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---
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: 0.2252
- Mean Iou: 0.4213
- Mean Accuracy: 0.8427
- Overall Accuracy: 0.8427
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.8427
- Iou Unlabeled: 0.0
- Iou Circuit: 0.8427
- Dice Coefficient: 0.8060
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
| 0.564 | 1.25 | 100 | 0.3976 | 0.2158 | 0.4316 | 0.4316 | nan | 0.4316 | 0.0 | 0.4316 | 0.3032 |
| 0.523 | 2.5 | 200 | 0.3853 | 0.2051 | 0.4102 | 0.4102 | nan | 0.4102 | 0.0 | 0.4102 | 0.2797 |
| 0.5447 | 3.75 | 300 | 0.3570 | 0.1866 | 0.3731 | 0.3731 | nan | 0.3731 | 0.0 | 0.3731 | 0.2145 |
| 0.5087 | 5.0 | 400 | 0.3325 | 0.2632 | 0.5264 | 0.5264 | nan | 0.5264 | 0.0 | 0.5264 | 0.4352 |
| 0.5064 | 6.25 | 500 | 0.3596 | 0.3047 | 0.6094 | 0.6094 | nan | 0.6094 | 0.0 | 0.6094 | 0.5244 |
| 0.4947 | 7.5 | 600 | 0.3153 | 0.3062 | 0.6124 | 0.6124 | nan | 0.6124 | 0.0 | 0.6124 | 0.5797 |
| 0.4703 | 8.75 | 700 | 0.2752 | 0.4433 | 0.8866 | 0.8866 | nan | 0.8866 | 0.0 | 0.8866 | 0.8004 |
| 0.4679 | 10.0 | 800 | 0.2900 | 0.3833 | 0.7666 | 0.7666 | nan | 0.7666 | 0.0 | 0.7666 | 0.7333 |
| 0.4691 | 11.25 | 900 | 0.3102 | 0.4024 | 0.8048 | 0.8048 | nan | 0.8048 | 0.0 | 0.8048 | 0.7452 |
| 0.4648 | 12.5 | 1000 | 0.2768 | 0.3698 | 0.7396 | 0.7396 | nan | 0.7396 | 0.0 | 0.7396 | 0.7157 |
| 0.4459 | 13.75 | 1100 | 0.2575 | 0.4120 | 0.8239 | 0.8239 | nan | 0.8239 | 0.0 | 0.8239 | 0.7781 |
| 0.446 | 15.0 | 1200 | 0.2927 | 0.4653 | 0.9306 | 0.9306 | nan | 0.9306 | 0.0 | 0.9306 | 0.8262 |
| 0.4299 | 16.25 | 1300 | 0.2682 | 0.3375 | 0.6749 | 0.6749 | nan | 0.6749 | 0.0 | 0.6749 | 0.6881 |
| 0.4464 | 17.5 | 1400 | 0.2379 | 0.4282 | 0.8563 | 0.8563 | nan | 0.8563 | 0.0 | 0.8563 | 0.8051 |
| 0.4241 | 18.75 | 1500 | 0.2479 | 0.3996 | 0.7993 | 0.7993 | nan | 0.7993 | 0.0 | 0.7993 | 0.7770 |
| 0.4154 | 20.0 | 1600 | 0.2441 | 0.4133 | 0.8265 | 0.8265 | nan | 0.8265 | 0.0 | 0.8265 | 0.7923 |
| 0.428 | 21.25 | 1700 | 0.2505 | 0.4258 | 0.8515 | 0.8515 | nan | 0.8515 | 0.0 | 0.8515 | 0.8082 |
| 0.4126 | 22.5 | 1800 | 0.2419 | 0.4549 | 0.9097 | 0.9097 | nan | 0.9097 | 0.0 | 0.9097 | 0.8370 |
| 0.3986 | 23.75 | 1900 | 0.2364 | 0.3863 | 0.7726 | 0.7726 | nan | 0.7726 | 0.0 | 0.7726 | 0.7577 |
