---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001
  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_Not-Augmented_4_lr0.0001

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: 0.0217
- Mean Iou: 0.9708
- Mean Accuracy: 0.9835
- Overall Accuracy: 0.9941
- Accuracy Background: 0.9965
- Accuracy Melt: 0.9584
- Accuracy Substrate: 0.9957
- Iou Background: 0.9940
- Iou Melt: 0.9288
- Iou Substrate: 0.9895

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1107        | 0.8850  | 50   | 0.1152          | 0.8138   | 0.8439        | 0.9627           | 0.9781              | 0.5623        | 0.9914             | 0.9677         | 0.5412   | 0.9325        |
| 0.0564        | 1.7699  | 100  | 0.0520          | 0.9163   | 0.9432        | 0.9829           | 0.9967              | 0.8488        | 0.9841             | 0.9806         | 0.7963   | 0.9721        |
| 0.0296        | 2.6549  | 150  | 0.0270          | 0.9557   | 0.9821        | 0.9906           | 0.9916              | 0.9621        | 0.9928             | 0.9893         | 0.8939   | 0.9839        |
| 0.042         | 3.5398  | 200  | 0.0226          | 0.9619   | 0.9763        | 0.9922           | 0.9934              | 0.9384        | 0.9969             | 0.9917         | 0.9077   | 0.9862        |
| 0.0166        | 4.4248  | 250  | 0.0300          | 0.9616   | 0.9768        | 0.9904           | 0.9957              | 0.9446        | 0.9903             | 0.9872         | 0.9153   | 0.9823        |
| 0.0159        | 5.3097  | 300  | 0.0203          | 0.9658   | 0.9863        | 0.9931           | 0.9946              | 0.9701        | 0.9941             | 0.9923         | 0.9169   | 0.9883        |
| 0.0121        | 6.1947  | 350  | 0.0221          | 0.9645   | 0.9795        | 0.9928           | 0.9937              | 0.9480        | 0.9968             | 0.9923         | 0.9141   | 0.9872        |
| 0.0149        | 7.0796  | 400  | 0.0220          | 0.9648   | 0.9821        | 0.9930           | 0.9949              | 0.9565        | 0.9951             | 0.9930         | 0.9138   | 0.9874        |
| 0.0352        | 7.9646  | 450  | 0.0215          | 0.9658   | 0.9764        | 0.9933           | 0.9959              | 0.9361        | 0.9971             | 0.9935         | 0.9158   | 0.9880        |
| 0.0106        | 8.8496  | 500  | 0.0201          | 0.9696   | 0.9820        | 0.9939           | 0.9961              | 0.9535        | 0.9962             | 0.9938         | 0.9256   | 0.9892        |
| 0.0095        | 9.7345  | 550  | 0.0216          | 0.9674   | 0.9796        | 0.9936           | 0.9955              | 0.9463        | 0.9969             | 0.9936         | 0.9202   | 0.9886        |
| 0.009         | 10.6195 | 600  | 0.0209          | 0.9702   | 0.9821        | 0.9941           | 0.9966              | 0.9539        | 0.9960             | 0.9940         | 0.9273   | 0.9894        |
| 0.0106        | 11.5044 | 650  | 0.0211          | 0.9700   | 0.9830        | 0.9940           | 0.9964              | 0.9568        | 0.9958             | 0.9940         | 0.9266   | 0.9893        |
| 0.0099        | 12.3894 | 700  | 0.0217          | 0.9708   | 0.9835        | 0.9941           | 0.9965              | 0.9584        | 0.9957             | 0.9940         | 0.9288   | 0.9895        |


### Framework versions

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