vimassaru/segformer-b0-finetuned-teeth-segmentation

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segformer-b0-finetuned-segments-sidewalk-oct-22

This model is a fine-tuned version of nvidia/mit-b0 on the vimassaru/teethsegmentation dataset. It achieves the following results on the evaluation set:

• —Loss: 0.1880
• —Mean Iou: 0.7311
• —Mean Accuracy: 0.8106
• —Overall Accuracy: 0.8184
• —Accuracy Background: nan
• —Accuracy 11: 0.8456
• —Accuracy 12: 0.8351
• —Accuracy 13: 0.8619
• —Accuracy 14: 0.8112
• —Accuracy 15: 0.8087
• —Accuracy 16: 0.9141
• —Accuracy 17: 0.8742
• —Accuracy 18: 0.7394
• —Accuracy 21: 0.8758
• —Accuracy 22: 0.8579
• —Accuracy 23: 0.8480
• —Accuracy 24: 0.7169
• —Accuracy 25: 0.8273
• —Accuracy 26: 0.8481
• —Accuracy 27: 0.8284
• —Accuracy 28: 0.7298
• —Accuracy 31: 0.7495
• —Accuracy 32: 0.7987
• —Accuracy 33: 0.8661
• —Accuracy 34: 0.8392
• —Accuracy 35: 0.7596
• —Accuracy 36: 0.7482
• —Accuracy 37: 0.8109
• —Accuracy 38: 0.7016
• —Accuracy 41: 0.7217
• —Accuracy 42: 0.7480
• —Accuracy 43: 0.8447
• —Accuracy 44: 0.7868
• —Accuracy 45: 0.8250
• —Accuracy 46: 0.8762
• —Accuracy 47: 0.8519
• —Accuracy 48: 0.7878
• —Iou Background: 0.0
• —Iou 11: 0.8226
• —Iou 12: 0.8155
• —Iou 13: 0.8048
• —Iou 14: 0.7807
• —Iou 15: 0.7909
• —Iou 16: 0.8609
• —Iou 17: 0.8145
• —Iou 18: 0.6999
• —Iou 21: 0.8266
• —Iou 22: 0.8160
• —Iou 23: 0.8000
• —Iou 24: 0.6900
• —Iou 25: 0.7760
• —Iou 26: 0.8065
• —Iou 27: 0.7338
• —Iou 28: 0.6771
• —Iou 31: 0.6604
• —Iou 32: 0.7394
• —Iou 33: 0.7977
• —Iou 34: 0.7577
• —Iou 35: 0.6944
• —Iou 36: 0.6774
• —Iou 37: 0.7224
• —Iou 38: 0.6099
• —Iou 41: 0.6166
• —Iou 42: 0.6741
• —Iou 43: 0.7706
• —Iou 44: 0.7386
• —Iou 45: 0.7555
• —Iou 46: 0.8271
• —Iou 47: 0.8210
• —Iou 48: 0.7466

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: 0.0006
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 200

Training results

Framework versions

• —Transformers 4.30.2
• —Pytorch 2.0.1+cu118
• —Datasets 2.13.1
• —Tokenizers 0.13.3