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rowbody-4cats-outputs

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metadata
license: other
tags:
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-segments-rowbody-4cats
    results: []

segformer-b0-finetuned-segments-rowbody-4cats

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: 0.1486
  • Mean Iou: 0.6527
  • Mean Accuracy: 0.9381
  • Overall Accuracy: 0.9558
  • Accuracy Sleeve-right: nan
  • Accuracy Sleeve-left: 0.9259
  • Accuracy Neck: 0.9212
  • Accuracy Body: 0.9670
  • Iou Sleeve-right: 0.0
  • Iou Sleeve-left: 0.9012
  • Iou Neck: 0.7545
  • Iou Body: 0.9551

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Sleeve-right Accuracy Sleeve-left Accuracy Neck Accuracy Body Iou Sleeve-right Iou Sleeve-left Iou Neck Iou Body
0.9629 2.5 20 1.1228 0.2325 0.3900 0.7680 nan 0.1708 0.0 0.9991 0.0 0.1703 0.0 0.7598
0.6667 5.0 40 0.6465 0.4513 0.6646 0.8681 nan 0.5731 0.4337 0.9870 0.0 0.5659 0.3911 0.8483
0.414 7.5 60 0.4340 0.5935 0.8506 0.9377 nan 0.8496 0.7215 0.9806 0.0 0.8374 0.6136 0.9228
0.3351 10.0 80 0.3371 0.6263 0.9237 0.9411 nan 0.9325 0.8909 0.9478 0.0 0.8568 0.7171 0.9314
0.4849 12.5 100 0.3146 0.6428 0.9438 0.9526 nan 0.9708 0.9093 0.9513 0.0 0.8922 0.7375 0.9415
0.2461 15.0 120 0.2716 0.6431 0.9554 0.9496 nan 0.9717 0.9511 0.9434 0.0 0.8938 0.7410 0.9373
0.2123 17.5 140 0.2477 0.6715 0.9481 0.9679 nan 0.9523 0.9151 0.9768 0.0 0.9229 0.7999 0.9632
0.1827 20.0 160 0.2413 0.6620 0.9048 0.9649 nan 0.9200 0.8032 0.9912 0.0 0.9147 0.7717 0.9617
0.2828 22.5 180 0.2286 0.6484 0.9472 0.9532 nan 0.9622 0.9265 0.9530 0.0 0.8996 0.7495 0.9443
0.4631 25.0 200 0.2137 0.6459 0.9452 0.9485 nan 0.9523 0.9345 0.9486 0.0 0.8886 0.7543 0.9408
0.159 27.5 220 0.1854 0.6336 0.9374 0.9415 nan 0.9355 0.9328 0.9440 0.0 0.8832 0.7145 0.9368
0.1361 30.0 240 0.1760 0.6563 0.9429 0.9576 nan 0.9492 0.9161 0.9635 0.0 0.9027 0.7687 0.9538
0.1369 32.5 260 0.1634 0.6516 0.9451 0.9562 nan 0.9497 0.9249 0.9607 0.0 0.9130 0.7426 0.9507
0.1356 35.0 280 0.1580 0.6488 0.9248 0.9527 nan 0.9154 0.8905 0.9684 0.0 0.8966 0.7457 0.9528
0.2415 37.5 300 0.1635 0.6457 0.9439 0.9523 nan 0.9406 0.9340 0.9571 0.0 0.9003 0.7344 0.9482
0.1183 40.0 320 0.1639 0.6463 0.9386 0.9524 nan 0.9286 0.9262 0.9612 0.0 0.8998 0.7351 0.9500
0.1264 42.5 340 0.1564 0.6512 0.9439 0.9552 nan 0.9385 0.9312 0.9618 0.0 0.9066 0.7463 0.9521
0.2418 45.0 360 0.1553 0.6569 0.9366 0.9589 nan 0.9198 0.9166 0.9734 0.0 0.9033 0.7656 0.9587
0.189 47.5 380 0.1611 0.6520 0.9386 0.9544 nan 0.9284 0.9231 0.9643 0.0 0.8990 0.7554 0.9536
0.1132 50.0 400 0.1486 0.6527 0.9381 0.9558 nan 0.9259 0.9212 0.9670 0.0 0.9012 0.7545 0.9551

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2