|
---
|
|
license: other
|
|
base_model: nvidia/mit-b0
|
|
tags:
|
|
- vision
|
|
- image-segmentation
|
|
- generated_from_trainer
|
|
model-index:
|
|
- name: segformer-b0-miic-tl
|
|
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-miic-tl
|
|
|
|
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Loss: 0.3532
|
|
- Mean Iou: 0.4001
|
|
- Mean Accuracy: 0.8002
|
|
- Overall Accuracy: 0.8002
|
|
- Accuracy Unlabeled: nan
|
|
- Accuracy Circuit: 0.8002
|
|
- Iou Unlabeled: 0.0
|
|
- Iou Circuit: 0.8002
|
|
- Dice Coefficient: 0.7456
|
|
|
|
## 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 Unlabeled | Accuracy Circuit | Iou Unlabeled | Iou Circuit | Dice Coefficient |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
|
|
| 0.5722 | 3.12 | 250 | 0.4296 | 0.3809 | 0.7618 | 0.7618 | nan | 0.7618 | 0.0 | 0.7618 | 0.6766 |
|
|
| 0.547 | 6.25 | 500 | 0.3983 | 0.3370 | 0.6739 | 0.6739 | nan | 0.6739 | 0.0 | 0.6739 | 0.6065 |
|
|
| 0.5147 | 9.38 | 750 | 0.3643 | 0.3487 | 0.6974 | 0.6974 | nan | 0.6974 | 0.0 | 0.6974 | 0.6477 |
|
|
| 0.5083 | 12.5 | 1000 | 0.3505 | 0.3006 | 0.6012 | 0.6012 | nan | 0.6012 | 0.0 | 0.6012 | 0.5586 |
|
|
| 0.4818 | 15.62 | 1250 | 0.3184 | 0.4400 | 0.8799 | 0.8799 | nan | 0.8799 | 0.0 | 0.8799 | 0.7758 |
|
|
| 0.4664 | 18.75 | 1500 | 0.3622 | 0.4347 | 0.8693 | 0.8693 | nan | 0.8693 | 0.0 | 0.8693 | 0.7755 |
|
|
| 0.4504 | 21.88 | 1750 | 0.3279 | 0.4327 | 0.8654 | 0.8654 | nan | 0.8654 | 0.0 | 0.8654 | 0.7792 |
|
|
| 0.4427 | 25.0 | 2000 | 0.3168 | 0.4386 | 0.8771 | 0.8771 | nan | 0.8771 | 0.0 | 0.8771 | 0.7840 |
|
|
| 0.4336 | 28.12 | 2250 | 0.2790 | 0.4100 | 0.8200 | 0.8200 | nan | 0.8200 | 0.0 | 0.8200 | 0.7636 |
|
|
| 0.4226 | 31.25 | 2500 | 0.3237 | 0.4148 | 0.8295 | 0.8295 | nan | 0.8295 | 0.0 | 0.8295 | 0.7641 |
|
|
| 0.4155 | 34.38 | 2750 | 0.3336 | 0.4169 | 0.8339 | 0.8339 | nan | 0.8339 | 0.0 | 0.8339 | 0.7664 |
|
|
| 0.4082 | 37.5 | 3000 | 0.3787 | 0.4267 | 0.8533 | 0.8533 | nan | 0.8533 | 0.0 | 0.8533 | 0.7760 |
|
|
| 0.403 | 40.62 | 3250 | 0.3541 | 0.3693 | 0.7387 | 0.7387 | nan | 0.7387 | 0.0 | 0.7387 | 0.6942 |
|
|
| 0.398 | 43.75 | 3500 | 0.3361 | 0.3864 | 0.7728 | 0.7728 | nan | 0.7728 | 0.0 | 0.7728 | 0.7244 |
|
|
| 0.3943 | 46.88 | 3750 | 0.3599 | 0.4053 | 0.8106 | 0.8106 | nan | 0.8106 | 0.0 | 0.8106 | 0.7519 |
|
|
| 0.3951 | 50.0 | 4000 | 0.3532 | 0.4001 | 0.8002 | 0.8002 | nan | 0.8002 | 0.0 | 0.8002 | 0.7456 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.36.2
|
|
- Pytorch 1.11.0+cu115
|
|
- Datasets 2.15.0
|
|
- Tokenizers 0.15.0
|
|
|
