---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: SegFormer_Clean_Set1_240430_V2-Augmented_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. -->

# SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5_RGB

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_240430_V2-Augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4812
- Mean Iou: 0.6072
- Mean Accuracy: 0.6905
- Overall Accuracy: 0.8761
- Accuracy Background: 0.8967
- Accuracy Melt: 0.2439
- Accuracy Substrate: 0.9311
- Iou Background: 0.8325
- Iou Melt: 0.1423
- Iou Substrate: 0.8467

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.2826        | 6.6667 | 20   | 0.4812          | 0.6072   | 0.6905        | 0.8761           | 0.8967              | 0.2439        | 0.9311             | 0.8325         | 0.1423   | 0.8467        |


### Framework versions

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