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+*This model was contributed to Hugging Face Transformers on 2025-04-29.*
+
+
+# HGNet-V2
+
+[HGNetV2](https://github.com/PaddlePaddle/PaddleClas/blob/v2.6.0/docs/zh_CN/models/ImageNet1k/PP-HGNetV2.md) is a next-generation convolutional neural network (CNN) backbone built for optimal accuracy-latency tradeoff on NVIDIA GPUs. Building on the original[HGNet](https://github.com/PaddlePaddle/PaddleClas/blob/v2.6.0/docs/en/models/PP-HGNet_en.md), HGNetV2 delivers high accuracy at fast inference speeds and performs strongly on tasks like image classification, object detection, and segmentation, making it a practical choice for GPU-based computer vision applications.
+
+You can find all the original HGNet V2 models under the [USTC](https://huggingface.co/ustc-community/models?search=hgnet) organization.
+
+> [!TIP]
+> This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber).
+> Click on the HGNet V2 models in the right sidebar for more examples of how to apply HGNet V2 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">
+
+```python
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="image-classification",
+    model="ustc-community/hgnet-v2",
+    device=0
+)
+pipeline("http://images.cocodataset.org/val2017/000000039769.jpg")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import AutoImageProcessor, HGNetV2ForImageClassification
+
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+model = HGNetV2ForImageClassification.from_pretrained("ustc-community/hgnet-v2", device_map="auto")
+processor = AutoImageProcessor.from_pretrained("ustc-community/hgnet-v2")
+
+inputs = processor(images=image, return_tensors="pt").to(model.device)
+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