---
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
  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

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: 0.0899
- Mean Iou: 0.8524
- Mean Accuracy: 0.8932
- Overall Accuracy: 0.9653
- Accuracy Background: 0.9900
- Accuracy Melt: 0.7107
- Accuracy Substrate: 0.9788
- Iou Background: 0.9693
- Iou Melt: 0.6451
- Iou Substrate: 0.9429

## 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: 20

### 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1394        | 1.6129  | 50   | 0.2486          | 0.6252   | 0.6776        | 0.9171           | 0.9800              | 0.0713        | 0.9816             | 0.9285         | 0.0680   | 0.8792        |
| 0.2482        | 3.2258  | 100  | 0.2178          | 0.6883   | 0.7470        | 0.9224           | 0.9831              | 0.3037        | 0.9543             | 0.9307         | 0.2490   | 0.8854        |
| 0.1697        | 4.8387  | 150  | 0.2044          | 0.6993   | 0.7613        | 0.9236           | 0.9847              | 0.3511        | 0.9480             | 0.9313         | 0.2796   | 0.8871        |
| 0.139         | 6.4516  | 200  | 0.1897          | 0.7250   | 0.7835        | 0.9317           | 0.9771              | 0.4086        | 0.9648             | 0.9415         | 0.3395   | 0.8940        |
| 0.0951        | 8.0645  | 250  | 0.1879          | 0.6863   | 0.7344        | 0.9291           | 0.9851              | 0.2414        | 0.9766             | 0.9372         | 0.2290   | 0.8928        |
| 0.0812        | 9.6774  | 300  | 0.1875          | 0.7513   | 0.8449        | 0.9285           | 0.9636              | 0.6338        | 0.9372             | 0.9370         | 0.4265   | 0.8903        |
| 0.1349        | 11.2903 | 350  | 0.2020          | 0.6825   | 0.7357        | 0.9247           | 0.9810              | 0.2577        | 0.9685             | 0.9328         | 0.2265   | 0.8882        |
| 0.1312        | 12.9032 | 400  | 0.1401          | 0.7627   | 0.8053        | 0.9465           | 0.9864              | 0.4477        | 0.9816             | 0.9624         | 0.4169   | 0.9090        |
| 0.1061        | 14.5161 | 450  | 0.1051          | 0.8297   | 0.8811        | 0.9586           | 0.9890              | 0.6853        | 0.9691             | 0.9657         | 0.5932   | 0.9302        |
| 0.0287        | 16.1290 | 500  | 0.1045          | 0.8349   | 0.8835        | 0.9598           | 0.9850              | 0.6905        | 0.9749             | 0.9640         | 0.6073   | 0.9335        |
| 0.2051        | 17.7419 | 550  | 0.0928          | 0.8466   | 0.8868        | 0.9644           | 0.9875              | 0.6906        | 0.9824             | 0.9687         | 0.6290   | 0.9420        |
| 0.0898        | 19.3548 | 600  | 0.0899          | 0.8524   | 0.8932        | 0.9653           | 0.9900              | 0.7107        | 0.9788             | 0.9693         | 0.6451   | 0.9429        |


### Framework versions

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