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---
license: other
base_model: sayeed99/segformer-b3-fashion
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b3-fashion-finetuned-polo-segments-v1.4
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-v1.4
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: 0.0547
- Mean Iou: 0.7482
- Mean Accuracy: 0.9206
- Overall Accuracy: 0.9823
- Accuracy Unlabeled: nan
- Accuracy Collar: 0.8807
- Accuracy Polo: 0.9847
- Accuracy Lines-cuff: 0.7598
- Accuracy Lines-chest: 0.9230
- Accuracy Human: 0.9823
- Accuracy Background: 0.9929
- Accuracy Tape: nan
- Iou Unlabeled: nan
- Iou Collar: 0.8276
- Iou Polo: 0.9580
- Iou Lines-cuff: 0.6735
- Iou Lines-chest: 0.8230
- Iou Human: 0.9680
- Iou Background: 0.9872
- Iou Tape: 0.0
## 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 | Accuracy Tape | Iou Unlabeled | Iou Collar | Iou Polo | Iou Lines-cuff | Iou Lines-chest | Iou Human | Iou Background | Iou Tape |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:-------------------:|:--------------------:|:--------------:|:-------------------:|:-------------:|:-------------:|:----------:|:--------:|:--------------:|:---------------:|:---------:|:--------------:|:--------:|
| 0.206 | 2.5 | 20 | 0.1808 | 0.5772 | 0.6083 | 0.9552 | nan | 0.6933 | 0.9875 | 0.0 | 0.0304 | 0.9818 | 0.9566 | nan | nan | 0.6406 | 0.9100 | 0.0 | 0.0276 | 0.9299 | 0.9553 | nan |
| 0.0873 | 5.0 | 40 | 0.0882 | 0.7806 | 0.8207 | 0.9768 | nan | 0.8457 | 0.9848 | 0.2359 | 0.8896 | 0.9780 | 0.9904 | nan | nan | 0.7808 | 0.9460 | 0.2351 | 0.7783 | 0.9605 | 0.9827 | nan |
| 0.0648 | 7.5 | 60 | 0.0712 | 0.8502 | 0.8900 | 0.9794 | nan | 0.8586 | 0.9880 | 0.6659 | 0.8584 | 0.9796 | 0.9892 | nan | nan | 0.8059 | 0.9499 | 0.6054 | 0.7918 | 0.9642 | 0.9842 | nan |
| 0.0607 | 10.0 | 80 | 0.0631 | 0.8556 | 0.8957 | 0.9806 | nan | 0.8586 | 0.9856 | 0.7087 | 0.8477 | 0.9829 | 0.9907 | nan | nan | 0.8055 | 0.9539 | 0.6394 | 0.7834 | 0.9659 | 0.9856 | nan |
| 0.057 | 12.5 | 100 | 0.0605 | 0.8661 | 0.9135 | 0.9815 | nan | 0.8708 | 0.9818 | 0.7296 | 0.9224 | 0.9855 | 0.9908 | nan | nan | 0.8148 | 0.9570 | 0.6577 | 0.8144 | 0.9669 | 0.9859 | nan |
| 0.0458 | 15.0 | 120 | 0.0573 | 0.7446 | 0.9169 | 0.9819 | nan | 0.8925 | 0.9838 | 0.7505 | 0.9009 | 0.9792 | 0.9949 | nan | nan | 0.8244 | 0.9581 | 0.6600 | 0.8164 | 0.9669 | 0.9863 | 0.0 |
| 0.0413 | 17.5 | 140 | 0.0587 | 0.7428 | 0.9196 | 0.9818 | nan | 0.8818 | 0.9820 | 0.7483 | 0.9299 | 0.9823 | 0.9932 | nan | nan | 0.8217 | 0.9571 | 0.6671 | 0.7997 | 0.9673 | 0.9869 | 0.0 |
| 0.0449 | 20.0 | 160 | 0.0542 | 0.7468 | 0.9202 | 0.9826 | nan | 0.8850 | 0.9833 | 0.7516 | 0.9248 | 0.9842 | 0.9925 | nan | nan | 0.8270 | 0.9590 | 0.6678 | 0.8179 | 0.9688 | 0.9873 | 0.0 |
| 0.0394 | 22.5 | 180 | 0.0558 | 0.7468 | 0.9208 | 0.9819 | nan | 0.8934 | 0.9853 | 0.7528 | 0.9207 | 0.9808 | 0.9919 | nan | nan | 0.8298 | 0.9564 | 0.6657 | 0.8214 | 0.9672 | 0.9869 | 0.0 |
| 0.0472 | 25.0 | 200 | 0.0549 | 0.7474 | 0.9185 | 0.9823 | nan | 0.8792 | 0.9854 | 0.7531 | 0.9186 | 0.9828 | 0.9922 | nan | nan | 0.8274 | 0.9577 | 0.6681 | 0.8233 | 0.9681 | 0.9871 | 0.0 |
| 0.0452 | 27.5 | 220 | 0.0547 | 0.7482 | 0.9217 | 0.9823 | nan | 0.8837 | 0.9846 | 0.7622 | 0.9247 | 0.9823 | 0.9927 | nan | nan | 0.8287 | 0.9580 | 0.6733 | 0.8221 | 0.9681 | 0.9871 | 0.0 |
| 0.0392 | 30.0 | 240 | 0.0547 | 0.7482 | 0.9206 | 0.9823 | nan | 0.8807 | 0.9847 | 0.7598 | 0.9230 | 0.9823 | 0.9929 | nan | nan | 0.8276 | 0.9580 | 0.6735 | 0.8230 | 0.9680 | 0.9872 | 0.0 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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