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zeromodels/swin_tiny_patch4_window7_224_ms_in22k
swin_tiny_patch4_window7_224_ms_in22k is a image classification model from zeromodels. Use it when you need a label for an image. It is set up for zeromodels. The card lists the license as mit.
[](https://github.com/IMvision12/ZeroModels) [](https://imvision12.github.io/ZeroModels/classificationbackbones/) [](https://huggingface.co/collections/zeromodels/swin-transformer-6a8eae9c99ad126043e91850)
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From the Hugging Face model README
Paper: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (arXiv:2103.14030) · HF Papers
Swin Transformer builds hierarchical feature maps with shifted-window attention. Strong as an ImageNet classifier and as a 4-stage backbone.
For more details on the model, please go to the upstream model card.
Pure-Keras 3 conversion of timm/swin_tiny_patch4_window7_224.ms_in22k for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.
This is an image-classification / backbone checkpoint (SwinImageClassify / SwinModel).
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.swin import SwinImageClassify, SwinModel, SwinImageProcessor
model = SwinImageClassify.from_weights("zeromodels/swin_tiny_patch4_window7_224_ms_in22k")
processor = SwinImageProcessor.from_weights("zeromodels/swin_tiny_patch4_window7_224_ms_in22k")
image = Image.open("your_image.jpg").convert("RGB")
pixels = processor(image) # resize + normalize (normalization lives in the processor)
logits = model(pixels, training=False)
print(logits.shape) # (1, num_classes)
# Feature extraction: the backbone without the classifier head
backbone = SwinModel.from_weights("zeromodels/swin_tiny_patch4_window7_224_ms_in22k", as_backbone=True)
features = backbone(pixels, training=False)
Load any Swin Transformer variant the same way with from_weights("zeromodels/<variant>"):
KERAS_BACKEND before importing Keras / zeromodels.SwinImageClassify returns class logits; SwinModel returns features (as_backbone=True for multi-scale stages).SwinImageClassify.from_weights("hf:timm/swin_tiny_patch4_window7_224.ms_in22k").A huge thank you to the Swin Transformer authors and the timm / Hub communities for creating and releasing these models.
License: see YAML license (usually matches the upstream checkpoint).