huggingface--transformers/docs/source/en/model_doc/hgnet_v2.md

This model was contributed to Hugging Face Transformers on 2025-04-29.

HGNet-V2

HGNetV2 is a next-generation convolutional neural network (CNN) backbone built for optimal accuracy-latency tradeoff on NVIDIA GPUs. Building on the originalHGNet, 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 organization.

Tip

This model was contributed by 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.

from transformers import pipeline


pipeline = pipeline(
    task="image-classification",
    model="ustc-community/hgnet-v2",
    device=0
)
pipeline("http://images.cocodataset.org/val2017/000000039769.jpg")

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}")

HGNetV2Config

autodoc HGNetV2Config

HGNetV2Backbone

autodoc HGNetV2Backbone - forward

HGNetV2ForImageClassification

autodoc HGNetV2ForImageClassification - forward