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---
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license: other
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base_model: nvidia/mit-b5
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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-b5-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-b5-miic-tl
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the yijisuk/ic-chip-sample dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 0.2028
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- eval_mean_iou: 0.3885
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- eval_mean_accuracy: 0.7770
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- eval_overall_accuracy: 0.7770
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- eval_accuracy_unlabeled: nan
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- eval_accuracy_circuit: 0.7770
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- eval_iou_unlabeled: 0.0
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- eval_iou_circuit: 0.7770
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- eval_dice_coefficient: 0.7854
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- eval_runtime: 1.8601
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- eval_samples_per_second: 5.376
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- eval_steps_per_second: 2.688
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- epoch: 48.75
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- step: 3900
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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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### 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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