---
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