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segformer-b1-finetuned-Hiking

Image Segmentation
Transformers
PyTorch
segformer
vision
Generated from Trainer
Inference Endpoints
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segformer-b1-finetuned-Hiking / README.md
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metadata
license: other
base_model: nvidia/mit-b1
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b1-finetuned-Hiking
    results: []

segformer-b1-finetuned-Hiking

This model is a fine-tuned version of nvidia/mit-b1 on the twdent/Hiking dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1129
  • Mean Iou: 0.6261
  • Mean Accuracy: 0.9707
  • Overall Accuracy: 0.9700
  • Accuracy Unlabeled: nan
  • Accuracy Traversable: 0.9730
  • Accuracy Non-traversable: 0.9684
  • Iou Unlabeled: 0.0
  • Iou Traversable: 0.9226
  • Iou Non-traversable: 0.9557

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: 2
  • eval_batch_size: 2
  • 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 Unlabeled Accuracy Traversable Accuracy Non-traversable Iou Unlabeled Iou Traversable Iou Non-traversable
0.5526 1.33 20 0.7232 0.5814 0.9404 0.9333 nan 0.9630 0.9177 0.0 0.8433 0.9009
0.3768 2.67 40 0.3616 0.5929 0.9504 0.9453 nan 0.9666 0.9341 0.0 0.8605 0.9183
0.3271 4.0 60 0.2683 0.6080 0.9606 0.9571 nan 0.9718 0.9494 0.0 0.8883 0.9358
0.2377 5.33 80 0.2754 0.5869 0.9256 0.9429 nan 0.8706 0.9807 0.0 0.8415 0.9191
0.2242 6.67 100 0.2299 0.6027 0.9447 0.9545 nan 0.9134 0.9760 0.0 0.8739 0.9341
0.2458 8.0 120 0.1939 0.6207 0.9604 0.9667 nan 0.9402 0.9805 0.0 0.9107 0.9513
0.1541 9.33 140 0.1988 0.6121 0.9654 0.9599 nan 0.9831 0.9477 0.0 0.8967 0.9395
0.1448 10.67 160 0.1722 0.6202 0.9677 0.9662 nan 0.9725 0.9629 0.0 0.9112 0.9494
0.1533 12.0 180 0.2112 0.5951 0.9570 0.9454 nan 0.9941 0.9199 0.0 0.8676 0.9176
0.107 13.33 200 0.1658 0.6139 0.9626 0.9614 nan 0.9665 0.9587 0.0 0.8992 0.9426
0.109 14.67 220 0.1342 0.6267 0.9714 0.9712 nan 0.9724 0.9705 0.0 0.9231 0.9569
0.1092 16.0 240 0.1448 0.6173 0.9690 0.9636 nan 0.9860 0.9519 0.0 0.9065 0.9453
0.0971 17.33 260 0.1282 0.6216 0.9691 0.9673 nan 0.9747 0.9635 0.0 0.9136 0.9512
0.1448 18.67 280 0.1504 0.6155 0.9661 0.9619 nan 0.9795 0.9526 0.0 0.9032 0.9434
0.0797 20.0 300 0.1312 0.6209 0.9669 0.9666 nan 0.9680 0.9659 0.0 0.9124 0.9503
0.0766 21.33 320 0.1164 0.6251 0.9667 0.9696 nan 0.9574 0.9760 0.0 0.9198 0.9555
0.0822 22.67 340 0.1365 0.6171 0.9638 0.9639 nan 0.9635 0.9641 0.0 0.9050 0.9464
0.075 24.0 360 0.1401 0.6160 0.9679 0.9621 nan 0.9862 0.9495 0.0 0.9046 0.9433
0.0684 25.33 380 0.1317 0.6190 0.9687 0.9645 nan 0.9822 0.9552 0.0 0.9099 0.9472
0.0767 26.67 400 0.1293 0.6195 0.9699 0.9651 nan 0.9851 0.9547 0.0 0.9107 0.9478
0.0576 28.0 420 0.1195 0.6236 0.9701 0.9679 nan 0.9771 0.9631 0.0 0.9180 0.9529
0.0596 29.33 440 0.1179 0.6248 0.9717 0.9692 nan 0.9794 0.9639 0.0 0.9204 0.9541
0.0564 30.67 460 0.1110 0.6264 0.9719 0.9701 nan 0.9777 0.9661 0.0 0.9233 0.9559
0.0496 32.0 480 0.1063 0.6284 0.9726 0.9714 nan 0.9761 0.9690 0.0 0.9271 0.9581
0.0722 33.33 500 0.1073 0.6272 0.9712 0.9711 nan 0.9716 0.9708 0.0 0.9244 0.9573
0.0465 34.67 520 0.1228 0.6220 0.9692 0.9669 nan 0.9763 0.9620 0.0 0.9150 0.9510
0.0655 36.0 540 0.1142 0.6245 0.9704 0.9689 nan 0.9752 0.9656 0.0 0.9196 0.9540
0.0516 37.33 560 0.1197 0.6238 0.9687 0.9684 nan 0.9696 0.9677 0.0 0.9181 0.9533
0.0774 38.67 580 0.1114 0.6266 0.9706 0.9704 nan 0.9712 0.9700 0.0 0.9234 0.9565
0.0572 40.0 600 0.1124 0.6261 0.9707 0.9700 nan 0.9730 0.9684 0.0 0.9227 0.9558
0.0554 41.33 620 0.1116 0.6273 0.9718 0.9709 nan 0.9747 0.9688 0.0 0.9248 0.9570
0.0438 42.67 640 0.1192 0.6259 0.9707 0.9694 nan 0.9746 0.9667 0.0 0.9225 0.9551
0.0486 44.0 660 0.1186 0.6248 0.9709 0.9689 nan 0.9775 0.9643 0.0 0.9203 0.9540
0.0582 45.33 680 0.1194 0.6250 0.9705 0.9691 nan 0.9751 0.9660 0.0 0.9209 0.9542
0.0643 46.67 700 0.1157 0.6252 0.9706 0.9692 nan 0.9750 0.9662 0.0 0.9209 0.9546
0.057 48.0 720 0.1181 0.6251 0.9708 0.9691 nan 0.9763 0.9654 0.0 0.9210 0.9543
0.0468 49.33 740 0.1129 0.6261 0.9707 0.9700 nan 0.9730 0.9684 0.0 0.9226 0.9557

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.0