---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Clean-Set3-Grayscale
  results: []
pipeline_tag: image-segmentation
---

<!-- 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_Clean-Set3-Grayscale

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on _Clean-Set3-Grayscale.
It achieves the following results on the evaluation set:
- Train-Loss: 0.0053 
- Loss: 0.0156
- Mean Iou: 0.9776
- Mean Accuracy: 0.9882
- Overall Accuracy: 0.9952
- Accuracy Background: 0.9974
- Accuracy Melt: 0.9708
- Accuracy Substrate: 0.9963
- Iou Background: 0.9942
- Iou Melt: 0.9458
- Iou Substrate: 0.9927

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1206        | 1.8519  | 50   | 0.0898          | 0.8826   | 0.9277        | 0.9727           | 0.9809              | 0.8182        | 0.9840             | 0.9697         | 0.7209   | 0.9571        |
| 0.0687        | 3.7037  | 100  | 0.0445          | 0.9291   | 0.9568        | 0.9845           | 0.9920              | 0.8888        | 0.9895             | 0.9833         | 0.8286   | 0.9754        |
| 0.0457        | 5.5556  | 150  | 0.0413          | 0.9284   | 0.9428        | 0.9859           | 0.9938              | 0.8381        | 0.9966             | 0.9877         | 0.8204   | 0.9770        |
| 0.0281        | 7.4074  | 200  | 0.0240          | 0.9592   | 0.9706        | 0.9914           | 0.9971              | 0.9198        | 0.9949             | 0.9900         | 0.9011   | 0.9865        |
| 0.0234        | 9.2593  | 250  | 0.0179          | 0.9672   | 0.9810        | 0.9932           | 0.9960              | 0.9513        | 0.9957             | 0.9926         | 0.9195   | 0.9893        |
| 0.0147        | 11.1111 | 300  | 0.0180          | 0.9672   | 0.9785        | 0.9932           | 0.9955              | 0.9429        | 0.9972             | 0.9925         | 0.9197   | 0.9893        |
| 0.012         | 12.9630 | 350  | 0.0139          | 0.9748   | 0.9864        | 0.9946           | 0.9967              | 0.9664        | 0.9962             | 0.9936         | 0.9390   | 0.9918        |
| 0.0104        | 14.8148 | 400  | 0.0138          | 0.9756   | 0.9890        | 0.9947           | 0.9972              | 0.9748        | 0.9949             | 0.9935         | 0.9413   | 0.9919        |
| 0.0094        | 16.6667 | 450  | 0.0136          | 0.9767   | 0.9862        | 0.9950           | 0.9965              | 0.9646        | 0.9974             | 0.9940         | 0.9436   | 0.9924        |
| 0.0101        | 18.5185 | 500  | 0.0135          | 0.9767   | 0.9867        | 0.9950           | 0.9974              | 0.9663        | 0.9964             | 0.9940         | 0.9438   | 0.9924        |
| 0.0087        | 20.3704 | 550  | 0.0144          | 0.9764   | 0.9887        | 0.9949           | 0.9954              | 0.9736        | 0.9970             | 0.9935         | 0.9435   | 0.9923        |
| 0.0078        | 22.2222 | 600  | 0.0145          | 0.9760   | 0.9885        | 0.9949           | 0.9967              | 0.9727        | 0.9960             | 0.9938         | 0.9417   | 0.9924        |
| 0.0095        | 24.0741 | 650  | 0.0145          | 0.9753   | 0.9855        | 0.9948           | 0.9971              | 0.9626        | 0.9967             | 0.9939         | 0.9398   | 0.9921        |
| 0.0073        | 25.9259 | 700  | 0.0145          | 0.9761   | 0.9892        | 0.9949           | 0.9965              | 0.9752        | 0.9960             | 0.9938         | 0.9419   | 0.9925        |
| 0.009         | 27.7778 | 750  | 0.0143          | 0.9772   | 0.9891        | 0.9951           | 0.9958              | 0.9745        | 0.9970             | 0.9938         | 0.9451   | 0.9929        |
| 0.0049        | 29.6296 | 800  | 0.0143          | 0.9782   | 0.9883        | 0.9953           | 0.9966              | 0.9713        | 0.9971             | 0.9942         | 0.9474   | 0.9929        |
| 0.0075        | 31.4815 | 850  | 0.0153          | 0.9767   | 0.9886        | 0.9951           | 0.9967              | 0.9727        | 0.9963             | 0.9941         | 0.9434   | 0.9925        |
| 0.008         | 33.3333 | 900  | 0.0155          | 0.9772   | 0.9876        | 0.9952           | 0.9970              | 0.9690        | 0.9968             | 0.9943         | 0.9447   | 0.9927        |
| 0.0061        | 35.1852 | 950  | 0.0150          | 0.9777   | 0.9877        | 0.9953           | 0.9973              | 0.9691        | 0.9967             | 0.9943         | 0.9461   | 0.9928        |
| 0.0053        | 37.0370 | 1000 | 0.0156          | 0.9776   | 0.9882        | 0.9952           | 0.9974              | 0.9708        | 0.9963             | 0.9942         | 0.9458   | 0.9927        |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1