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*This model was released on 2024-07-01 and added to Hugging Face Transformers on 2025-04-29 and contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber).*

# HGNet-V2

[HGNetV2](https://github.com/PaddlePaddle/PaddleClas/blob/v2.6.0/docs/zh_CN/models/ImageNet1k/PP-HGNetV2.md) is a next-generation efficient convolutional neural network proposed by PaddlePaddle, mainly designed for image classification tasks. Its architecture adopts a hierarchical hybrid group convolution (HGConv) design, combined with lightweight attention mechanisms and improved residual connections, to boost accuracy while maintaining computational efficiency. Experimental results show that it outperforms similar lightweight models on ImageNet1K, while also balancing training and inference speed. Overall, PP-HGNetV2 offers both efficiency and practicality, making it well-suited for deployment in resource-constrained environments.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="ustc-community/hgnet-v2", dtype="auto")
pipeline("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

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

model = AutoModelForImageClassification.from_pretrained("ustc-community/hgnet-v2", dtype="auto")
processor = AutoImageProcessor.from_pretrained("ustc-community/hgnet-v2")

inputs = processor(images=image, return_tensors="pt")
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>

## HGNetV2Config

[[autodoc]] HGNetV2Config

## HGNetV2Backbone

[[autodoc]] HGNetV2Backbone
    - forward

## HGNetV2ForImageClassification

[[autodoc]] HGNetV2ForImageClassification
    - forward