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---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-v0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-v0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the tontokoton/artery-ultrasound-siit dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5847
- Mean Iou: 0.1252
- Mean Accuracy: 0.2052
- Overall Accuracy: 0.2652
- Accuracy Artery: nan
- Accuracy Vein: 0.2256
- Accuracy Nerve: 0.0
- Accuracy Muscle1: 0.0
- Accuracy Muscle2: 0.3266
- Accuracy Muscle3: 0.0009
- Accuracy Muscle4: 0.6780
- Accuracy Unknown: nan
- Iou Artery: 0.0
- Iou Vein: 0.2256
- Iou Nerve: 0.0
- Iou Muscle1: 0.0
- Iou Muscle2: 0.2135
- Iou Muscle3: 0.0005
- Iou Muscle4: 0.4366
- Iou Unknown: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------:|:-------------:|:--------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------:|:--------:|:---------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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