iou Potholes: 0.6809

## 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: 8e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-----------------:|:-------------:|:---------:|:------------:|
| 0.4007        | 0.07  | 20   | 0.3652          | 0.0624   | 0.0949        | 0.0850           | nan                | 0.0374         | 0.1524            | 0.0           | 0.0372    | 0.1501       |
| 0.1665        | 0.14  | 40   | 0.2121          | 0.1691   | 0.2604        | 0.2421           | nan                | 0.1550         | 0.3657            | 0.0           | 0.1517    | 0.3555       |
| 0.1145        | 0.21  | 60   | 0.1806          | 0.1182   | 0.1856        | 0.2049           | nan                | 0.2972         | 0.0740            | 0.0           | 0.2807    | 0.0739       |
| 0.0835        | 0.28  | 80   | 0.1521          | 0.2059   | 0.3151        | 0.3161           | nan                | 0.3207         | 0.3095            | 0.0           | 0.3104    | 0.3073       |
| 0.2789        | 0.35  | 100  | 0.1636          | 0.1249   | 0.1951        | 0.2099           | nan                | 0.2803         | 0.1100            | 0.0           | 0.2648    | 0.1099       |
| 0.1435        | 0.42  | 120  | 0.1490          | 0.2611   | 0.4122        | 0.4119           | nan                | 0.4104         | 0.4139            | 0.0           | 0.4062    | 0.3772       |
| 0.2562        | 0.49  | 140  | 0.1370          | 0.2293   | 0.3530        | 0.3499           | nan                | 0.3350         | 0.3711            | 0.0           | 0.3265    | 0.3613       |
| 0.1211        | 0.56  | 160  | 0.1310          | 0.2644   | 0.4043        | 0.3979           | nan                | 0.3674         | 0.4411            | 0.0           | 0.3551    | 0.4381       |
| 0.1379        | 0.63  | 180  | 0.1331          | 0.3149   | 0.4811        | 0.4793           | nan                | 0.4704         | 0.4918            | 0.0           | 0.4542    | 0.4907       |
| 0.1017        | 0.7   | 200  | 0.1477          | 0.1476   | 0.2284        | 0.2249           | nan                | 0.2079         | 0.2489            | 0.0           | 0.2062    | 0.2367       |
| 0.1201        | 0.77  | 220  | 0.1340          | 0.3514   | 0.5395        | 0.5410           | nan                | 0.5482         | 0.5307            | 0.0           | 0.5278    | 0.5262       |
| 0.1098        | 0.84  | 240  | 0.1263          | 0.3347   | 0.5155        | 0.4942           | nan                | 0.3926         | 0.6384            | 0.0           | 0.3861    | 0.6181       |
| 0.1992        | 0.91  | 260  | 0.1235          | 0.3532   | 0.5454        | 0.5258           | nan                | 0.4320         | 0.6588            | 0.0           | 0.4261    | 0.6334       |
| 0.0827        | 0.98  | 280  | 0.1295          | 0.2970   | 0.4775        | 0.4428           | nan                | 0.2768         | 0.6782            | 0.0           | 0.2762    | 0.6147       |
| 0.1425        | 1.05  | 300  | 0.1229          | 0.3292   | 0.5049        | 0.5057           | nan                | 0.5095         | 0.5003            | 0.0           | 0.4962    | 0.4915       |
| 0.0923        | 1.12  | 320  | 0.1092          | 0.3527   | 0.5398        | 0.5058           | nan                | 0.3433         | 0.7363            | 0.0           | 0.3395    | 0.7186       |
| 0.0869        | 1.19  | 340  | 0.1298          | 0.2323   | 0.3519        | 0.3558           | nan                | 0.3746         | 0.3292            | 0.0           | 0.3695    | 0.3275       |
| 0.1178        | 1.26  | 360  | 0.1221          | 0.2744   | 0.4174        | 0.4113           | nan                | 0.3819         | 0.4530            | 0.0           | 0.3727    | 0.4506       |
| 0.1186        | 1.33  | 380  | 0.1162          | 0.2850   | 0.4392        | 0.4270           | nan                | 0.3688         | 0.5096            | 0.0           | 0.3651    | 0.4899       |
