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

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: 0.1049
- Mean Iou: 0.3975
- Mean Accuracy: 0.6057
- Overall Accuracy: 0.5953
- Accuracy Unlabeled: nan
- Accuracy Crack: 0.5456
- Accuracy Potholes: 0.6658
- Iou Unlabeled: 0.0
- Iou Crack: 0.5283
- Iou Potholes: 0.6641

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-----------------:|:-------------:|:---------:|:------------:|
| 0.3187        | 0.07  | 20   | 0.3997          | 0.0630   | 0.0962        | 0.0920           | nan                | 0.0720         | 0.1203            | 0.0           | 0.0707    | 0.1182       |
| 0.1572        | 0.14  | 40   | 0.1967          | 0.1256   | 0.1920        | 0.1806           | nan                | 0.1259         | 0.2582            | 0.0           | 0.1242    | 0.2524       |
| 0.1316        | 0.21  | 60   | 0.1744          | 0.1255   | 0.2012        | 0.2238           | nan                | 0.3322         | 0.0701            | 0.0           | 0.3065    | 0.0701       |
| 0.1051        | 0.28  | 80   | 0.1538          | 0.1497   | 0.2309        | 0.2448           | nan                | 0.3111         | 0.1507            | 0.0           | 0.2985    | 0.1505       |
| 0.2733        | 0.35  | 100  | 0.1587          | 0.1738   | 0.2751        | 0.2985           | nan                | 0.4104         | 0.1397            | 0.0           | 0.3816    | 0.1397       |
| 0.1544        | 0.42  | 120  | 0.1306          | 0.2941   | 0.4527        | 0.4424           | nan                | 0.3930         | 0.5124            | 0.0           | 0.3865    | 0.4957       |
| 0.2418        | 0.49  | 140  | 0.1322          | 0.2712   | 0.4150        | 0.3977           | nan                | 0.3150         | 0.5150            | 0.0           | 0.3115    | 0.5020       |
| 0.1182        | 0.56  | 160  | 0.1189          | 0.2774   | 0.4224        | 0.4091           | nan                | 0.3453         | 0.4996            | 0.0           | 0.3403    | 0.4920       |
| 0.1287        | 0.63  | 180  | 0.1263          | 0.2728   | 0.4170        | 0.4111           | nan                | 0.3828         | 0.4512            | 0.0           | 0.3675    | 0.4508       |
| 0.0895        | 0.7   | 200  | 0.1285          | 0.2252   | 0.3426        | 0.3489           | nan                | 0.3787         | 0.3065            | 0.0           | 0.3738    | 0.3017       |
| 0.1097        | 0.77  | 220  | 0.1254          | 0.2902   | 0.4440        | 0.4396           | nan                | 0.4185         | 0.4696            | 0.0           | 0.4015    | 0.4691       |
| 0.1234        | 0.84  | 240  | 0.1180          | 0.2718   | 0.4132        | 0.4029           | nan                | 0.3535         | 0.4730            | 0.0           | 0.3439    | 0.4717       |
| 0.1774        | 0.91  | 260  | 0.1170          | 0.3964   | 0.6114        | 0.5946           | nan                | 0.5145         | 0.7084            | 0.0           | 0.5040    | 0.6851       |
| 0.0754        | 0.98  | 280  | 0.1170          | 0.2904   | 0.4468        | 0.4031           | nan                | 0.1942         | 0.6994            | 0.0           | 0.1931    | 0.6781       |
| 0.1382        | 1.05  | 300  | 0.1054          | 0.3532   | 0.5403        | 0.5220           | nan                | 0.4344         | 0.6462            | 0.0           | 0.4284    | 0.6312       |
| 0.0812        | 1.12  | 320  | 0.1029          | 0.3404   | 0.5203        | 0.4872           | nan                | 0.3294         | 0.7111            | 0.0           | 0.3273    | 0.6938       |
| 0.0754        | 1.19  | 340  | 0.1113          | 0.2714   | 0.4123        | 0.4066           | nan                | 0.3794         | 0.4451            | 0.0           | 0.3698    | 0.4443       |
| 0.1153        | 1.26  | 360  | 0.1053          | 0.3478   | 0.5274        | 0.5136           | nan                | 0.4478         | 0.6070            | 0.0           | 0.4407    | 0.6028       |
| 0.1036        | 1.33  | 380  | 0.1052          | 0.3907   | 0.5977        | 0.5784           | nan                | 0.4859         | 0.7095            | 0.0           | 0.4810    | 0.6912       |
| 0.1002        | 1.4   | 400  | 0.1050          | 0.3212   | 0.4887        | 0.4820           | nan                | 0.4498         | 0.5276            | 0.0           | 0.4373    | 0.5261       |