| 0.4053 | 25.0 | 2000 | 0.2419 | 0.3752 | 0.7504 | 0.7504 | nan | 0.7504 | 0.0 | 0.7504 | 0.7367 |
| 0.4018 | 26.25 | 2100 | 0.2310 | 0.4299 | 0.8598 | 0.8598 | nan | 0.8598 | 0.0 | 0.8598 | 0.8078 |
| 0.4048 | 27.5 | 2200 | 0.2292 | 0.4288 | 0.8577 | 0.8577 | nan | 0.8577 | 0.0 | 0.8577 | 0.8095 |
| 0.3838 | 28.75 | 2300 | 0.2294 | 0.4185 | 0.8371 | 0.8371 | nan | 0.8371 | 0.0 | 0.8371 | 0.7979 |
| 0.389 | 30.0 | 2400 | 0.2255 | 0.4337 | 0.8675 | 0.8675 | nan | 0.8675 | 0.0 | 0.8675 | 0.8181 |
| 0.3889 | 31.25 | 2500 | 0.2247 | 0.4307 | 0.8613 | 0.8613 | nan | 0.8613 | 0.0 | 0.8613 | 0.8180 |
| 0.4058 | 32.5 | 2600 | 0.2290 | 0.3806 | 0.7611 | 0.7611 | nan | 0.7611 | 0.0 | 0.7611 | 0.7493 |
| 0.3822 | 33.75 | 2700 | 0.2301 | 0.4023 | 0.8046 | 0.8046 | nan | 0.8046 | 0.0 | 0.8046 | 0.7794 |
| 0.3807 | 35.0 | 2800 | 0.2261 | 0.3952 | 0.7904 | 0.7904 | nan | 0.7904 | 0.0 | 0.7904 | 0.7691 |
| 0.3993 | 36.25 | 2900 | 0.2199 | 0.4163 | 0.8326 | 0.8326 | nan | 0.8326 | 0.0 | 0.8326 | 0.7997 |
| 0.3586 | 37.5 | 3000 | 0.2238 | 0.4098 | 0.8195 | 0.8195 | nan | 0.8195 | 0.0 | 0.8195 | 0.7897 |
| 0.3894 | 38.75 | 3100 | 0.2334 | 0.3539 | 0.7077 | 0.7077 | nan | 0.7077 | 0.0 | 0.7077 | 0.7093 |
| 0.3627 | 40.0 | 3200 | 0.2311 | 0.3646 | 0.7292 | 0.7292 | nan | 0.7292 | 0.0 | 0.7292 | 0.7249 |
| 0.3704 | 41.25 | 3300 | 0.2266 | 0.3876 | 0.7751 | 0.7751 | nan | 0.7751 | 0.0 | 0.7751 | 0.7621 |
| 0.3808 | 42.5 | 3400 | 0.2227 | 0.3996 | 0.7993 | 0.7993 | nan | 0.7993 | 0.0 | 0.7993 | 0.7793 |
| 0.3631 | 43.75 | 3500 | 0.2222 | 0.3910 | 0.7820 | 0.7820 | nan | 0.7820 | 0.0 | 0.7820 | 0.7655 |
| 0.367 | 45.0 | 3600 | 0.2253 | 0.4118 | 0.8237 | 0.8237 | nan | 0.8237 | 0.0 | 0.8237 | 0.7939 |
| 0.3609 | 46.25 | 3700 | 0.2225 | 0.4082 | 0.8165 | 0.8165 | nan | 0.8165 | 0.0 | 0.8165 | 0.7897 |
| 0.3515 | 47.5 | 3800 | 0.2226 | 0.4210 | 0.8420 | 0.8420 | nan | 0.8420 | 0.0 | 0.8420 | 0.8064 |
| 0.3888 | 48.75 | 3900 | 0.2283 | 0.3815 | 0.7630 | 0.7630 | nan | 0.7630 | 0.0 | 0.7630 | 0.7509 |
| 0.3503 | 50.0 | 4000 | 0.2252 | 0.4213 | 0.8427 | 0.8427 | nan | 0.8427 | 0.0 | 0.8427 | 0.8060 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu115
- Datasets 2.15.0
- Tokenizers 0.15.0
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