A ConvNet for the 2020s
Paper • 2201.03545 • Published • 2
How to use cs-giung/convnext-v1-tiny-imagenet1k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="cs-giung/convnext-v1-tiny-imagenet1k")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png") # Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("cs-giung/convnext-v1-tiny-imagenet1k")
model = AutoModelForImageClassification.from_pretrained("cs-giung/convnext-v1-tiny-imagenet1k")ConvNext model pre-trained on ImageNet-1k (1.28 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s.
The weights were converted from the convnext_tiny_1k_224_ema.pth file presented in the official repository.