| 0.1096        | 1.4   | 400  | 0.1152          | 0.3531   | 0.5435        | 0.5225           | nan                | 0.4224         | 0.6645            | 0.0           | 0.4160    | 0.6435       |
| 0.0721        | 1.47  | 420  | 0.1093          | 0.3263   | 0.4962        | 0.4784           | nan                | 0.3936         | 0.5987            | 0.0           | 0.3852    | 0.5937       |
| 0.0771        | 1.54  | 440  | 0.1220          | 0.2754   | 0.4189        | 0.4082           | nan                | 0.3571         | 0.4808            | 0.0           | 0.3485    | 0.4779       |
| 0.1211        | 1.61  | 460  | 0.1183          | 0.3253   | 0.5003        | 0.4915           | nan                | 0.4495         | 0.5511            | 0.0           | 0.4350    | 0.5411       |
| 0.1116        | 1.68  | 480  | 0.1151          | 0.2657   | 0.4045        | 0.3948           | nan                | 0.3487         | 0.4602            | 0.0           | 0.3376    | 0.4595       |
| 0.0958        | 1.75  | 500  | 0.1086          | 0.3547   | 0.5430        | 0.5300           | nan                | 0.4682         | 0.6177            | 0.0           | 0.4621    | 0.6021       |
| 0.0861        | 1.82  | 520  | 0.1115          | 0.3653   | 0.5563        | 0.5405           | nan                | 0.4651         | 0.6476            | 0.0           | 0.4543    | 0.6415       |
| 0.0792        | 1.89  | 540  | 0.1236          | 0.3061   | 0.4694        | 0.4720           | nan                | 0.4843         | 0.4545            | 0.0           | 0.4668    | 0.4516       |
| 0.1062        | 1.96  | 560  | 0.1381          | 0.2473   | 0.3812        | 0.3945           | nan                | 0.4581         | 0.3042            | 0.0           | 0.4380    | 0.3039       |
| 0.1673        | 2.03  | 580  | 0.1208          | 0.2577   | 0.4016        | 0.3795           | nan                | 0.2737         | 0.5296            | 0.0           | 0.2708    | 0.5024       |
| 0.0792        | 2.1   | 600  | 0.1131          | 0.3264   | 0.4979        | 0.4946           | nan                | 0.4790         | 0.5168            | 0.0           | 0.4685    | 0.5108       |
| 0.0746        | 2.17  | 620  | 0.1110          | 0.3276   | 0.4999        | 0.5010           | nan                | 0.5063         | 0.4935            | 0.0           | 0.4986    | 0.4844       |
| 0.1398        | 2.24  | 640  | 0.1144          | 0.2616   | 0.3995        | 0.3822           | nan                | 0.2998         | 0.4992            | 0.0           | 0.2970    | 0.4878       |
| 0.0998        | 2.31  | 660  | 0.1018          | 0.3241   | 0.4946        | 0.4600           | nan                | 0.2951         | 0.6940            | 0.0           | 0.2921    | 0.6803       |
| 0.0804        | 2.38  | 680  | 0.1070          | 0.3108   | 0.4730        | 0.4570           | nan                | 0.3806         | 0.5654            | 0.0           | 0.3730    | 0.5594       |
| 0.0467        | 2.45  | 700  | 0.1154          | 0.2576   | 0.3901        | 0.3715           | nan                | 0.2828         | 0.4974            | 0.0           | 0.2782    | 0.4946       |
| 0.1093        | 2.52  | 720  | 0.1035          | 0.3819   | 0.5911        | 0.5641           | nan                | 0.4353         | 0.7469            | 0.0           | 0.4326    | 0.7132       |
| 0.0975        | 2.59  | 740  | 0.1105          | 0.3186   | 0.4839        | 0.4634           | nan                | 0.3658         | 0.6019            | 0.0           | 0.3570    | 0.5990       |
| 0.073         | 2.66  | 760  | 0.0997          | 0.3789   | 0.5831        | 0.5574           | nan                | 0.4348         | 0.7314            | 0.0           | 0.4308    | 0.7058       |
