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---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: SegFormer_Clean_Set1_Grayscale_mit-b5_Grayscale
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_Clean_Set1_Grayscale_mit-b5_Grayscale
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_Grayscale dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0178
- Mean Iou: 0.9760
- Mean Accuracy: 0.9847
- Overall Accuracy: 0.9949
- Accuracy Background: 0.9976
- Accuracy Melt: 0.9586
- Accuracy Substrate: 0.9978
- Iou Background: 0.9959
- Iou Melt: 0.9408
- Iou Substrate: 0.9912
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 50
### 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.0882 | 5.5556 | 50 | 0.1733 | 0.7543 | 0.7985 | 0.9397 | 0.9594 | 0.4375 | 0.9986 | 0.9562 | 0.4189 | 0.8880 |
| 0.0295 | 11.1111 | 100 | 0.0270 | 0.9580 | 0.9736 | 0.9907 | 0.9965 | 0.9302 | 0.9940 | 0.9918 | 0.8978 | 0.9843 |
| 0.0143 | 16.6667 | 150 | 0.0260 | 0.9561 | 0.9798 | 0.9901 | 0.9970 | 0.9541 | 0.9884 | 0.9910 | 0.8938 | 0.9836 |
| 0.0095 | 22.2222 | 200 | 0.0224 | 0.9645 | 0.9747 | 0.9926 | 0.9985 | 0.9293 | 0.9962 | 0.9944 | 0.9119 | 0.9872 |
| 0.0083 | 27.7778 | 250 | 0.0180 | 0.9742 | 0.9819 | 0.9945 | 0.9982 | 0.9498 | 0.9977 | 0.9955 | 0.9366 | 0.9905 |
| 0.0072 | 33.3333 | 300 | 0.0175 | 0.9751 | 0.9838 | 0.9947 | 0.9984 | 0.9563 | 0.9968 | 0.9957 | 0.9388 | 0.9909 |
| 0.0073 | 38.8889 | 350 | 0.0177 | 0.9758 | 0.9854 | 0.9948 | 0.9970 | 0.9613 | 0.9978 | 0.9957 | 0.9406 | 0.9912 |
| 0.0054 | 44.4444 | 400 | 0.0179 | 0.9758 | 0.9844 | 0.9949 | 0.9978 | 0.9579 | 0.9976 | 0.9959 | 0.9404 | 0.9911 |
| 0.0052 | 50.0 | 450 | 0.0178 | 0.9760 | 0.9847 | 0.9949 | 0.9976 | 0.9586 | 0.9978 | 0.9959 | 0.9408 | 0.9912 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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