---
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