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--- |
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license: other |
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base_model: nvidia/segformer-b1-finetuned-cityscapes-1024-1024 |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b1-finetuned-cityscapes-1024-1024-straighter-only |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer-b1-finetuned-cityscapes-1024-1024-straighter-only |
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This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on the selvaa/straighter-only-final dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0331 |
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- Mean Iou: 0.9378 |
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- Mean Accuracy: 0.9644 |
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- Overall Accuracy: 0.9883 |
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- Accuracy Default: 1e-06 |
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- Accuracy Pipe: 0.9182 |
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- Accuracy Floor: 0.9790 |
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- Accuracy Background: 0.9961 |
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- Iou Default: 1e-06 |
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- Iou Pipe: 0.8600 |
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- Iou Floor: 0.9637 |
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- Iou Background: 0.9896 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 3 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 60 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Default | Accuracy Pipe | Accuracy Floor | Accuracy Background | Iou Default | Iou Pipe | Iou Floor | Iou Background | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:| |
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| 0.5349 | 1.0 | 36 | 0.1593 | 0.8143 | 0.8613 | 0.9661 | 1e-06 | 0.6324 | 0.9614 | 0.9903 | 1e-06 | 0.5490 | 0.9277 | 0.9660 | |
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| 0.1472 | 2.0 | 72 | 0.0977 | 0.8792 | 0.9255 | 0.9782 | 1e-06 | 0.8153 | 0.9690 | 0.9922 | 1e-06 | 0.7119 | 0.9456 | 0.9800 | |
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| 0.0902 | 3.0 | 108 | 0.0708 | 0.9014 | 0.9285 | 0.9820 | 1e-06 | 0.8194 | 0.9690 | 0.9972 | 1e-06 | 0.7669 | 0.9558 | 0.9815 | |
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| 0.0662 | 4.0 | 144 | 0.0586 | 0.9146 | 0.9552 | 0.9842 | 1e-06 | 0.9036 | 0.9666 | 0.9954 | 1e-06 | 0.8015 | 0.9567 | 0.9856 | |
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| 0.0543 | 5.0 | 180 | 0.0490 | 0.9225 | 0.9514 | 0.9856 | 1e-06 | 0.8844 | 0.9734 | 0.9964 | 1e-06 | 0.8208 | 0.9606 | 0.9860 | |
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| 0.0486 | 6.0 | 216 | 0.0445 | 0.9252 | 0.9640 | 0.9862 | 1e-06 | 0.9244 | 0.9729 | 0.9947 | 1e-06 | 0.8265 | 0.9616 | 0.9875 | |
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| 0.042 | 7.0 | 252 | 0.0414 | 0.9279 | 0.9658 | 0.9867 | 1e-06 | 0.9315 | 0.9699 | 0.9959 | 1e-06 | 0.8332 | 0.9626 | 0.9880 | |
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| 0.0389 | 8.0 | 288 | 0.0381 | 0.9322 | 0.9695 | 0.9874 | 1e-06 | 0.9413 | 0.9716 | 0.9956 | 1e-06 | 0.8448 | 0.9632 | 0.9888 | |
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| 0.0359 | 9.0 | 324 | 0.0386 | 0.9319 | 0.9629 | 0.9871 | 1e-06 | 0.9215 | 0.9702 | 0.9970 | 1e-06 | 0.8451 | 0.9630 | 0.9877 | |
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| 0.034 | 10.0 | 360 | 0.0374 | 0.9313 | 0.9632 | 0.9873 | 1e-06 | 0.9202 | 0.9730 | 0.9965 | 1e-06 | 0.8422 | 0.9634 | 0.9883 | |
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| 0.0322 | 11.0 | 396 | 0.0383 | 0.9300 | 0.9570 | 0.9871 | 1e-06 | 0.8993 | 0.9746 | 0.9971 | 1e-06 | 0.8379 | 0.9642 | 0.9878 | |
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| 0.0306 | 12.0 | 432 | 0.0353 | 0.9340 | 0.9678 | 0.9876 | 1e-06 | 0.9358 | 0.9710 | 0.9965 | 1e-06 | 0.8494 | 0.9637 | 0.9888 | |
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| 0.0292 | 13.0 | 468 | 0.0337 | 0.9355 | 0.9734 | 0.9881 | 1e-06 | 0.9527 | 0.9719 | 0.9957 | 1e-06 | 0.8529 | 0.9637 | 0.9898 | |
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| 0.0286 | 14.0 | 504 | 0.0334 | 0.9355 | 0.9686 | 0.9881 | 1e-06 | 0.9352 | 0.9745 | 0.9960 | 1e-06 | 0.8530 | 0.9641 | 0.9895 | |
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| 0.0271 | 15.0 | 540 | 0.0325 | 0.9389 | 0.9682 | 0.9885 | 1e-06 | 0.9325 | 0.9758 | 0.9964 | 1e-06 | 0.8624 | 0.9648 | 0.9897 | |
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| 0.0266 | 16.0 | 576 | 0.0327 | 0.9373 | 0.9696 | 0.9883 | 1e-06 | 0.9378 | 0.9748 | 0.9961 | 1e-06 | 0.8576 | 0.9646 | 0.9897 | |
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| 0.0257 | 17.0 | 612 | 0.0350 | 0.9330 | 0.9673 | 0.9877 | 1e-06 | 0.9302 | 0.9766 | 0.9952 | 1e-06 | 0.8463 | 0.9636 | 0.9892 | |
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| 0.0246 | 18.0 | 648 | 0.0333 | 0.9354 | 0.9665 | 0.9881 | 1e-06 | 0.9269 | 0.9764 | 0.9961 | 1e-06 | 0.8522 | 0.9644 | 0.9896 | |
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| 0.0242 | 19.0 | 684 | 0.0326 | 0.9378 | 0.9681 | 0.9884 | 1e-06 | 0.9311 | 0.9772 | 0.9959 | 1e-06 | 0.8588 | 0.9648 | 0.9896 | |
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| 0.0231 | 20.0 | 720 | 0.0339 | 0.9366 | 0.9665 | 0.9883 | 1e-06 | 0.9256 | 0.9781 | 0.9958 | 1e-06 | 0.8557 | 0.9646 | 0.9896 | |
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| 0.0236 | 21.0 | 756 | 0.0333 | 0.9365 | 0.9702 | 0.9883 | 1e-06 | 0.9375 | 0.9779 | 0.9951 | 1e-06 | 0.8552 | 0.9644 | 0.9900 | |
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| 0.0227 | 22.0 | 792 | 0.0327 | 0.9375 | 0.9690 | 0.9885 | 1e-06 | 0.9339 | 0.9773 | 0.9958 | 1e-06 | 0.8577 | 0.9649 | 0.9900 | |
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| 0.0226 | 23.0 | 828 | 0.0331 | 0.9378 | 0.9644 | 0.9883 | 1e-06 | 0.9182 | 0.9790 | 0.9961 | 1e-06 | 0.8600 | 0.9637 | 0.9896 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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