README.md

---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: Test-SegFormer_Mixed_Set2_788images_mit-b5_RGB
  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. -->

# Test-SegFormer_Mixed_Set2_788images_mit-b5_RGB

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: 0.1661
- Mean Iou: 0.7900
- Mean Accuracy: 0.8412
- Overall Accuracy: 0.9471
- Accuracy Background: 0.9799
- Accuracy Melt: 0.5677
- Accuracy Substrate: 0.9760
- Iou Background: 0.9430
- Iou Melt: 0.4987
- Iou Substrate: 0.9285

## 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: 1

### 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1897        | 0.7042 | 50   | 0.1661          | 0.7900   | 0.8412        | 0.9471           | 0.9799              | 0.5677        | 0.9760             | 0.9430         | 0.4987   | 0.9285        |


### Framework versions

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