| 0.0667        | 1.47  | 420  | 0.1145          | 0.2930   | 0.4444        | 0.4272           | nan                | 0.3453         | 0.5434            | 0.0           | 0.3374    | 0.5416       |
| 0.0789        | 1.54  | 440  | 0.1021          | 0.3482   | 0.5335        | 0.5037           | nan                | 0.3611         | 0.7060            | 0.0           | 0.3584    | 0.6861       |
| 0.1067        | 1.61  | 460  | 0.1052          | 0.3891   | 0.6020        | 0.5718           | nan                | 0.4277         | 0.7762            | 0.0           | 0.4243    | 0.7430       |
| 0.0931        | 1.68  | 480  | 0.1172          | 0.2708   | 0.4163        | 0.4196           | nan                | 0.4354         | 0.3971            | 0.0           | 0.4155    | 0.3970       |
| 0.0963        | 1.75  | 500  | 0.0984          | 0.3751   | 0.5701        | 0.5526           | nan                | 0.4688         | 0.6713            | 0.0           | 0.4637    | 0.6615       |
| 0.0787        | 1.82  | 520  | 0.0973          | 0.3590   | 0.5448        | 0.5282           | nan                | 0.4490         | 0.6406            | 0.0           | 0.4402    | 0.6367       |
| 0.0699        | 1.89  | 540  | 0.1156          | 0.3087   | 0.4707        | 0.4711           | nan                | 0.4731         | 0.4683            | 0.0           | 0.4581    | 0.4680       |
| 0.0949        | 1.96  | 560  | 0.0978          | 0.3474   | 0.5313        | 0.5081           | nan                | 0.3974         | 0.6653            | 0.0           | 0.3936    | 0.6486       |
| 0.1397        | 2.03  | 580  | 0.0969          | 0.3561   | 0.5429        | 0.5201           | nan                | 0.4112         | 0.6746            | 0.0           | 0.4067    | 0.6616       |
| 0.0749        | 2.1   | 600  | 0.0992          | 0.3904   | 0.5960        | 0.5901           | nan                | 0.5620         | 0.6299            | 0.0           | 0.5493    | 0.6220       |
| 0.0916        | 2.17  | 620  | 0.1031          | 0.4125   | 0.6294        | 0.6244           | nan                | 0.6005         | 0.6583            | 0.0           | 0.5889    | 0.6486       |
| 0.1168        | 2.24  | 640  | 0.0958          | 0.3648   | 0.5568        | 0.5369           | nan                | 0.4420         | 0.6716            | 0.0           | 0.4366    | 0.6577       |
| 0.0879        | 2.31  | 660  | 0.0963          | 0.3617   | 0.5539        | 0.5220           | nan                | 0.3698         | 0.7379            | 0.0           | 0.3657    | 0.7195       |
| 0.074         | 2.38  | 680  | 0.1027          | 0.3534   | 0.5387        | 0.5240           | nan                | 0.4541         | 0.6233            | 0.0           | 0.4458    | 0.6144       |
| 0.0448        | 2.45  | 700  | 0.0968          | 0.3591   | 0.5437        | 0.5241           | nan                | 0.4303         | 0.6571            | 0.0           | 0.4233    | 0.6540       |
| 0.1011        | 2.52  | 720  | 0.0925          | 0.3884   | 0.5908        | 0.5692           | nan                | 0.4660         | 0.7156            | 0.0           | 0.4580    | 0.7071       |
| 0.0898        | 2.59  | 740  | 0.0929          | 0.3687   | 0.5610        | 0.5424           | nan                | 0.4539         | 0.6680            | 0.0           | 0.4469    | 0.6592       |
| 0.0645        | 2.66  | 760  | 0.0920          | 0.4107   | 0.6235        | 0.6133           | nan                | 0.5648         | 0.6821            | 0.0           | 0.5560    | 0.6761       |
| 0.0657        | 2.73  | 780  | 0.0905          | 0.3793   | 0.5822        | 0.5561           | nan                | 0.4309         | 0.7336            | 0.0           | 0.4293    | 0.7087       |
| 0.0792        | 2.8   | 800  | 0.1014          | 0.3526   | 0.5358        | 0.5216           | nan                | 0.4539         | 0.6177            | 0.0           | 0.4409    | 0.6168       |
| 0.1359        | 2.87  | 820  | 0.0988          | 0.3957   | 0.6123        | 0.5772           | nan                | 0.4095         | 0.8152            | 0.0           | 0.4075    | 0.7794       |
