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* docs(swinv2): Update SwinV2 model card to new standard format * docs(swinv2): Apply review suggestions Incorporates feedback from @stevhliu to: - Enhance the introductory paragraph with more details about scaling and SimMIM. - Generalize the tip from "image classification tasks" to "vision tasks". Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
104 lines
3.8 KiB
Markdown
104 lines
3.8 KiB
Markdown
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# Swin Transformer V2
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[Swin Transformer V2](https://huggingface.co/papers/2111.09883) is a 3B parameter model that focuses on how to scale a vision model to billions of parameters. It introduces techniques like residual-post-norm combined with cosine attention for improved training stability, log-spaced continuous position bias to better handle varying image resolutions between pre-training and fine-tuning, and a new pre-training method (SimMIM) to reduce the need for large amounts of labeled data. These improvements enable efficiently training very large models (up to 3 billion parameters) capable of processing high-resolution images.
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You can find official Swin Transformer V2 checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=swinv2) organization.
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> [!TIP]
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> Click on the Swin Transformer V2 models in the right sidebar for more examples of how to apply Swin Transformer V2 to vision tasks.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="image-classification",
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model="microsoft/swinv2-tiny-patch4-window8-256",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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import requests
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from PIL import Image
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from transformers import AutoModelForImageClassification, AutoImageProcessor
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image_processor = AutoImageProcessor.from_pretrained(
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"microsoft/swinv2-tiny-patch4-window8-256",
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)
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model = AutoModelForImageClassification.from_pretrained(
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"microsoft/swinv2-tiny-patch4-window8-256",
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device_map="auto"
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = image_processor(image, return_tensors="pt").to(model.device)
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with torch.no_grad():
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logits = model(**inputs).logits
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predicted_class_id = logits.argmax(dim=-1).item()
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predicted_class_label = model.config.id2label[predicted_class_id]
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print(f"The predicted class label is: {predicted_class_label}")
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Swin Transformer V2 can pad the inputs for any input height and width divisible by `32`.
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- Swin Transformer V2 can be used as a [backbone](../backbones). When `output_hidden_states = True`, it outputs both `hidden_states` and `reshaped_hidden_states`. The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than `(batch_size, sequence_length, num_channels)`.
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## Swinv2Config
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[[autodoc]] Swinv2Config
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## Swinv2Model
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[[autodoc]] Swinv2Model
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- forward
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## Swinv2ForMaskedImageModeling
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[[autodoc]] Swinv2ForMaskedImageModeling
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- forward
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## Swinv2ForImageClassification
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[[autodoc]] transformers.Swinv2ForImageClassification
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- forward
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