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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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-v0 |
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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-b0-finetuned-v0 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the tontokoton/artery-ultrasound-siit dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5847 |
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- Mean Iou: 0.1252 |
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- Mean Accuracy: 0.2052 |
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- Overall Accuracy: 0.2652 |
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- Accuracy Artery: nan |
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- Accuracy Vein: 0.2256 |
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- Accuracy Nerve: 0.0 |
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- Accuracy Muscle1: 0.0 |
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- Accuracy Muscle2: 0.3266 |
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- Accuracy Muscle3: 0.0009 |
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- Accuracy Muscle4: 0.6780 |
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- Accuracy Unknown: nan |
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- Iou Artery: 0.0 |
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- Iou Vein: 0.2256 |
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- Iou Nerve: 0.0 |
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- Iou Muscle1: 0.0 |
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- Iou Muscle2: 0.2135 |
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- Iou Muscle3: 0.0005 |
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- Iou Muscle4: 0.4366 |
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- Iou Unknown: nan |
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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 Artery | Accuracy Vein | Accuracy Nerve | Accuracy Muscle1 | Accuracy Muscle2 | Accuracy Muscle3 | Accuracy Muscle4 | Accuracy Unknown | Iou Artery | Iou Vein | Iou Nerve | Iou Muscle1 | Iou Muscle2 | Iou Muscle3 | Iou Muscle4 | Iou Unknown | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------:|:-------------:|:--------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------:|:--------:|:---------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:| |
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| 1.7835 | 10.0 | 10 | 1.9744 | 0.0620 | 0.1572 | 0.2117 | nan | 0.0834 | 0.0 | 0.0 | 0.2330 | 0.0002 | 0.6266 | nan | 0.0 | 0.0563 | 0.0 | 0.0 | 0.1422 | 0.0001 | 0.2975 | 0.0 | |
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| 1.4779 | 20.0 | 20 | 1.8578 | 0.0954 | 0.1794 | 0.2576 | nan | 0.0670 | 0.0 | 0.0 | 0.3099 | 0.0 | 0.6992 | nan | 0.0 | 0.0670 | 0.0 | 0.0 | 0.1857 | 0.0 | 0.4152 | nan | |
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| 1.3434 | 30.0 | 30 | 1.6759 | 0.1239 | 0.2014 | 0.2743 | nan | 0.1921 | 0.0 | 0.0 | 0.3566 | 0.0 | 0.6594 | nan | 0.0 | 0.1921 | 0.0 | 0.0 | 0.2244 | 0.0 | 0.4509 | nan | |
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| 1.2779 | 40.0 | 40 | 1.6276 | 0.1375 | 0.2263 | 0.3176 | nan | 0.2159 | 0.0 | 0.0 | 0.4348 | 0.0 | 0.7073 | nan | 0.0 | 0.2159 | 0.0 | 0.0 | 0.2784 | 0.0 | 0.4684 | nan | |
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| 1.1988 | 50.0 | 50 | 1.5847 | 0.1252 | 0.2052 | 0.2652 | nan | 0.2256 | 0.0 | 0.0 | 0.3266 | 0.0009 | 0.6780 | nan | 0.0 | 0.2256 | 0.0 | 0.0 | 0.2135 | 0.0005 | 0.4366 | nan | |
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### Framework versions |
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- Transformers 4.33.1 |
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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 |
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