| 0.1265        | 2.94  | 840  | 0.1329          | 0.2427   | 0.3705        | 0.3669           | nan                | 0.3497         | 0.3912            | 0.0           | 0.3372    | 0.3911       |
| 0.1051        | 3.01  | 860  | 0.0971          | 0.3935   | 0.5987        | 0.5920           | nan                | 0.5603         | 0.6371            | 0.0           | 0.5495    | 0.6311       |
| 0.1066        | 3.08  | 880  | 0.0906          | 0.3882   | 0.5888        | 0.5667           | nan                | 0.4614         | 0.7161            | 0.0           | 0.4576    | 0.7070       |
| 0.1001        | 3.15  | 900  | 0.0940          | 0.3714   | 0.5661        | 0.5318           | nan                | 0.3684         | 0.7637            | 0.0           | 0.3657    | 0.7485       |
| 0.114         | 3.22  | 920  | 0.1004          | 0.3721   | 0.5649        | 0.5571           | nan                | 0.5198         | 0.6100            | 0.0           | 0.5086    | 0.6078       |
| 0.0969        | 3.29  | 940  | 0.0960          | 0.3933   | 0.5965        | 0.5751           | nan                | 0.4728         | 0.7201            | 0.0           | 0.4664    | 0.7136       |
| 0.1018        | 3.36  | 960  | 0.0973          | 0.3314   | 0.5007        | 0.4885           | nan                | 0.4302         | 0.5712            | 0.0           | 0.4255    | 0.5686       |
| 0.1173        | 3.43  | 980  | 0.0890          | 0.4049   | 0.6205        | 0.5990           | nan                | 0.4962         | 0.7448            | 0.0           | 0.4928    | 0.7219       |
| 0.0957        | 3.5   | 1000 | 0.0941          | 0.3724   | 0.5672        | 0.5452           | nan                | 0.4404         | 0.6939            | 0.0           | 0.4346    | 0.6825       |
| 0.0976        | 3.57  | 1020 | 0.0994          | 0.3447   | 0.5219        | 0.5082           | nan                | 0.4430         | 0.6007            | 0.0           | 0.4360    | 0.5980       |
| 0.145         | 3.64  | 1040 | 0.0916          | 0.3668   | 0.5572        | 0.5300           | nan                | 0.4000         | 0.7144            | 0.0           | 0.3951    | 0.7053       |
| 0.0494        | 3.71  | 1060 | 0.0867          | 0.4152   | 0.6317        | 0.6105           | nan                | 0.5093         | 0.7540            | 0.0           | 0.5023    | 0.7432       |
| 0.0851        | 3.78  | 1080 | 0.0873          | 0.3918   | 0.5949        | 0.5719           | nan                | 0.4618         | 0.7281            | 0.0           | 0.4547    | 0.7208       |
| 0.0463        | 3.85  | 1100 | 0.0907          | 0.3686   | 0.5600        | 0.5335           | nan                | 0.4068         | 0.7131            | 0.0           | 0.4009    | 0.7048       |
| 0.0825        | 3.92  | 1120 | 0.0930          | 0.3694   | 0.5606        | 0.5369           | nan                | 0.4237         | 0.6976            | 0.0           | 0.4175    | 0.6909       |
| 0.0622        | 3.99  | 1140 | 0.0934          | 0.3848   | 0.5835        | 0.5638           | nan                | 0.4697         | 0.6974            | 0.0           | 0.4623    | 0.6919       |
| 0.0633        | 4.06  | 1160 | 0.0932          | 0.4324   | 0.6573        | 0.6438           | nan                | 0.5791         | 0.7356            | 0.0           | 0.5700    | 0.7272       |
| 0.0975        | 4.13  | 1180 | 0.0925          | 0.4221   | 0.6430        | 0.6230           | nan                | 0.5277         | 0.7584            | 0.0           | 0.5188    | 0.7474       |
| 0.0961        | 4.2   | 1200 | 0.0899          | 0.3870   | 0.5889        | 0.5640           | nan                | 0.4448         | 0.7330            | 0.0           | 0.4404    | 0.7206       |
| 0.0528        | 4.27  | 1220 | 0.0914          | 0.4008   | 0.6107        | 0.5804           | nan                | 0.4354         | 0.7860            | 0.0           | 0.4282    | 0.7742       |
| 0.0877        | 4.34  | 1240 | 0.0933          | 0.3777   | 0.5732        | 0.5536           | nan                | 0.4599         | 0.6866            | 0.0           | 0.4525    | 0.6808       |
| 0.0579        | 4.41  | 1260 | 0.0906          | 0.3926   | 0.5972        | 0.5783           | nan                | 0.4881         | 0.7064            | 0.0           | 0.4801    | 0.6977       |
