---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Clean-Set3_Augmented_Medium
  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_mit-b5_Clean-Set3_Augmented_Medium

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: 0.0205
- Mean Iou: 0.9709
- Mean Accuracy: 0.9837
- Overall Accuracy: 0.9928
- Accuracy Background: 0.9979
- Accuracy Melt: 0.9593
- Accuracy Substrate: 0.9938
- Iou Background: 0.9927
- Iou Melt: 0.9315
- Iou Substrate: 0.9885

## 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: 0.0002
- 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: 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1481        | 0.3968 | 50   | 0.1951          | 0.7748   | 0.8972        | 0.9261           | 0.9808              | 0.8114        | 0.8996             | 0.9583         | 0.4859   | 0.8802        |
| 0.1364        | 0.7937 | 100  | 0.0745          | 0.8855   | 0.9155        | 0.9727           | 0.9933              | 0.7647        | 0.9884             | 0.9753         | 0.7206   | 0.9604        |
| 0.0776        | 1.1905 | 150  | 0.1238          | 0.8046   | 0.8351        | 0.9565           | 0.9922              | 0.5172        | 0.9960             | 0.9754         | 0.5059   | 0.9325        |
| 0.0851        | 1.5873 | 200  | 0.0878          | 0.8651   | 0.8901        | 0.9687           | 0.9923              | 0.6841        | 0.9940             | 0.9791         | 0.6665   | 0.9498        |
| 0.1893        | 1.9841 | 250  | 0.0602          | 0.9077   | 0.9628        | 0.9766           | 0.9958              | 0.9233        | 0.9693             | 0.9872         | 0.7755   | 0.9602        |
| 0.1236        | 2.3810 | 300  | 0.0643          | 0.9042   | 0.9455        | 0.9768           | 0.9971              | 0.8609        | 0.9783             | 0.9758         | 0.7669   | 0.9699        |
| 0.0809        | 2.7778 | 350  | 0.0387          | 0.9408   | 0.9699        | 0.9857           | 0.9964              | 0.9271        | 0.9862             | 0.9895         | 0.8562   | 0.9769        |
| 0.0357        | 3.1746 | 400  | 0.0364          | 0.9431   | 0.9701        | 0.9860           | 0.9948              | 0.9274        | 0.9880             | 0.9897         | 0.8629   | 0.9767        |
| 0.0408        | 3.5714 | 450  | 0.0424          | 0.9349   | 0.9815        | 0.9834           | 0.9936              | 0.9745        | 0.9765             | 0.9904         | 0.8434   | 0.9708        |
| 0.0973        | 3.9683 | 500  | 0.0541          | 0.9172   | 0.9798        | 0.9785           | 0.9941              | 0.9796        | 0.9655             | 0.9903         | 0.7997   | 0.9615        |
| 0.0274        | 4.3651 | 550  | 0.0256          | 0.9636   | 0.9830        | 0.9904           | 0.9967              | 0.9629        | 0.9894             | 0.9893         | 0.9170   | 0.9844        |
| 0.04          | 4.7619 | 600  | 0.0329          | 0.9482   | 0.9696        | 0.9877           | 0.9977              | 0.9210        | 0.9899             | 0.9888         | 0.8743   | 0.9815        |
| 0.0301        | 5.1587 | 650  | 0.0247          | 0.9609   | 0.9756        | 0.9905           | 0.9975              | 0.9361        | 0.9933             | 0.9917         | 0.9067   | 0.9844        |
| 0.0137        | 5.5556 | 700  | 0.0214          | 0.9667   | 0.9779        | 0.9919           | 0.9962              | 0.9414        | 0.9962             | 0.9924         | 0.9206   | 0.9870        |
| 0.019         | 5.9524 | 750  | 0.0243          | 0.9619   | 0.9817        | 0.9907           | 0.9959              | 0.9577        | 0.9915             | 0.9916         | 0.9090   | 0.9851        |
| 0.021         | 6.3492 | 800  | 0.0200          | 0.9678   | 0.9853        | 0.9920           | 0.9972              | 0.9668        | 0.9917             | 0.9926         | 0.9237   | 0.9871        |
| 0.0156        | 6.7460 | 850  | 0.0211          | 0.9689   | 0.9813        | 0.9924           | 0.9965              | 0.9520        | 0.9954             | 0.9926         | 0.9259   | 0.9880        |
| 0.0153        | 7.1429 | 900  | 0.0205          | 0.9685   | 0.9842        | 0.9923           | 0.9970              | 0.9626        | 0.9930             | 0.9930         | 0.9249   | 0.9876        |
| 0.0125        | 7.5397 | 950  | 0.0205          | 0.9709   | 0.9837        | 0.9928           | 0.9979              | 0.9593        | 0.9938             | 0.9927         | 0.9315   | 0.9885        |


### Framework versions

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