| 0.0707        | 2.73  | 780  | 0.1029          | 0.3297   | 0.5169        | 0.4728           | nan                | 0.2621         | 0.7716            | 0.0           | 0.2619    | 0.7273       |
| 0.0773        | 2.8   | 800  | 0.1080          | 0.3066   | 0.4655        | 0.4518           | nan                | 0.3860         | 0.5451            | 0.0           | 0.3773    | 0.5424       |
| 0.1331        | 2.87  | 820  | 0.0986          | 0.3536   | 0.5407        | 0.5157           | nan                | 0.3962         | 0.6852            | 0.0           | 0.3915    | 0.6693       |
| 0.1179        | 2.94  | 840  | 0.1160          | 0.2542   | 0.3856        | 0.3731           | nan                | 0.3131         | 0.4581            | 0.0           | 0.3051    | 0.4576       |
| 0.107         | 3.01  | 860  | 0.1082          | 0.3336   | 0.5072        | 0.5030           | nan                | 0.4831         | 0.5313            | 0.0           | 0.4726    | 0.5282       |
| 0.1113        | 3.08  | 880  | 0.1055          | 0.3116   | 0.4874        | 0.4569           | nan                | 0.3110         | 0.6639            | 0.0           | 0.3102    | 0.6247       |
| 0.1252        | 3.15  | 900  | 0.1177          | 0.2671   | 0.4057        | 0.3930           | nan                | 0.3322         | 0.4792            | 0.0           | 0.3285    | 0.4727       |
| 0.1175        | 3.22  | 920  | 0.1033          | 0.3595   | 0.5479        | 0.5346           | nan                | 0.4709         | 0.6249            | 0.0           | 0.4618    | 0.6166       |
| 0.1227        | 3.29  | 940  | 0.1054          | 0.3316   | 0.5022        | 0.4793           | nan                | 0.3696         | 0.6349            | 0.0           | 0.3640    | 0.6307       |
| 0.112         | 3.36  | 960  | 0.1165          | 0.3649   | 0.5604        | 0.5391           | nan                | 0.4373         | 0.6836            | 0.0           | 0.4256    | 0.6692       |
| 0.1261        | 3.43  | 980  | 0.1025          | 0.3636   | 0.5579        | 0.5369           | nan                | 0.4365         | 0.6793            | 0.0           | 0.4294    | 0.6616       |
| 0.0946        | 3.5   | 1000 | 0.1065          | 0.3146   | 0.4856        | 0.4603           | nan                | 0.3395         | 0.6317            | 0.0           | 0.3364    | 0.6075       |
| 0.1034        | 3.57  | 1020 | 0.1142          | 0.2993   | 0.4536        | 0.4347           | nan                | 0.3444         | 0.5628            | 0.0           | 0.3388    | 0.5589       |
| 0.148         | 3.64  | 1040 | 0.1038          | 0.3243   | 0.4949        | 0.4704           | nan                | 0.3534         | 0.6363            | 0.0           | 0.3493    | 0.6234       |
| 0.0485        | 3.71  | 1060 | 0.1084          | 0.3227   | 0.4911        | 0.4750           | nan                | 0.3979         | 0.5842            | 0.0           | 0.3895    | 0.5786       |
| 0.0977        | 3.78  | 1080 | 0.1158          | 0.2886   | 0.4387        | 0.4213           | nan                | 0.3378         | 0.5396            | 0.0           | 0.3305    | 0.5352       |
| 0.0483        | 3.85  | 1100 | 0.1095          | 0.3233   | 0.4906        | 0.4659           | nan                | 0.3480         | 0.6331            | 0.0           | 0.3406    | 0.6295       |
| 0.0904        | 3.92  | 1120 | 0.1191          | 0.2858   | 0.4341        | 0.4219           | nan                | 0.3637         | 0.5046            | 0.0           | 0.3539    | 0.5034       |
| 0.0652        | 3.99  | 1140 | 0.1077          | 0.3449   | 0.5275        | 0.5087           | nan                | 0.4189         | 0.6361            | 0.0           | 0.4110    | 0.6237       |
| 0.0647        | 4.06  | 1160 | 0.1184          | 0.3120   | 0.4741        | 0.4731           | nan                | 0.4681         | 0.4802            | 0.0           | 0.4572    | 0.4788       |
