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mrkprc1
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segformer-b1-finetuned-sudoku

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +24 -24
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -1,10 +1,10 @@
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  ---
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  license: other
 
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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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- base_model: nvidia/mit-b1
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  model-index:
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  - name: segformer-b1-finetuned-sudoku
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  results: []
@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the mrkprc1/SudokuBoundaries2 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7703
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- - Mean Iou: 0.0967
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- - Mean Accuracy: 0.1934
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- - Overall Accuracy: 0.1934
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- - Accuracy Unlabelled: nan
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- - Accuracy Sudoku-boundary: 0.1934
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- - Iou Unlabelled: 0.0
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- - Iou Sudoku-boundary: 0.1934
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  ## Model description
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@@ -55,21 +55,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
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- | 0.6531 | 3.33 | 20 | 0.7016 | 0.1433 | 0.2867 | 0.2867 | nan | 0.2867 | 0.0 | 0.2867 |
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- | 0.7654 | 6.67 | 40 | 0.7142 | 0.3064 | 0.6129 | 0.6129 | nan | 0.6129 | 0.0 | 0.6129 |
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- | 0.4761 | 10.0 | 60 | 1.0391 | 0.0002 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0005 |
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- | 0.7746 | 13.33 | 80 | 1.7648 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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- | 0.5488 | 16.67 | 100 | 1.2288 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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- | 0.6242 | 20.0 | 120 | 1.5012 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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- | 0.5423 | 23.33 | 140 | 0.9650 | 0.0029 | 0.0059 | 0.0059 | nan | 0.0059 | 0.0 | 0.0059 |
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- | 0.521 | 26.67 | 160 | 0.8594 | 0.0197 | 0.0393 | 0.0393 | nan | 0.0393 | 0.0 | 0.0393 |
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- | 0.5655 | 30.0 | 180 | 0.7950 | 0.0527 | 0.1055 | 0.1055 | nan | 0.1055 | 0.0 | 0.1055 |
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- | 0.4229 | 33.33 | 200 | 0.7910 | 0.0982 | 0.1964 | 0.1964 | nan | 0.1964 | 0.0 | 0.1964 |
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- | 0.288 | 36.67 | 220 | 0.7591 | 0.1358 | 0.2715 | 0.2715 | nan | 0.2715 | 0.0 | 0.2715 |
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- | 0.2002 | 40.0 | 240 | 0.7395 | 0.2414 | 0.4828 | 0.4828 | nan | 0.4828 | 0.0 | 0.4828 |
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- | 0.6014 | 43.33 | 260 | 0.7405 | 0.2644 | 0.5289 | 0.5289 | nan | 0.5289 | 0.0 | 0.5289 |
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- | 0.4336 | 46.67 | 280 | 0.7423 | 0.1751 | 0.3502 | 0.3502 | nan | 0.3502 | 0.0 | 0.3502 |
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- | 0.565 | 50.0 | 300 | 0.7703 | 0.0967 | 0.1934 | 0.1934 | nan | 0.1934 | 0.0 | 0.1934 |
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  ### Framework versions
 
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  ---
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  license: other
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+ base_model: nvidia/mit-b1
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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-sudoku
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  results: []
 
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  This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the mrkprc1/SudokuBoundaries2 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9826
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+ - Mean Iou: 0.2452
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+ - Mean Accuracy: 0.4999
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+ - Overall Accuracy: 0.4903
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+ - Accuracy Unlabelled: 0.9996
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+ - Accuracy Sudoku-boundary: 0.0001
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+ - Iou Unlabelled: 0.4903
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+ - Iou Sudoku-boundary: 0.0001
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
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+ | 0.6034 | 3.33 | 20 | 0.6951 | 0.3427 | 0.5173 | 0.5149 | 0.6432 | 0.3914 | 0.3940 | 0.2913 |
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+ | 0.7796 | 6.67 | 40 | 0.7150 | 0.3049 | 0.5083 | 0.5021 | 0.8309 | 0.1857 | 0.4501 | 0.1597 |
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+ | 0.4378 | 10.0 | 60 | 0.9772 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
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+ | 0.6804 | 13.33 | 80 | 1.1605 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
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+ | 0.58 | 16.67 | 100 | 0.9787 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
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+ | 0.6563 | 20.0 | 120 | 1.1860 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
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+ | 0.5128 | 23.33 | 140 | 0.8884 | 0.2457 | 0.5002 | 0.4907 | 0.9996 | 0.0009 | 0.4905 | 0.0009 |
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+ | 0.5054 | 26.67 | 160 | 0.8746 | 0.2455 | 0.5002 | 0.4907 | 0.9998 | 0.0006 | 0.4905 | 0.0006 |
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+ | 0.5532 | 30.0 | 180 | 0.9540 | 0.2452 | 0.5000 | 0.4905 | 1.0 | 0.0000 | 0.4905 | 0.0000 |
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+ | 0.3238 | 33.33 | 200 | 0.8916 | 0.2470 | 0.5009 | 0.4914 | 0.9984 | 0.0035 | 0.4905 | 0.0035 |
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+ | 0.2964 | 36.67 | 220 | 1.0162 | 0.2453 | 0.5000 | 0.4905 | 1.0000 | 0.0000 | 0.4905 | 0.0000 |
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+ | 0.2102 | 40.0 | 240 | 0.9650 | 0.2452 | 0.4998 | 0.4903 | 0.9996 | 0.0001 | 0.4903 | 0.0001 |
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+ | 0.623 | 43.33 | 260 | 0.9071 | 0.2461 | 0.5004 | 0.4909 | 0.9991 | 0.0017 | 0.4904 | 0.0017 |
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+ | 0.3741 | 46.67 | 280 | 0.9245 | 0.2454 | 0.5000 | 0.4904 | 0.9994 | 0.0006 | 0.4903 | 0.0006 |
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+ | 0.5765 | 50.0 | 300 | 0.9826 | 0.2452 | 0.4999 | 0.4903 | 0.9996 | 0.0001 | 0.4903 | 0.0001 |
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  ### Framework versions
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