nielsr HF staff commited on
Commit
3141758
1 Parent(s): 38c7417

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +90 -0
README.md ADDED
@@ -0,0 +1,90 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - vision
5
+ - image-classification
6
+ datasets:
7
+ - imagenet-1k
8
+ widget:
9
+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
10
+ example_title: Tiger
11
+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
12
+ example_title: Teapot
13
+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
14
+ example_title: Palace
15
+ ---
16
+
17
+ # Swin Transformer v2 (small-sized model)
18
+
19
+ Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer).
20
+
21
+ Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team.
22
+
23
+ ## Model description
24
+
25
+ The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.
26
+
27
+ Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.
28
+
29
+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png)
30
+
31
+ [Source](https://paperswithcode.com/method/swin-transformer)
32
+
33
+ ## Intended uses & limitations
34
+
35
+ You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swinv2) to look for
36
+ fine-tuned versions on a task that interests you.
37
+
38
+ ### How to use
39
+
40
+ Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
41
+
42
+ ```python
43
+ from transformers import AutoImageProcessor, AutoModelForImageClassification
44
+ from PIL import Image
45
+ import requests
46
+
47
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
48
+ image = Image.open(requests.get(url, stream=True).raw)
49
+
50
+ processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-small-patch4-window8-256")
51
+ model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-small-patch4-window8-256")
52
+
53
+ inputs = processor(images=image, return_tensors="pt")
54
+ outputs = model(**inputs)
55
+ logits = outputs.logits
56
+ # model predicts one of the 1000 ImageNet classes
57
+ predicted_class_idx = logits.argmax(-1).item()
58
+ print("Predicted class:", model.config.id2label[predicted_class_idx])
59
+ ```
60
+
61
+ For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swinv2.html#).
62
+
63
+ ### BibTeX entry and citation info
64
+
65
+ ```bibtex
66
+ @article{DBLP:journals/corr/abs-2111-09883,
67
+ author = {Ze Liu and
68
+ Han Hu and
69
+ Yutong Lin and
70
+ Zhuliang Yao and
71
+ Zhenda Xie and
72
+ Yixuan Wei and
73
+ Jia Ning and
74
+ Yue Cao and
75
+ Zheng Zhang and
76
+ Li Dong and
77
+ Furu Wei and
78
+ Baining Guo},
79
+ title = {Swin Transformer {V2:} Scaling Up Capacity and Resolution},
80
+ journal = {CoRR},
81
+ volume = {abs/2111.09883},
82
+ year = {2021},
83
+ url = {https://arxiv.org/abs/2111.09883},
84
+ eprinttype = {arXiv},
85
+ eprint = {2111.09883},
86
+ timestamp = {Thu, 02 Dec 2021 15:54:22 +0100},
87
+ biburl = {https://dblp.org/rec/journals/corr/abs-2111-09883.bib},
88
+ bibsource = {dblp computer science bibliography, https://dblp.org}
89
+ }
90
+ ```