| 0.1083        | 4.13  | 1180 | 0.1026          | 0.3622   | 0.5591        | 0.5305           | nan                | 0.3940         | 0.7243            | 0.0           | 0.3900    | 0.6964       |
| 0.097         | 4.2   | 1200 | 0.1150          | 0.3055   | 0.4638        | 0.4407           | nan                | 0.3302         | 0.5974            | 0.0           | 0.3252    | 0.5913       |
| 0.0577        | 4.27  | 1220 | 0.0989          | 0.3769   | 0.5744        | 0.5452           | nan                | 0.4059         | 0.7429            | 0.0           | 0.3988    | 0.7319       |
| 0.0905        | 4.34  | 1240 | 0.1048          | 0.3180   | 0.4861        | 0.4615           | nan                | 0.3442         | 0.6280            | 0.0           | 0.3425    | 0.6115       |
| 0.0622        | 4.41  | 1260 | 0.0958          | 0.3764   | 0.5772        | 0.5470           | nan                | 0.4029         | 0.7515            | 0.0           | 0.3989    | 0.7304       |
| 0.1154        | 4.48  | 1280 | 0.1102          | 0.3525   | 0.5393        | 0.5386           | nan                | 0.5352         | 0.5435            | 0.0           | 0.5186    | 0.5390       |
| 0.1005        | 4.55  | 1300 | 0.1081          | 0.3593   | 0.5496        | 0.5437           | nan                | 0.5154         | 0.5838            | 0.0           | 0.5040    | 0.5739       |
| 0.0698        | 4.62  | 1320 | 0.1156          | 0.3279   | 0.4992        | 0.4926           | nan                | 0.4615         | 0.5368            | 0.0           | 0.4487    | 0.5349       |
| 0.0758        | 4.69  | 1340 | 0.1124          | 0.3384   | 0.5161        | 0.5110           | nan                | 0.4866         | 0.5457            | 0.0           | 0.4734    | 0.5417       |
| 0.0997        | 4.76  | 1360 | 0.1065          | 0.3157   | 0.4800        | 0.4559           | nan                | 0.3405         | 0.6196            | 0.0           | 0.3362    | 0.6109       |
| 0.0878        | 4.83  | 1380 | 0.1015          | 0.3561   | 0.5424        | 0.5249           | nan                | 0.4412         | 0.6437            | 0.0           | 0.4330    | 0.6354       |
| 0.0707        | 4.9   | 1400 | 0.1002          | 0.3398   | 0.5182        | 0.4862           | nan                | 0.3331         | 0.7033            | 0.0           | 0.3297    | 0.6897       |
| 0.0649        | 4.97  | 1420 | 0.1267          | 0.3123   | 0.4773        | 0.4715           | nan                | 0.4438         | 0.5107            | 0.0           | 0.4270    | 0.5100       |
| 0.0972        | 5.03  | 1440 | 0.1053          | 0.3397   | 0.5248        | 0.4902           | nan                | 0.3251         | 0.7245            | 0.0           | 0.3242    | 0.6950       |
| 0.0572        | 5.1   | 1460 | 0.0991          | 0.3752   | 0.5773        | 0.5486           | nan                | 0.4115         | 0.7431            | 0.0           | 0.4087    | 0.7170       |
| 0.0708        | 5.17  | 1480 | 0.0969          | 0.3593   | 0.5495        | 0.5186           | nan                | 0.3712         | 0.7277            | 0.0           | 0.3667    | 0.7111       |
| 0.1332        | 5.24  | 1500 | 0.0984          | 0.3562   | 0.5433        | 0.5243           | nan                | 0.4340         | 0.6525            | 0.0           | 0.4277    | 0.6409       |
| 0.0694        | 5.31  | 1520 | 0.0972          | 0.3703   | 0.5649        | 0.5440           | nan                | 0.4438         | 0.6860            | 0.0           | 0.4370    | 0.6738       |
| 0.08          | 5.38  | 1540 | 0.0982          | 0.3846   | 0.5857        | 0.5671           | nan                | 0.4783         | 0.6930            | 0.0           | 0.4711    | 0.6826       |
