ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Paper • 2301.00808 • Published • 2
How to use cs-giung/convnext-v2-huge-imagenet1k-fcmae with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="cs-giung/convnext-v2-huge-imagenet1k-fcmae")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("cs-giung/convnext-v2-huge-imagenet1k-fcmae")
model = AutoModelForImageClassification.from_pretrained("cs-giung/convnext-v2-huge-imagenet1k-fcmae", device_map="auto")ConvNext-V2 model pre-trained on ImageNet-1k (1.28 million images, 1,000 classes) at resolution 224x224 in a fully convolutional masked autoencoder framework (FCMAE). It was introduced in the paper ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.
The weights were converted from the convnextv2_huge_1k_224_fcmae.pt file presented in the official repository.