---
license: other
base_model: microsoft/beit-base-finetuned-ade-640-640
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set1_RGB
  results: []
pipeline_tag: image-segmentation
---
<!-- 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. -->

# BEiT_beit-base-finetuned-ade-640-640_Clean-Set1_RGB

This model is a fine-tuned version of [microsoft/beit-base-finetuned-ade-640-640](https://huggingface.co/microsoft/beit-base-finetuned-ade-640-640) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0603
- Mean Iou: 0.9672
- Mean Accuracy: 0.9774
- Overall Accuracy: 0.9930
- Accuracy Background: 0.9961
- Accuracy Melt: 0.9392
- Accuracy Substrate: 0.9971
- Iou Background: 0.9929
- Iou Melt: 0.9207
- Iou Substrate: 0.9879

## 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: 2e-05
- 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: 200
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.3924        | 5.5556  | 50   | 0.3038          | 0.9022   | 0.9499        | 0.9809           | 0.9854              | 0.8738        | 0.9906             | 0.9853         | 0.7493   | 0.9719        |
| 0.0857        | 11.1111 | 100  | 0.0788          | 0.9656   | 0.9771        | 0.9931           | 0.9972              | 0.9377        | 0.9964             | 0.9939         | 0.9146   | 0.9883        |
| 0.0816        | 16.6667 | 150  | 0.0603          | 0.9672   | 0.9774        | 0.9930           | 0.9961              | 0.9392        | 0.9971             | 0.9929         | 0.9207   | 0.9879        |


### Framework versions

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