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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-v1.5
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-v1.5
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: 0.1007
- Mean Iou: 0.8404
- Mean Accuracy: 0.9136
- Overall Accuracy: 0.9704
- Accuracy Unlabeled: nan
- Accuracy Collar: 0.8876
- Accuracy Polo: 0.9746
- Accuracy Lines-cuff: 0.7358
- Accuracy Lines-chest: 0.9360
- Accuracy Human: 0.9631
- Accuracy Background: 0.9848
- Accuracy Tape: nan
- Iou Unlabeled: nan
- Iou Collar: 0.7360
- Iou Polo: 0.9428
- Iou Lines-cuff: 0.6178
- Iou Lines-chest: 0.8353
- Iou Human: 0.9386
- Iou Background: 0.9718
- Iou Tape: nan
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:-------------------:|:--------------------:|:--------------:|:-------------------:|:-------------:|:-------------:|:----------:|:--------:|:--------------:|:---------------:|:---------:|:--------------:|:--------:|
| 0.1679 | 2.2222 | 20 | 0.2031 | 0.5532 | 0.5985 | 0.9492 | nan | 0.6856 | 0.9707 | 0.0 | 0.0022 | 0.9482 | 0.9843 | nan | nan | 0.5491 | 0.8882 | 0.0 | 0.0022 | 0.9192 | 0.9604 | nan |
| 0.0921 | 4.4444 | 40 | 0.1359 | 0.7103 | 0.7618 | 0.9631 | nan | 0.8586 | 0.9739 | 0.1373 | 0.6598 | 0.9577 | 0.9835 | nan | nan | 0.6786 | 0.9244 | 0.1373 | 0.6217 | 0.9305 | 0.9691 | nan |
| 0.0603 | 6.6667 | 60 | 0.1166 | 0.8147 | 0.8651 | 0.9672 | nan | 0.8436 | 0.9795 | 0.6385 | 0.7867 | 0.9586 | 0.9837 | nan | nan | 0.7114 | 0.9315 | 0.5955 | 0.7446 | 0.9352 | 0.9700 | nan |
| 0.0581 | 8.8889 | 80 | 0.1121 | 0.8185 | 0.8809 | 0.9677 | nan | 0.8363 | 0.9767 | 0.6995 | 0.8279 | 0.9594 | 0.9857 | nan | nan | 0.7091 | 0.9336 | 0.6009 | 0.7611 | 0.9357 | 0.9709 | nan |
| 0.0445 | 11.1111 | 100 | 0.1047 | 0.8317 | 0.9033 | 0.9699 | nan | 0.8719 | 0.9687 | 0.7198 | 0.9070 | 0.9686 | 0.9836 | nan | nan | 0.7263 | 0.9403 | 0.6081 | 0.8045 | 0.9390 | 0.9721 | nan |
| 0.0456 | 13.3333 | 120 | 0.1055 | 0.8342 | 0.9151 | 0.9694 | nan | 0.8931 | 0.9687 | 0.7391 | 0.9402 | 0.9614 | 0.9878 | nan | nan | 0.7285 | 0.9405 | 0.6102 | 0.8184 | 0.9371 | 0.9708 | nan |
| 0.0443 | 15.5556 | 140 | 0.1034 | 0.8349 | 0.9039 | 0.9700 | nan | 0.8740 | 0.9742 | 0.7208 | 0.9059 | 0.9636 | 0.9851 | nan | nan | 0.7324 | 0.9411 | 0.6091 | 0.8166 | 0.9384 | 0.9717 | nan |
| 0.0475 | 17.7778 | 160 | 0.1032 | 0.8384 | 0.9139 | 0.9699 | nan | 0.8885 | 0.9738 | 0.7383 | 0.9356 | 0.9604 | 0.9868 | nan | nan | 0.7341 | 0.9409 | 0.6160 | 0.8300 | 0.9377 | 0.9717 | nan |
| 0.0411 | 20.0 | 180 | 0.1018 | 0.8403 | 0.9150 | 0.9702 | nan | 0.8911 | 0.9770 | 0.7389 | 0.9378 | 0.9592 | 0.9862 | nan | nan | 0.7362 | 0.9417 | 0.6194 | 0.8346 | 0.9383 | 0.9716 | nan |
| 0.0345 | 22.2222 | 200 | 0.1003 | 0.8397 | 0.9112 | 0.9704 | nan | 0.8885 | 0.9768 | 0.7359 | 0.9201 | 0.9625 | 0.9836 | nan | nan | 0.7355 | 0.9423 | 0.6157 | 0.8345 | 0.9387 | 0.9716 | nan |
| 0.0403 | 24.4444 | 220 | 0.1007 | 0.8397 | 0.9140 | 0.9705 | nan | 0.8826 | 0.9745 | 0.7393 | 0.9392 | 0.9633 | 0.9851 | nan | nan | 0.7353 | 0.9434 | 0.6172 | 0.8319 | 0.9388 | 0.9716 | nan |
| 0.0563 | 26.6667 | 240 | 0.1009 | 0.8406 | 0.9140 | 0.9704 | nan | 0.8914 | 0.9765 | 0.7306 | 0.9391 | 0.9603 | 0.9859 | nan | nan | 0.7360 | 0.9427 | 0.6202 | 0.8344 | 0.9383 | 0.9718 | nan |
| 0.0464 | 28.8889 | 260 | 0.1007 | 0.8404 | 0.9136 | 0.9704 | nan | 0.8876 | 0.9746 | 0.7358 | 0.9360 | 0.9631 | 0.9848 | nan | nan | 0.7360 | 0.9428 | 0.6178 | 0.8353 | 0.9386 | 0.9718 | nan |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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