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segformer_b3_clothes/.gitattributes
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segformer_b3_clothes/README.md
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---
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license: mit
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tags:
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- vision
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- image-segmentation
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widget:
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- src: >-
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https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
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example_title: Person
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- src: >-
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https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
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example_title: Person
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datasets:
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- mattmdjaga/human_parsing_dataset
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pipeline_tag: image-segmentation
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---
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# Segformer B3 fine-tuned for clothes segmentation
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SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
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The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
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**NEW** -
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**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version
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and a blog post with it to make it more friendly.
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```python
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from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
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from PIL import Image
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import requests
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import matplotlib.pyplot as plt
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import torch.nn as nn
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processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer_b3_clothes")
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model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer_b3_clothes")
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url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits.cpu()
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upsampled_logits = nn.functional.interpolate(
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logits,
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size=image.size[::-1],
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mode="bilinear",
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align_corners=False,
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)
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pred_seg = upsampled_logits.argmax(dim=1)[0]
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plt.imshow(pred_seg)
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```
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Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf"
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### Evaluation
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| Label Index | Label Name | Category Accuracy | Category IoU |
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|:-------------:|:----------------:|:-----------------:|:------------:|
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| 0 | Background | 0.99 | 0.99 |
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| 1 | Hat | 0.73 | 0.68 |
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| 2 | Hair | 0.91 | 0.82 |
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| 3 | Sunglasses | 0.73 | 0.63 |
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| 4 | Upper-clothes | 0.87 | 0.78 |
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| 5 | Skirt | 0.76 | 0.65 |
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| 6 | Pants | 0.90 | 0.84 |
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| 7 | Dress | 0.74 | 0.55 |
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| 8 | Belt | 0.35 | 0.30 |
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| 9 | Left-shoe | 0.74 | 0.58 |
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| 10 | Right-shoe | 0.75 | 0.60 |
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| 11 | Face | 0.92 | 0.85 |
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| 12 | Left-leg | 0.90 | 0.82 |
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| 13 | Right-leg | 0.90 | 0.81 |
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| 14 | Left-arm | 0.86 | 0.74 |
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| 15 | Right-arm | 0.82 | 0.73 |
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| 16 | Bag | 0.91 | 0.84 |
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| 17 | Scarf | 0.63 | 0.29 |
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Overall Evaluation Metrics:
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- Evaluation Loss: 0.15
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- Mean Accuracy: 0.80
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- Mean IoU: 0.69
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### License
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The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-2105-15203,
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author = {Enze Xie and
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Wenhai Wang and
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Zhiding Yu and
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Anima Anandkumar and
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Jose M. Alvarez and
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Ping Luo},
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title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
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Transformers},
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journal = {CoRR},
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volume = {abs/2105.15203},
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year = {2021},
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url = {https://arxiv.org/abs/2105.15203},
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eprinttype = {arXiv},
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eprint = {2105.15203},
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timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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segformer_b3_clothes/config.json
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{
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"_name_or_path": "nvidia/mit-b3",
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"architectures": [
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"SegformerForSemanticSegmentation"
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],
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"attention_probs_dropout_prob": 0.0,
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"classifier_dropout_prob": 0.1,
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"decoder_hidden_size": 768,
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"depths": [
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3,
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4,
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18,
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3
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],
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"downsampling_rates": [
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1,
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4,
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8,
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16
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],
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"drop_path_rate": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_sizes": [
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64,
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128,
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320,
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512
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],
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"id2label": {
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"0": "Background",
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"1": "Hat",
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"10": "Right-shoe",
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"11": "Face",
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"12": "Left-leg",
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"13": "Right-leg",
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"14": "Left-arm",
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"15": "Right-arm",
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"16": "Bag",
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"17": "Scarf",
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"2": "Hair",
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"3": "Sunglasses",
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"4": "Upper-clothes",
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"5": "Skirt",
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"6": "Pants",
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"7": "Dress",
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"8": "Belt",
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"9": "Left-shoe"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"label2id": {
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"Background": "0",
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"Bag": "16",
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"Belt": "8",
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"Dress": "7",
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"Face": "11",
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"Hair": "2",
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"Hat": "1",
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"Left-arm": "14",
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"Left-leg": "12",
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"Left-shoe": "9",
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"Pants": "6",
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"Right-arm": "15",
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"Right-leg": "13",
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"Right-shoe": "10",
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"Scarf": "17",
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"Skirt": "5",
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"Sunglasses": "3",
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"Upper-clothes": "4"
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},
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"layer_norm_eps": 1e-06,
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"mlp_ratios": [
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4,
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4,
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4,
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4
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],
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"model_type": "segformer",
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"num_attention_heads": [
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1,
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2,
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5,
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+
8
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],
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"num_channels": 3,
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"num_encoder_blocks": 4,
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"patch_sizes": [
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7,
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3,
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3,
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3
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],
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"reshape_last_stage": true,
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"semantic_loss_ignore_index": 255,
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"sr_ratios": [
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8,
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4,
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2,
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1
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],
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"strides": [
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4,
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2,
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2,
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2
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],
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"torch_dtype": "float32",
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"transformers_version": "4.38.1"
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}
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segformer_b3_clothes/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f70ae566c5773fb335796ebaa8acc924ac25eb97222c2b2967d44d2fc11568e6
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size 189029000
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segformer_b3_clothes/preprocessor_config.json
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{
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"do_normalize": true,
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"do_reduce_labels": false,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "SegformerImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 512,
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"width": 512
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}
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}
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