---
license: other
base_model: sayeed99/segformer-b3-fashion
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b3-fashion-finetuned-polo-segments-aug-07-v1.2
  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-b3-fashion-finetuned-polo-segments-aug-07-v1.2

This model is a fine-tuned version of [sayeed99/segformer-b3-fashion](https://huggingface.co/sayeed99/segformer-b3-fashion) on the sshk/polo-badges-segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0801
- Mean Iou: 0.8698
- Mean Accuracy: 0.9244
- Overall Accuracy: 0.9721
- Accuracy Unlabeled: nan
- Accuracy Collar: 0.8554
- Accuracy Polo: 0.9713
- Accuracy Lines-cuff: 0.8045
- Accuracy Lines-chest: 0.9543
- Accuracy Human: 0.9710
- Accuracy Background: 0.9896
- Iou Unlabeled: nan
- Iou Collar: 0.7817
- Iou Polo: 0.9387
- Iou Lines-cuff: 0.7252
- Iou Lines-chest: 0.8492
- Iou Human: 0.9456
- Iou Background: 0.9783

## 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: 6e-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: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Collar | Accuracy Polo | Accuracy Lines-cuff | Accuracy Lines-chest | Accuracy Human | Accuracy Background | Iou Unlabeled | Iou Collar | Iou Polo | Iou Lines-cuff | Iou Lines-chest | Iou Human | Iou Background |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:-------------------:|:--------------------:|:--------------:|:-------------------:|:-------------:|:----------:|:--------:|:--------------:|:---------------:|:---------:|:--------------:|
| 0.1958        | 4.0   | 20   | 0.1935          | 0.5473   | 0.5809        | 0.9484           | nan                | 0.5688          | 0.9710        | 0.0                 | 0.0                  | 0.9699         | 0.9754              | nan           | 0.5048     | 0.8825   | 0.0            | 0.0             | 0.9291    | 0.9676         |
| 0.0878        | 8.0   | 40   | 0.1158          | 0.7585   | 0.7864        | 0.9643           | nan                | 0.7548          | 0.9721        | 0.3063              | 0.7322               | 0.9787         | 0.9740              | nan           | 0.7084     | 0.9275   | 0.3054         | 0.7048          | 0.9353    | 0.9694         |
| 0.0737        | 12.0  | 60   | 0.0929          | 0.8595   | 0.9043        | 0.9708           | nan                | 0.8229          | 0.9704        | 0.7743              | 0.8982               | 0.9757         | 0.9842              | nan           | 0.7641     | 0.9366   | 0.7031         | 0.8330          | 0.9436    | 0.9766         |
| 0.0646        | 16.0  | 80   | 0.0868          | 0.8643   | 0.9140        | 0.9711           | nan                | 0.8521          | 0.9747        | 0.7778              | 0.9226               | 0.9662         | 0.9909              | nan           | 0.7807     | 0.9359   | 0.7101         | 0.8379          | 0.9435    | 0.9774         |
| 0.0688        | 20.0  | 100  | 0.0819          | 0.8665   | 0.9176        | 0.9720           | nan                | 0.8502          | 0.9721        | 0.7841              | 0.9386               | 0.9709         | 0.9899              | nan           | 0.7814     | 0.9384   | 0.7139         | 0.8418          | 0.9459    | 0.9778         |
| 0.052         | 24.0  | 120  | 0.0821          | 0.8652   | 0.9207        | 0.9716           | nan                | 0.8302          | 0.9646        | 0.8053              | 0.9590               | 0.9769         | 0.9883              | nan           | 0.7694     | 0.9381   | 0.7210         | 0.8400          | 0.9445    | 0.9781         |
| 0.0483        | 28.0  | 140  | 0.0801          | 0.8698   | 0.9244        | 0.9721           | nan                | 0.8554          | 0.9713        | 0.8045              | 0.9543               | 0.9710         | 0.9896              | nan           | 0.7817     | 0.9387   | 0.7252         | 0.8492          | 0.9456    | 0.9783         |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1