Instructions to use ZafarLocAI/convnext_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZafarLocAI/convnext_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ZafarLocAI/convnext_checkpoints") 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("ZafarLocAI/convnext_checkpoints") model = AutoModelForImageClassification.from_pretrained("ZafarLocAI/convnext_checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f3aa75595ca8050bb75b15e21fd5e20148b123ba4c4a14debb93fb515992a4b
- Size of remote file:
- 786 MB
- SHA256:
- 53bc6169cca2281c48bee8c32f57143cf123e56ff724d53e0e70eb4e71bd1fa6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.