This is the HF transformers implementation for HGNet-V2

Model: HGNet-V2 - B4

A HGNet-V2 (High Performance GPU Net) image classification model.

Usage:

import torch
import requests

from PIL import Image
from transformers import HGNetV2ForImageClassification, AutoImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("ustc-community/hgnet-v2")
model = HGNetV2ForImageClassification.from_pretrained("ustc-community/hgnet-v2")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
outputs.logits.shape
torch.Size([1, 2])
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