| 0.1101        | 4.48  | 1280 | 0.0946          | 0.4184   | 0.6364        | 0.6313           | nan                | 0.6072         | 0.6655            | 0.0           | 0.5986    | 0.6565       |
| 0.0898        | 4.55  | 1300 | 0.0930          | 0.4115   | 0.6248        | 0.6156           | nan                | 0.5715         | 0.6781            | 0.0           | 0.5619    | 0.6726       |
| 0.0636        | 4.62  | 1320 | 0.0920          | 0.3985   | 0.6036        | 0.5852           | nan                | 0.4970         | 0.7103            | 0.0           | 0.4892    | 0.7062       |
| 0.0714        | 4.69  | 1340 | 0.0900          | 0.3977   | 0.6032        | 0.5884           | nan                | 0.5179         | 0.6885            | 0.0           | 0.5093    | 0.6837       |
| 0.0862        | 4.76  | 1360 | 0.0880          | 0.4039   | 0.6131        | 0.5891           | nan                | 0.4749         | 0.7513            | 0.0           | 0.4702    | 0.7416       |
| 0.0801        | 4.83  | 1380 | 0.0878          | 0.4142   | 0.6297        | 0.6114           | nan                | 0.5239         | 0.7355            | 0.0           | 0.5173    | 0.7254       |
| 0.0652        | 4.9   | 1400 | 0.0933          | 0.3704   | 0.5614        | 0.5336           | nan                | 0.4010         | 0.7217            | 0.0           | 0.3966    | 0.7145       |
| 0.0633        | 4.97  | 1420 | 0.0914          | 0.3790   | 0.5754        | 0.5630           | nan                | 0.5039         | 0.6469            | 0.0           | 0.4944    | 0.6427       |
| 0.0905        | 5.03  | 1440 | 0.0920          | 0.3680   | 0.5620        | 0.5331           | nan                | 0.3949         | 0.7291            | 0.0           | 0.3911    | 0.7127       |
| 0.0461        | 5.1   | 1460 | 0.0851          | 0.4118   | 0.6274        | 0.5987           | nan                | 0.4614         | 0.7934            | 0.0           | 0.4560    | 0.7795       |
| 0.0598        | 5.17  | 1480 | 0.0907          | 0.4047   | 0.6128        | 0.5942           | nan                | 0.5054         | 0.7202            | 0.0           | 0.4959    | 0.7181       |
| 0.1264        | 5.24  | 1500 | 0.1093          | 0.3601   | 0.5495        | 0.5494           | nan                | 0.5490         | 0.5501            | 0.0           | 0.5315    | 0.5487       |
| 0.065         | 5.31  | 1520 | 0.0904          | 0.4181   | 0.6369        | 0.6184           | nan                | 0.5299         | 0.7439            | 0.0           | 0.5210    | 0.7332       |
| 0.0711        | 5.38  | 1540 | 0.1021          | 0.4315   | 0.6585        | 0.6563           | nan                | 0.6460         | 0.6709            | 0.0           | 0.6259    | 0.6685       |
| 0.0726        | 5.45  | 1560 | 0.0993          | 0.3713   | 0.5609        | 0.5420           | nan                | 0.4516         | 0.6703            | 0.0           | 0.4445    | 0.6694       |
| 0.1181        | 5.52  | 1580 | 0.0957          | 0.3985   | 0.6066        | 0.5941           | nan                | 0.5346         | 0.6786            | 0.0           | 0.5300    | 0.6654       |
| 0.1049        | 5.59  | 1600 | 0.0982          | 0.4586   | 0.6967        | 0.6912           | nan                | 0.6649         | 0.7284            | 0.0           | 0.6517    | 0.7242       |
| 0.1034        | 5.66  | 1620 | 0.0877          | 0.4091   | 0.6254        | 0.5941           | nan                | 0.4448         | 0.8060            | 0.0           | 0.4409    | 0.7863       |
| 0.0745        | 5.73  | 1640 | 0.0912          | 0.3913   | 0.5938        | 0.5731           | nan                | 0.4745         | 0.7131            | 0.0           | 0.4692    | 0.7047       |
| 0.0665        | 5.8   | 1660 | 0.0861          | 0.4349   | 0.6610        | 0.6365           | nan                | 0.5196         | 0.8024            | 0.0           | 0.5142    | 0.7906       |
| 0.0836        | 5.87  | 1680 | 0.0953          | 0.3982   | 0.6031        | 0.5838           | nan                | 0.4912         | 0.7151            | 0.0           | 0.4818    | 0.7128       |
| 0.0765        | 5.94  | 1700 | 0.0899          | 0.3915   | 0.5976        | 0.5774           | nan                | 0.4811         | 0.7141            | 0.0           | 0.4755    | 0.6990       |
