---
license: apache-2.0
base_model: microsoft/beit-base-finetuned-ade-640-640
tags:
- generated_from_trainer
model-index:
- name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_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. -->

# BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_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: 0.0652
- Mean Iou: 0.9446
- Mean Accuracy: 0.9687
- Overall Accuracy: 0.9855
- Accuracy Background: 0.9893
- Accuracy Melt: 0.9263
- Accuracy Substrate: 0.9905
- Iou Background: 0.9796
- Iou Melt: 0.8757
- Iou Substrate: 0.9785

## 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: 16
- eval_batch_size: 16
- 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: 50

### 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1893        | 3.7037  | 100  | 0.1358          | 0.9180   | 0.9486        | 0.9815           | 0.9923              | 0.8651        | 0.9884             | 0.9821         | 0.7997   | 0.9723        |
| 0.0951        | 7.4074  | 200  | 0.0746          | 0.9483   | 0.9791        | 0.9876           | 0.9909              | 0.9575        | 0.9890             | 0.9860         | 0.8775   | 0.9814        |
| 0.0892        | 11.1111 | 300  | 0.0631          | 0.9489   | 0.9772        | 0.9866           | 0.9875              | 0.9536        | 0.9903             | 0.9817         | 0.8854   | 0.9796        |
| 0.0878        | 14.8148 | 400  | 0.0692          | 0.9375   | 0.9687        | 0.9829           | 0.9853              | 0.9332        | 0.9877             | 0.9742         | 0.8632   | 0.9750        |
| 0.07          | 18.5185 | 500  | 0.0631          | 0.9419   | 0.9700        | 0.9844           | 0.9865              | 0.9339        | 0.9897             | 0.9777         | 0.8714   | 0.9767        |
| 0.0507        | 22.2222 | 600  | 0.0646          | 0.9379   | 0.9659        | 0.9829           | 0.9879              | 0.9230        | 0.9869             | 0.9738         | 0.8653   | 0.9747        |
| 0.0523        | 25.9259 | 700  | 0.0569          | 0.9485   | 0.9724        | 0.9864           | 0.9903              | 0.9371        | 0.9898             | 0.9809         | 0.8855   | 0.9791        |
| 0.0402        | 29.6296 | 800  | 0.0622          | 0.9422   | 0.9686        | 0.9848           | 0.9881              | 0.9279        | 0.9897             | 0.9777         | 0.8711   | 0.9778        |
| 0.0406        | 33.3333 | 900  | 0.0631          | 0.9427   | 0.9683        | 0.9852           | 0.9894              | 0.9256        | 0.9899             | 0.9790         | 0.8707   | 0.9785        |
| 0.0389        | 37.0370 | 1000 | 0.0650          | 0.9436   | 0.9695        | 0.9852           | 0.9879              | 0.9303        | 0.9904             | 0.9784         | 0.8739   | 0.9784        |
| 0.0396        | 40.7407 | 1100 | 0.0634          | 0.9457   | 0.9700        | 0.9857           | 0.9900              | 0.9301        | 0.9899             | 0.9798         | 0.8787   | 0.9787        |
| 0.036         | 44.4444 | 1200 | 0.0653          | 0.9443   | 0.9684        | 0.9855           | 0.9894              | 0.9254        | 0.9906             | 0.9797         | 0.8748   | 0.9786        |
| 0.0224        | 48.1481 | 1300 | 0.0652          | 0.9446   | 0.9687        | 0.9855           | 0.9893              | 0.9263        | 0.9905             | 0.9796         | 0.8757   | 0.9785        |


### Framework versions

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