Instructions to use cs-giung/convnext-v1-base-imagenet21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-giung/convnext-v1-base-imagenet21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cs-giung/convnext-v1-base-imagenet21k") 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-v1-base-imagenet21k") model = AutoModelForImageClassification.from_pretrained("cs-giung/convnext-v1-base-imagenet21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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license: apache-2.0
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# ConvNext
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ConvNext model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
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The weights were converted from the `convnext_base_22k_224.pth` file presented in the [official repository](https://github.com/facebookresearch/ConvNeXt).
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