README.md
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1 ---
2 license: other
3 tags:
4 - vision
5 - image-segmentation
6 widget:
7 - src: >-
8 https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
9 example_title: Person
10 - src: >-
11 https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
12 example_title: Person
13 datasets:
14 - mattmdjaga/human_parsing_dataset
15 ---
16 # Segformer B2 fine-tuned for clothes segmentation
17
18 SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
19 The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
20
21 **[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**.
22 ```python
23 from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
24 from PIL import Image
25 import requests
26 import matplotlib.pyplot as plt
27 import torch.nn as nn
28
29 processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
30 model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
31
32 url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
33
34 image = Image.open(requests.get(url, stream=True).raw)
35 inputs = processor(images=image, return_tensors="pt")
36
37 outputs = model(**inputs)
38 logits = outputs.logits.cpu()
39
40 upsampled_logits = nn.functional.interpolate(
41 logits,
42 size=image.size[::-1],
43 mode="bilinear",
44 align_corners=False,
45 )
46
47 pred_seg = upsampled_logits.argmax(dim=1)[0]
48 plt.imshow(pred_seg)
49 ```
50
51 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"
52
53 ### Evaluation
54
55 | Label Index | Label Name | Category Accuracy | Category IoU |
56 |:-------------:|:----------------:|:-----------------:|:------------:|
57 | 0 | Background | 0.99 | 0.99 |
58 | 1 | Hat | 0.73 | 0.68 |
59 | 2 | Hair | 0.91 | 0.82 |
60 | 3 | Sunglasses | 0.73 | 0.63 |
61 | 4 | Upper-clothes | 0.87 | 0.78 |
62 | 5 | Skirt | 0.76 | 0.65 |
63 | 6 | Pants | 0.90 | 0.84 |
64 | 7 | Dress | 0.74 | 0.55 |
65 | 8 | Belt | 0.35 | 0.30 |
66 | 9 | Left-shoe | 0.74 | 0.58 |
67 | 10 | Right-shoe | 0.75 | 0.60 |
68 | 11 | Face | 0.92 | 0.85 |
69 | 12 | Left-leg | 0.90 | 0.82 |
70 | 13 | Right-leg | 0.90 | 0.81 |
71 | 14 | Left-arm | 0.86 | 0.74 |
72 | 15 | Right-arm | 0.82 | 0.73 |
73 | 16 | Bag | 0.91 | 0.84 |
74 | 17 | Scarf | 0.63 | 0.29 |
75
76 Overall Evaluation Metrics:
77 - Evaluation Loss: 0.15
78 - Mean Accuracy: 0.80
79 - Mean IoU: 0.69
80
81 ### License
82
83 The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
84
85 ### BibTeX entry and citation info
86
87 ```bibtex
88 @article{DBLP:journals/corr/abs-2105-15203,
89 author = {Enze Xie and
90 Wenhai Wang and
91 Zhiding Yu and
92 Anima Anandkumar and
93 Jose M. Alvarez and
94 Ping Luo},
95 title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
96 Transformers},
97 journal = {CoRR},
98 volume = {abs/2105.15203},
99 year = {2021},
100 url = {https://arxiv.org/abs/2105.15203},
101 eprinttype = {arXiv},
102 eprint = {2105.15203},
103 timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
104 biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
105 bibsource = {dblp computer science bibliography, https://dblp.org}
106 }
107 ```