| 0.0813        | 5.45  | 1560 | 0.0966          | 0.3769   | 0.5737        | 0.5484           | nan                | 0.4276         | 0.7198            | 0.0           | 0.4237    | 0.7071       |
| 0.1169        | 5.52  | 1580 | 0.1064          | 0.3271   | 0.4972        | 0.4849           | nan                | 0.4260         | 0.5683            | 0.0           | 0.4149    | 0.5663       |
| 0.0689        | 5.59  | 1600 | 0.1038          | 0.3670   | 0.5610        | 0.5462           | nan                | 0.4756         | 0.6463            | 0.0           | 0.4672    | 0.6339       |
| 0.0999        | 5.66  | 1620 | 0.1013          | 0.3647   | 0.5576        | 0.5412           | nan                | 0.4630         | 0.6522            | 0.0           | 0.4536    | 0.6406       |
| 0.0787        | 5.73  | 1640 | 0.1090          | 0.3247   | 0.4924        | 0.4720           | nan                | 0.3749         | 0.6098            | 0.0           | 0.3668    | 0.6073       |
| 0.0715        | 5.8   | 1660 | 0.1099          | 0.3695   | 0.5636        | 0.5551           | nan                | 0.5147         | 0.6124            | 0.0           | 0.4976    | 0.6110       |
| 0.0926        | 5.87  | 1680 | 0.0988          | 0.3825   | 0.5837        | 0.5581           | nan                | 0.4357         | 0.7316            | 0.0           | 0.4296    | 0.7178       |
| 0.0884        | 5.94  | 1700 | 0.1006          | 0.3566   | 0.5411        | 0.5199           | nan                | 0.4188         | 0.6634            | 0.0           | 0.4138    | 0.6561       |
| 0.0552        | 6.01  | 1720 | 0.1058          | 0.3475   | 0.5279        | 0.4992           | nan                | 0.3620         | 0.6937            | 0.0           | 0.3529    | 0.6896       |
| 0.0878        | 6.08  | 1740 | 0.1020          | 0.3471   | 0.5284        | 0.5012           | nan                | 0.3711         | 0.6857            | 0.0           | 0.3651    | 0.6763       |
| 0.0887        | 6.15  | 1760 | 0.1062          | 0.3315   | 0.5034        | 0.4784           | nan                | 0.3591         | 0.6477            | 0.0           | 0.3513    | 0.6431       |
| 0.0475        | 6.22  | 1780 | 0.1019          | 0.3594   | 0.5464        | 0.5227           | nan                | 0.4094         | 0.6834            | 0.0           | 0.4024    | 0.6756       |
| 0.0659        | 6.29  | 1800 | 0.0989          | 0.4000   | 0.6156        | 0.5872           | nan                | 0.4512         | 0.7800            | 0.0           | 0.4483    | 0.7517       |
| 0.0565        | 6.36  | 1820 | 0.1119          | 0.3080   | 0.4690        | 0.4436           | nan                | 0.3221         | 0.6159            | 0.0           | 0.3187    | 0.6053       |
| 0.0598        | 6.43  | 1840 | 0.1119          | 0.3122   | 0.4755        | 0.4506           | nan                | 0.3320         | 0.6190            | 0.0           | 0.3276    | 0.6089       |
| 0.0498        | 6.5   | 1860 | 0.1142          | 0.2978   | 0.4520        | 0.4271           | nan                | 0.3079         | 0.5962            | 0.0           | 0.3051    | 0.5883       |
| 0.0968        | 6.57  | 1880 | 0.1041          | 0.3703   | 0.5638        | 0.5481           | nan                | 0.4731         | 0.6545            | 0.0           | 0.4669    | 0.6440       |
| 0.0648        | 6.64  | 1900 | 0.1073          | 0.3697   | 0.5627        | 0.5423           | nan                | 0.4449         | 0.6804            | 0.0           | 0.4356    | 0.6735       |
| 0.0892        | 6.71  | 1920 | 0.1095          | 0.3276   | 0.4978        | 0.4720           | nan                | 0.3490         | 0.6465            | 0.0           | 0.3442    | 0.6386       |
| 0.066         | 6.78  | 1940 | 0.1111          | 0.3513   | 0.5344        | 0.5296           | nan                | 0.5066         | 0.5623            | 0.0           | 0.4972    | 0.5566       |
