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---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Final-Set4-Grayscale_On-the-fly-Augmented_batch8_lr0.0002
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_mit-b5_Final-Set4-Grayscale_On-the-fly-Augmented_batch8_lr0.0002
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6332
- Mean Iou: 0.2042
- Mean Accuracy: 0.3547
- Overall Accuracy: 0.5005
- Accuracy Background: 0.8894
- Accuracy Melt: 0.0
- Accuracy Substrate: 0.1746
- Iou Background: 0.4549
- Iou Melt: 0.0
- Iou Substrate: 0.1576
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.0948 | 1.7699 | 50 | 3.2172 | 0.1562 | 0.3333 | 0.4687 | 1.0 | 0.0 | 0.0 | 0.4687 | 0.0 | 0.0 |
| 0.0735 | 3.5398 | 100 | 3.5520 | 0.1562 | 0.3333 | 0.4687 | 1.0 | 0.0 | 0.0 | 0.4687 | 0.0 | 0.0 |
| 0.064 | 5.3097 | 150 | 3.4135 | 0.1562 | 0.3333 | 0.4687 | 1.0 | 0.0 | 0.0 | 0.4687 | 0.0 | 0.0 |
| 0.062 | 7.0796 | 200 | 3.2473 | 0.1594 | 0.3297 | 0.4638 | 0.9688 | 0.0 | 0.0203 | 0.4585 | 0.0 | 0.0197 |
| 0.0672 | 8.8496 | 250 | 1.3897 | 0.1861 | 0.3555 | 0.5006 | 0.9884 | 0.0 | 0.0780 | 0.4812 | 0.0 | 0.0771 |
| 0.0423 | 10.6195 | 300 | 1.3204 | 0.1603 | 0.2938 | 0.4144 | 0.7495 | 0.0 | 0.1318 | 0.3750 | 0.0 | 0.1058 |
| 0.044 | 12.3894 | 350 | 1.2021 | 0.2482 | 0.3864 | 0.5467 | 0.8290 | 0.0 | 0.3303 | 0.4616 | 0.0 | 0.2829 |
| 0.0322 | 14.1593 | 400 | 1.5121 | 0.2118 | 0.3578 | 0.5052 | 0.8625 | 0.0 | 0.2108 | 0.4497 | 0.0 | 0.1858 |
| 0.0291 | 15.9292 | 450 | 1.6387 | 0.1855 | 0.3411 | 0.4808 | 0.9079 | 0.0 | 0.1155 | 0.4504 | 0.0 | 0.1059 |
| 0.0235 | 17.6991 | 500 | 1.6660 | 0.1874 | 0.3481 | 0.4906 | 0.9404 | 0.0 | 0.1040 | 0.4639 | 0.0 | 0.0982 |
| 0.0243 | 19.4690 | 550 | 1.5501 | 0.2051 | 0.3521 | 0.4970 | 0.8649 | 0.0 | 0.1913 | 0.4463 | 0.0 | 0.1690 |
| 0.0225 | 21.2389 | 600 | 1.7049 | 0.1982 | 0.3497 | 0.4934 | 0.8914 | 0.0 | 0.1578 | 0.4520 | 0.0 | 0.1427 |
| 0.0265 | 23.0088 | 650 | 1.6788 | 0.2008 | 0.3531 | 0.4982 | 0.8989 | 0.0 | 0.1606 | 0.4564 | 0.0 | 0.1461 |
| 0.0214 | 24.7788 | 700 | 1.6332 | 0.2042 | 0.3547 | 0.5005 | 0.8894 | 0.0 | 0.1746 | 0.4549 | 0.0 | 0.1576 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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