| 0.0456        | 6.01  | 1720 | 0.1060          | 0.3429   | 0.5206        | 0.4982           | nan                | 0.3908         | 0.6504            | 0.0           | 0.3796    | 0.6493       |
| 0.0939        | 6.08  | 1740 | 0.0892          | 0.4107   | 0.6285        | 0.5976           | nan                | 0.4499         | 0.8072            | 0.0           | 0.4459    | 0.7861       |
| 0.0756        | 6.15  | 1760 | 0.0867          | 0.4091   | 0.6207        | 0.5969           | nan                | 0.4831         | 0.7582            | 0.0           | 0.4765    | 0.7507       |
| 0.0435        | 6.22  | 1780 | 0.0898          | 0.3919   | 0.5929        | 0.5660           | nan                | 0.4374         | 0.7484            | 0.0           | 0.4308    | 0.7449       |
| 0.0673        | 6.29  | 1800 | 0.0861          | 0.4474   | 0.6795        | 0.6569           | nan                | 0.5489         | 0.8100            | 0.0           | 0.5424    | 0.7998       |
| 0.0499        | 6.36  | 1820 | 0.0877          | 0.3879   | 0.5923        | 0.5593           | nan                | 0.4014         | 0.7832            | 0.0           | 0.3981    | 0.7655       |
| 0.0532        | 6.43  | 1840 | 0.0889          | 0.4420   | 0.6742        | 0.6438           | nan                | 0.4987         | 0.8496            | 0.0           | 0.4933    | 0.8328       |
| 0.0483        | 6.5   | 1860 | 0.0907          | 0.4078   | 0.6178        | 0.5928           | nan                | 0.4737         | 0.7619            | 0.0           | 0.4677    | 0.7558       |
| 0.0886        | 6.57  | 1880 | 0.0938          | 0.4683   | 0.7105        | 0.6990           | nan                | 0.6441         | 0.7769            | 0.0           | 0.6309    | 0.7739       |
| 0.0603        | 6.64  | 1900 | 0.0883          | 0.4295   | 0.6553        | 0.6282           | nan                | 0.4989         | 0.8117            | 0.0           | 0.4951    | 0.7935       |
| 0.0844        | 6.71  | 1920 | 0.0917          | 0.4115   | 0.6241        | 0.6039           | nan                | 0.5076         | 0.7406            | 0.0           | 0.5017    | 0.7327       |
| 0.0688        | 6.78  | 1940 | 0.0910          | 0.4314   | 0.6549        | 0.6359           | nan                | 0.5451         | 0.7647            | 0.0           | 0.5372    | 0.7570       |
| 0.0863        | 6.85  | 1960 | 0.0886          | 0.3809   | 0.5779        | 0.5517           | nan                | 0.4265         | 0.7293            | 0.0           | 0.4220    | 0.7208       |
| 0.0744        | 6.92  | 1980 | 0.0904          | 0.3983   | 0.6038        | 0.5781           | nan                | 0.4554         | 0.7521            | 0.0           | 0.4483    | 0.7465       |
| 0.0836        | 6.99  | 2000 | 0.0878          | 0.4239   | 0.6440        | 0.6216           | nan                | 0.5149         | 0.7731            | 0.0           | 0.5091    | 0.7625       |
| 0.0677        | 7.06  | 2020 | 0.0892          | 0.4554   | 0.6921        | 0.6757           | nan                | 0.5973         | 0.7868            | 0.0           | 0.5898    | 0.7763       |
| 0.0601        | 7.13  | 2040 | 0.0862          | 0.4311   | 0.6546        | 0.6354           | nan                | 0.5435         | 0.7657            | 0.0           | 0.5382    | 0.7551       |
| 0.0682        | 7.2   | 2060 | 0.1049          | 0.3975   | 0.6057        | 0.5953           | nan                | 0.5456         | 0.6658            | 0.0           | 0.5283    | 0.6641       |


### 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-lr6-8", "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-lr6-8
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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: 0.1049
* Mean Iou: 0.3975
* Mean Accuracy: 0.6057
* Overall Accuracy: 0.5953
* Accuracy Unlabeled: nan
* Accuracy Crack: 0.5456
* Accuracy Potholes: 0.6658
* Iou Unlabeled: 0.0
* Iou Crack: 0.5283
* Iou Potholes: 0.6641


Model description
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More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
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More information needed


Training procedure
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### 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