---
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: 0.0582
- Mean Iou: 0.8583
- Mean Accuracy: 0.9104
- Overall Accuracy: 0.9803
- Accuracy Unlabeled: nan
- Accuracy Collar: 0.8741
- Accuracy Polo: 0.9786
- Accuracy Lines-cuff: 0.7185
- Accuracy Lines-chest: 0.9188
- Accuracy Human: 0.9805
- Accuracy Background: 0.9920
- Iou Unlabeled: nan
- Iou Collar: 0.8111
- Iou Polo: 0.9580
- Iou Lines-cuff: 0.6290
- Iou Lines-chest: 0.8101
- Iou Human: 0.9553
- Iou Background: 0.9863

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:-------------------:|:--------------------:|:--------------:|:-------------------:|:-------------:|:----------:|:--------:|:--------------:|:---------------:|:---------:|:--------------:|
| 0.1564        | 4.0   | 20   | 0.1474          | 0.5073   | 0.6210        | 0.9633           | nan                | 0.7561          | 0.9797        | 0.0183              | 0.0138               | 0.9829         | 0.9750              | 0.0           | 0.6873     | 0.9282   | 0.0182         | 0.0131          | 0.9332    | 0.9714         |
| 0.0912        | 8.0   | 40   | 0.0812          | 0.7332   | 0.7637        | 0.9751           | nan                | 0.8012          | 0.9798        | 0.1221              | 0.7058               | 0.9886         | 0.9847              | nan           | 0.7652     | 0.9512   | 0.1220         | 0.6314          | 0.9483    | 0.9811         |
| 0.0724        | 12.0  | 60   | 0.0693          | 0.8345   | 0.8765        | 0.9791           | nan                | 0.8651          | 0.9817        | 0.5794              | 0.8633               | 0.9810         | 0.9888              | nan           | 0.8025     | 0.9570   | 0.5407         | 0.7688          | 0.9537    | 0.9844         |
| 0.0609        | 16.0  | 80   | 0.0633          | 0.8506   | 0.9022        | 0.9796           | nan                | 0.8697          | 0.9771        | 0.6806              | 0.9123               | 0.9826         | 0.9907              | nan           | 0.8093     | 0.9573   | 0.6053         | 0.7921          | 0.9541    | 0.9853         |
| 0.0602        | 20.0  | 100  | 0.0629          | 0.8493   | 0.8960        | 0.9794           | nan                | 0.8576          | 0.9789        | 0.6686              | 0.8987               | 0.9818         | 0.9904              | nan           | 0.8024     | 0.9569   | 0.6004         | 0.7974          | 0.9532    | 0.9855         |
| 0.0536        | 24.0  | 120  | 0.0588          | 0.8556   | 0.9083        | 0.9801           | nan                | 0.8709          | 0.9788        | 0.7104              | 0.9172               | 0.9823         | 0.9902              | nan           | 0.8090     | 0.9581   | 0.6244         | 0.8011          | 0.9552    | 0.9856         |
| 0.0459        | 28.0  | 140  | 0.0582          | 0.8583   | 0.9104        | 0.9803           | nan                | 0.8741          | 0.9786        | 0.7185              | 0.9188               | 0.9805         | 0.9920              | nan           | 0.8111     | 0.9580   | 0.6290         | 0.8101          | 0.9553    | 0.9863         |


### Framework versions

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