mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 12:50:06 +06:00
3ae52cc312
* Update CvT documentation with improved usage examples and additional notes * initial update * cvt * Update docs/source/en/model_doc/cvt.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update cvt.md --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
121 lines
3.8 KiB
Markdown
121 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">
|
|
<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
|
|
</div>
|
|
</div>
|
|
|
|
# Convolutional Vision Transformer (CvT)
|
|
|
|
Convolutional Vision Transformer (CvT) is a model that combines the strengths of convolutional neural networks (CNNs) and Vision transformers for the computer vision tasks. It introduces convolutional layers into the vision transformer architecture, allowing it to capture local patterns in images while maintaining the global context provided by self-attention mechanisms.
|
|
|
|
You can find all the CvT checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=cvt) organization.
|
|
|
|
> [!TIP]
|
|
> This model was contributed by [anujunj](https://huggingface.co/anugunj).
|
|
>
|
|
> Click on the CvT models in the right sidebar for more examples of how to apply CvT to different computer vision tasks.
|
|
|
|
The example below demonstrates how to classify an image with [`Pipeline`] or the [`AutoModel`] class.
|
|
|
|
<hfoptions id="usage">
|
|
<hfoption id="Pipeline">
|
|
|
|
```py
|
|
import torch
|
|
from transformers import pipeline
|
|
|
|
pipeline = pipeline(
|
|
task="image-classification",
|
|
model="microsoft/cvt-13",
|
|
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/cvt-13")
|
|
model = AutoModelForImageClassification.from_pretrained(
|
|
"microsoft/cvt-13",
|
|
torch_dtype=torch.float16,
|
|
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("cuda")
|
|
|
|
with torch.no_grad():
|
|
logits = model(**inputs).logits
|
|
predicted_class_id = logits.argmax(dim=-1).item()
|
|
|
|
class_labels = model.config.id2label
|
|
predicted_class_label = class_labels[predicted_class_id]
|
|
print(f"The predicted class label is: {predicted_class_label}")
|
|
```
|
|
|
|
</hfoption>
|
|
</hfoptions>
|
|
|
|
## Resources
|
|
|
|
Refer to this set of ViT [notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer) for examples of inference and fine-tuning on custom datasets. Replace [`ViTFeatureExtractor`] and [`ViTForImageClassification`] in these notebooks with [`AutoImageProcessor`] and [`CvtForImageClassification`].
|
|
|
|
## CvtConfig
|
|
|
|
[[autodoc]] CvtConfig
|
|
|
|
<frameworkcontent>
|
|
<pt>
|
|
|
|
## CvtModel
|
|
|
|
[[autodoc]] CvtModel
|
|
- forward
|
|
|
|
## CvtForImageClassification
|
|
|
|
[[autodoc]] CvtForImageClassification
|
|
- forward
|
|
|
|
</pt>
|
|
<tf>
|
|
|
|
## TFCvtModel
|
|
|
|
[[autodoc]] TFCvtModel
|
|
- call
|
|
|
|
## TFCvtForImageClassification
|
|
|
|
[[autodoc]] TFCvtForImageClassification
|
|
- call
|
|
|
|
</tf>
|
|
</frameworkcontent>
|