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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-miic-tl
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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-miic-tl
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This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2212
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- Mean Iou: 0.4723
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- Mean Accuracy: 0.9446
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- Overall Accuracy: 0.9446
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- Accuracy Unlabeled: nan
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- Accuracy Circuit: 0.9446
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- Iou Unlabeled: 0.0
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- Iou Circuit: 0.9446
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- Dice Coefficient: 0.8541
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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: 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: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Circuit | Iou Unlabeled | Iou Circuit | Dice Coefficient |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
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| 0.3419 | 3.12 | 250 | 0.2745 | 0.4850 | 0.9701 | 0.9701 | nan | 0.9701 | 0.0 | 0.9701 | 0.8149 |
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| 0.2785 | 6.25 | 500 | 0.2789 | 0.4828 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | 0.8285 |
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| 0.2549 | 9.38 | 750 | 0.2888 | 0.4721 | 0.9443 | 0.9443 | nan | 0.9443 | 0.0 | 0.9443 | 0.8372 |
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| 0.2728 | 12.5 | 1000 | 0.2426 | 0.4699 | 0.9397 | 0.9397 | nan | 0.9397 | 0.0 | 0.9397 | 0.8424 |
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| 0.2625 | 15.62 | 1250 | 0.1990 | 0.4632 | 0.9264 | 0.9264 | nan | 0.9264 | 0.0 | 0.9264 | 0.8520 |
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| 0.2449 | 18.75 | 1500 | 0.2121 | 0.4706 | 0.9412 | 0.9412 | nan | 0.9412 | 0.0 | 0.9412 | 0.8508 |
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| 0.2173 | 21.88 | 1750 | 0.2768 | 0.4780 | 0.9559 | 0.9559 | nan | 0.9559 | 0.0 | 0.9559 | 0.8485 |
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| 0.2158 | 25.0 | 2000 | 0.2772 | 0.4643 | 0.9287 | 0.9287 | nan | 0.9287 | 0.0 | 0.9287 | 0.8383 |
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| 0.1843 | 28.12 | 2250 | 0.1818 | 0.4671 | 0.9343 | 0.9343 | nan | 0.9343 | 0.0 | 0.9343 | 0.8685 |
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| 0.1608 | 31.25 | 2500 | 0.1794 | 0.4591 | 0.9182 | 0.9182 | nan | 0.9182 | 0.0 | 0.9182 | 0.8618 |
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| 0.1504 | 34.38 | 2750 | 0.1805 | 0.4586 | 0.9172 | 0.9172 | nan | 0.9172 | 0.0 | 0.9172 | 0.8647 |
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| 0.1495 | 37.5 | 3000 | 0.2090 | 0.4773 | 0.9545 | 0.9545 | nan | 0.9545 | 0.0 | 0.9545 | 0.8595 |
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| 0.142 | 40.62 | 3250 | 0.2048 | 0.4750 | 0.9500 | 0.9500 | nan | 0.9500 | 0.0 | 0.9500 | 0.8666 |
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| 0.1401 | 43.75 | 3500 | 0.2131 | 0.4756 | 0.9512 | 0.9512 | nan | 0.9512 | 0.0 | 0.9512 | 0.8580 |
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| 0.1339 | 46.88 | 3750 | 0.2469 | 0.4773 | 0.9546 | 0.9546 | nan | 0.9546 | 0.0 | 0.9546 | 0.8481 |
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| 0.1303 | 50.0 | 4000 | 0.2212 | 0.4723 | 0.9446 | 0.9446 | nan | 0.9446 | 0.0 | 0.9446 | 0.8541 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 1.11.0+cu115
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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