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segformer-b0-finetuned-segments-toolwear

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update model card README.md

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  ---
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  license: other
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  tags:
 
 
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  - generated_from_trainer
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  model-index:
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  - name: segformer-b0-finetuned-segments-toolwear
@@ -12,18 +14,18 @@ should probably proofread and complete it, then remove this comment. -->
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  # segformer-b0-finetuned-segments-toolwear
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- This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1501
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- - Mean Iou: 0.4560
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- - Mean Accuracy: 0.9040
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- - Overall Accuracy: 0.9643
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  - Accuracy Unlabeled: nan
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- - Accuracy Wear: 0.8404
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- - Accuracy Tool: 0.9675
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  - Iou Unlabeled: 0.0
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- - Iou Wear: 0.4034
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- - Iou Tool: 0.9646
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  ## Model description
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@@ -54,19 +56,19 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wear | Accuracy Tool | Iou Unlabeled | Iou Wear | Iou Tool |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
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- | 0.4464 | 1.82 | 20 | 0.6527 | 0.3325 | 0.5116 | 0.9740 | nan | 0.0242 | 0.9990 | 0.0 | 0.0235 | 0.9740 |
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- | 0.3069 | 3.64 | 40 | 0.3300 | 0.4958 | 0.8505 | 0.9661 | nan | 0.7288 | 0.9723 | 0.0 | 0.5213 | 0.9662 |
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- | 0.276 | 5.45 | 60 | 0.2597 | 0.4089 | 0.9324 | 0.9368 | nan | 0.9278 | 0.9370 | 0.0 | 0.2909 | 0.9358 |
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- | 0.2648 | 7.27 | 80 | 0.2321 | 0.4338 | 0.8839 | 0.9567 | nan | 0.8071 | 0.9607 | 0.0 | 0.3441 | 0.9572 |
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- | 0.245 | 9.09 | 100 | 0.2298 | 0.4021 | 0.9265 | 0.9359 | nan | 0.9167 | 0.9364 | 0.0 | 0.2715 | 0.9348 |
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- | 0.2047 | 10.91 | 120 | 0.1897 | 0.4379 | 0.8814 | 0.9446 | nan | 0.8147 | 0.9480 | 0.0 | 0.3684 | 0.9455 |
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- | 0.1695 | 12.73 | 140 | 0.1681 | 0.4561 | 0.8444 | 0.9636 | nan | 0.7188 | 0.9701 | 0.0 | 0.4026 | 0.9657 |
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- | 0.1556 | 14.55 | 160 | 0.1741 | 0.4289 | 0.9060 | 0.9494 | nan | 0.8603 | 0.9517 | 0.0 | 0.3372 | 0.9497 |
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- | 0.1435 | 16.36 | 180 | 0.1528 | 0.4746 | 0.8851 | 0.9679 | nan | 0.7978 | 0.9723 | 0.0 | 0.4549 | 0.9689 |
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- | 0.1208 | 18.18 | 200 | 0.1648 | 0.4379 | 0.9126 | 0.9577 | nan | 0.8650 | 0.9601 | 0.0 | 0.3560 | 0.9577 |
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- | 0.1425 | 20.0 | 220 | 0.1587 | 0.4451 | 0.9116 | 0.9576 | nan | 0.8631 | 0.9601 | 0.0 | 0.3774 | 0.9578 |
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- | 0.1124 | 21.82 | 240 | 0.1515 | 0.4291 | 0.9044 | 0.9491 | nan | 0.8574 | 0.9515 | 0.0 | 0.3380 | 0.9493 |
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- | 0.1509 | 23.64 | 260 | 0.1501 | 0.4560 | 0.9040 | 0.9643 | nan | 0.8404 | 0.9675 | 0.0 | 0.4034 | 0.9646 |
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  ### Framework versions
 
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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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  model-index:
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  - name: segformer-b0-finetuned-segments-toolwear
 
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  # segformer-b0-finetuned-segments-toolwear
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the HorcruxNo13/toolwear_cleaned_preprocessed dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1338
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+ - Mean Iou: 0.4591
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+ - Mean Accuracy: 0.7164
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+ - Overall Accuracy: 0.9595
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  - Accuracy Unlabeled: nan
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+ - Accuracy Wear: 0.4489
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+ - Accuracy Tool: 0.9838
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  - Iou Unlabeled: 0.0
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+ - Iou Wear: 0.4154
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+ - Iou Tool: 0.9618
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wear | Accuracy Tool | Iou Unlabeled | Iou Wear | Iou Tool |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
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+ | 0.5488 | 1.82 | 20 | 0.7199 | 0.3293 | 0.5153 | 0.9405 | nan | 0.0476 | 0.9830 | 0.0 | 0.0475 | 0.9404 |
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+ | 0.5195 | 3.64 | 40 | 0.3507 | 0.3622 | 0.5634 | 0.9239 | nan | 0.1667 | 0.9600 | 0.0 | 0.1629 | 0.9236 |
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+ | 0.2738 | 5.45 | 60 | 0.2569 | 0.4662 | 0.7496 | 0.9435 | nan | 0.5363 | 0.9629 | 0.0 | 0.4547 | 0.9438 |
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+ | 0.2461 | 7.27 | 80 | 0.2220 | 0.4491 | 0.7057 | 0.9482 | nan | 0.4389 | 0.9725 | 0.0 | 0.3982 | 0.9492 |
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+ | 0.1999 | 9.09 | 100 | 0.1962 | 0.4492 | 0.7084 | 0.9597 | nan | 0.4319 | 0.9848 | 0.0 | 0.3860 | 0.9616 |
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+ | 0.2004 | 10.91 | 120 | 0.1890 | 0.4031 | 0.6239 | 0.9537 | nan | 0.2610 | 0.9867 | 0.0 | 0.2539 | 0.9553 |
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+ | 0.4753 | 12.73 | 140 | 0.1704 | 0.4360 | 0.6760 | 0.9494 | nan | 0.3753 | 0.9768 | 0.0 | 0.3562 | 0.9518 |
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+ | 0.1606 | 14.55 | 160 | 0.1579 | 0.4483 | 0.7028 | 0.9580 | nan | 0.4222 | 0.9835 | 0.0 | 0.3822 | 0.9625 |
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+ | 0.1388 | 16.36 | 180 | 0.1519 | 0.4829 | 0.7940 | 0.9565 | nan | 0.6152 | 0.9728 | 0.0 | 0.4900 | 0.9586 |
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+ | 0.138 | 18.18 | 200 | 0.1374 | 0.5120 | 0.8119 | 0.9643 | nan | 0.6443 | 0.9795 | 0.0 | 0.5693 | 0.9668 |
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+ | 0.1078 | 20.0 | 220 | 0.1400 | 0.4541 | 0.7066 | 0.9606 | nan | 0.4271 | 0.9860 | 0.0 | 0.3985 | 0.9638 |
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+ | 0.1426 | 21.82 | 240 | 0.1323 | 0.4530 | 0.7053 | 0.9581 | nan | 0.4272 | 0.9834 | 0.0 | 0.3978 | 0.9611 |
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+ | 0.3498 | 23.64 | 260 | 0.1338 | 0.4591 | 0.7164 | 0.9595 | nan | 0.4489 | 0.9838 | 0.0 | 0.4154 | 0.9618 |
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  ### Framework versions