| 0.0921        | 6.85  | 1960 | 0.1050          | 0.3576   | 0.5453        | 0.5285           | nan                | 0.4481         | 0.6425            | 0.0           | 0.4406    | 0.6322       |
| 0.0762        | 6.92  | 1980 | 0.1066          | 0.3585   | 0.5476        | 0.5281           | nan                | 0.4354         | 0.6597            | 0.0           | 0.4248    | 0.6507       |
| 0.0874        | 6.99  | 2000 | 0.1056          | 0.3390   | 0.5168        | 0.4934           | nan                | 0.3816         | 0.6519            | 0.0           | 0.3753    | 0.6418       |
| 0.0775        | 7.06  | 2020 | 0.1010          | 0.3632   | 0.5550        | 0.5372           | nan                | 0.4521         | 0.6579            | 0.0           | 0.4459    | 0.6438       |
| 0.0578        | 7.13  | 2040 | 0.1033          | 0.3673   | 0.5603        | 0.5442           | nan                | 0.4675         | 0.6531            | 0.0           | 0.4588    | 0.6432       |
| 0.0722        | 7.2   | 2060 | 0.1048          | 0.3518   | 0.5356        | 0.5161           | nan                | 0.4228         | 0.6485            | 0.0           | 0.4167    | 0.6387       |
| 0.0599        | 7.27  | 2080 | 0.1097          | 0.3453   | 0.5261        | 0.5100           | nan                | 0.4328         | 0.6195            | 0.0           | 0.4256    | 0.6102       |
| 0.0798        | 7.34  | 2100 | 0.1064          | 0.3494   | 0.5371        | 0.5139           | nan                | 0.4030         | 0.6711            | 0.0           | 0.3982    | 0.6499       |
| 0.0671        | 7.41  | 2120 | 0.1288          | 0.2880   | 0.4355        | 0.4143           | nan                | 0.3132         | 0.5578            | 0.0           | 0.3084    | 0.5555       |
| 0.0688        | 7.48  | 2140 | 0.1080          | 0.3426   | 0.5207        | 0.4964           | nan                | 0.3800         | 0.6614            | 0.0           | 0.3740    | 0.6538       |
| 0.0772        | 7.55  | 2160 | 0.1409          | 0.2690   | 0.4090        | 0.4058           | nan                | 0.3906         | 0.4275            | 0.0           | 0.3805    | 0.4266       |
| 0.0542        | 7.62  | 2180 | 0.1142          | 0.3239   | 0.4905        | 0.4707           | nan                | 0.3763         | 0.6047            | 0.0           | 0.3697    | 0.6020       |
| 0.0699        | 7.69  | 2200 | 0.1046          | 0.3775   | 0.5771        | 0.5432           | nan                | 0.3808         | 0.7734            | 0.0           | 0.3758    | 0.7567       |
| 0.0484        | 7.76  | 2220 | 0.1163          | 0.3437   | 0.5225        | 0.5123           | nan                | 0.4634         | 0.5816            | 0.0           | 0.4525    | 0.5786       |
| 0.0581        | 7.83  | 2240 | 0.1020          | 0.3700   | 0.5653        | 0.5367           | nan                | 0.4002         | 0.7304            | 0.0           | 0.3966    | 0.7135       |
| 0.0565        | 7.9   | 2260 | 0.1041          | 0.3648   | 0.5529        | 0.5298           | nan                | 0.4190         | 0.6869            | 0.0           | 0.4137    | 0.6809       |


### 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-lr8-350-8", "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-lr8-350-8
============================================================


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: 0.1041
* Mean Iou: 0.3648
* Mean Accuracy: 0.5529
* Overall Accuracy: 0.5298
* Accuracy Unlabeled: nan
* Accuracy Crack: 0.4190
* Accuracy Potholes: 0.6869
* Iou Unlabeled: 0.0
* Iou Crack: 0.4137
* Iou Potholes: 0.6809


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: 8e-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: 8e-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: 8e-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