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HorcruxNo13
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segformer-b0-finetuned-segments-toolwear

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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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+ results: []
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+ ---
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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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+
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+ # segformer-b0-finetuned-segments-toolwear
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+
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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 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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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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: 25
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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3