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alt.freeweights.vision / swin-tiny-patch4-window7-224
From: microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.visionSubject:

swin-tiny-patch4-window7-224

Date: 2026-11-14Commit: d00d478Signed: 2DC28CC7B26A283B ✓

Image classification model with 28M parameters. Safetensors weights, 340 MB in all. Published by microsoft under Apache-2.0.

28M params340MBswin-tiny-patch4-window7-224safetensorspermissive
FactsParams28MSize340 MBFormatssafetensorsTaskimage classificationLicenseApache-2.0From sourcepermissive
$ fw get microsoft/swin-tiny-patch4-window7-224
# manifest sha256 e18d8b12945b… · 340 MB · 7 files
$ fw verify ./swin-tiny-patch4-window7-224 microsoft/swin-tiny-patch4-window7-224
From sourceHosted (signed)microsoft/swin-tiny-patch4-window7-224Last check: 2026-11-14 21:23 UTC
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Overview

Size
340 MB
Weights
113 MB (safetensors)
Parameters
28M
Formats
safetensors
Task
image classification
License
Apache-2.0 (permissive)
Files
7, each with a sha256
Publisher
microsoft
Added
2026-11-14
Section
alt.freeweights.vision
Lineage

Original release from the publisher.

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Publisher at the pinned commit→You

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From sourcehuggingface.co2026-11-14 21:23 UTCpassed

Model card

From the publisher at commit d00d478. Shown as plain text with links; images open at the publisher.

Swin Transformer (tiny-sized model)#

Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.

Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description#

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.

Image: model image

Source

Intended uses & limitations#

You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.

How to use#

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

For more code examples, we refer to the documentation.

BibTeX entry and citation info#
@article{DBLP:journals/corr/abs-2103-14030,
  author    = {Ze Liu and
               Yutong Lin and
               Yue Cao and
               Han Hu and
               Yixuan Wei and
               Zheng Zhang and
               Stephen Lin and
               Baining Guo},
  title     = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  journal   = {CoRR},
  volume    = {abs/2103.14030},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.14030},
  eprinttype = {arXiv},
  eprint    = {2103.14030},
  timestamp = {Thu, 08 Apr 2021 07:53:26 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Files

7 files · 340 MBWeights 113 MB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 7 files with their sha256
FileSizesha256
.gitattributes1.23 KB98ccb431c012ebfe976280fbd45aea4cec7409935868ccecf3954370f96732a1
README.md3.68 KB314aeef8e1d3f1961c03e1e19b24f2758d352ab970098ae6f48485525f6307fb
config.json71.8 KBb99b2d1d80b05ff592979047b4a34fb459dd53b82c8036bae1c2a9d1ea2c5ed3
model.safetensors113 MBa941acf68a76b365d67738d6c6dce4f0db3ea5224b7cff856d3f8f3753502d29
preprocessor_config.json255 Bcf47f0a7c8ff723066a62ca5cf536ba0f530ed1e797a0eab270f1304e2f880db
pytorch_model.bin113 MBf2a3c1bc4ebd5f87ab22331b134c593dd24288c2dbaf7071ad49b4d9c59842d6
tf_model.h5113 MB613ffde402c38d5626003db04e3578c64a32ddee8c1272333524315faaa4c2c7
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
d00d478bfa pinned now manifest updated, same commit2026-11-14 21:23 UTC7 files · 340 MB
d00d478bfa2026-11-14 21:23 UTC7 files · 340 MB

Use it

$ fw get microsoft/swin-tiny-patch4-window7-224
# manifest sha256 e18d8b12945b… · 340 MB · 7 files
$ fw verify ./swin-tiny-patch4-window7-224 microsoft/swin-tiny-patch4-window7-224
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/microsoft/swin-tiny-patch4-window7-224.json --dir ./swin-tiny-patch4-window7-224 $ python3 fw.py verify ./swin-tiny-patch4-window7-224 ./microsoft__swin-tiny-patch4-window7-224.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download microsoft/swin-tiny-patch4-window7-224 --revision d00d478bfaf1f417d34a1186673ad4b4be264c51 --local-dir ./swin-tiny-patch4-window7-224
Run at home

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-14
Text in
README.md
Text sha256
314aeef8e1d3f1961c03e1e19b24f2758d352ab970098ae6f48485525f6307fb
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.visionSubject: swin-tiny-patch4-window7-224Date: 2026-11-14Commit: d00d478Signed: 2DC28CC7B26A283B ✓
Upstream
microsoft/swin-tiny-patch4-window7-224
Pinned commit
d00d478bfaf1f417d34a1186673ad4b4be264c51
Manifest
swin-tiny-patch4-window7-224.json · .minisig
Manifest sha256
e18d8b12945b7d9f061fd676e54c7e61825f75cd49d6541e93a6972e454a7599
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-14 21:23 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/microsoft/swin-tiny-patch4-window7-224.json $ curl -O https://freeweights.allternit.com/manifests/microsoft/swin-tiny-patch4-window7-224.json.minisig $ minisign -Vm swin-tiny-patch4-window7-224.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

1 model in the catalog name this one as their parent.

Quantizations
1
Quantizations 1
Get it
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$ HF_ENDPOINT=https://freeweights.allternit.com hf download microsoft/swin-tiny-patch4-window7-224
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Signed manifest
minisign, key 2DC28CC7B26A283B. swin-tiny-patch4-window7-224.json. Provenance. Takedown history: none.
Source
huggingface.co/microsoft/swin-tiny-patch4-window7-224

Variants

1 quantized or adapter build of swin-tiny-patch4-window7-224. Each has its own license check and page.

SubjectSizeParamsLicenseDate
swin-tiny-patch4-window7-224-ONNXHosted (signed) From: onnx-community in alt.freeweights.vision328 MB—Permissive2026-11-15

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