mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 21:00:08 +06:00
36f97ae15b
* 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.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
<div style="float: right;">
|
|
<div class="flex flex-wrap space-x-1">
|
|
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
|
|
</div>
|
|
</div>
|
|
|
|
# Swin Transformer V2
|
|
|
|
[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.
|
|
|
|
You can find official Swin Transformer V2 checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=swinv2) organization.
|
|
|
|
> [!TIP]
|
|
> Click on the Swin Transformer V2 models in the right sidebar for more examples of how to apply Swin Transformer V2 to vision tasks.
|
|
|
|
<hfoptions id="usage">
|
|
<hfoption id="Pipeline">
|
|
|
|
```py
|
|
import torch
|
|
from transformers import pipeline
|
|
|
|
pipeline = pipeline(
|
|
task="image-classification",
|
|
model="microsoft/swinv2-tiny-patch4-window8-256",
|
|
torch_dtype=torch.float16,
|
|
device=0
|
|
)
|
|
pipeline(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
|
|
```
|
|
|
|
</hfoption>
|
|
|
|
<hfoption id="AutoModel">
|
|
|
|
```py
|
|
import torch
|
|
import requests
|
|
from PIL import Image
|
|
from transformers import AutoModelForImageClassification, AutoImageProcessor
|
|
|
|
image_processor = AutoImageProcessor.from_pretrained(
|
|
"microsoft/swinv2-tiny-patch4-window8-256",
|
|
)
|
|
model = AutoModelForImageClassification.from_pretrained(
|
|
"microsoft/swinv2-tiny-patch4-window8-256",
|
|
device_map="auto"
|
|
)
|
|
|
|
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
|
|
image = Image.open(requests.get(url, stream=True).raw)
|
|
inputs = image_processor(image, return_tensors="pt").to(model.device)
|
|
|
|
with torch.no_grad():
|
|
logits = model(**inputs).logits
|
|
|
|
predicted_class_id = logits.argmax(dim=-1).item()
|
|
predicted_class_label = model.config.id2label[predicted_class_id]
|
|
print(f"The predicted class label is: {predicted_class_label}")
|
|
```
|
|
|
|
</hfoption>
|
|
</hfoptions>
|
|
|
|
## Notes
|
|
|
|
- Swin Transformer V2 can pad the inputs for any input height and width divisible by `32`.
|
|
- 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)`.
|
|
|
|
## Swinv2Config
|
|
|
|
[[autodoc]] Swinv2Config
|
|
|
|
## Swinv2Model
|
|
|
|
[[autodoc]] Swinv2Model
|
|
- forward
|
|
|
|
## Swinv2ForMaskedImageModeling
|
|
|
|
[[autodoc]] Swinv2ForMaskedImageModeling
|
|
- forward
|
|
|
|
## Swinv2ForImageClassification
|
|
|
|
[[autodoc]] transformers.Swinv2ForImageClassification
|
|
- forward
|