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")# pip install -U transformers accelerate # 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
Download model.safetensors from ZafarLocAI/convnext_checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 786 MB
-
https://huggingface.co/ZafarLocAI/convnext_checkpoints/resolve/main/model.safetensors
- Command line
-
hf download hf://ZafarLocAI/convnext_checkpoints/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ZafarLocAI/convnext_checkpoints/resolve/main/model.safetensors
786 MB
- Xet hash:
- 7f3aa75595ca8050bb75b15e21fd5e20148b123ba4c4a14debb93fb515992a4b
- Size of remote file:
- 786 MB
- SHA256:
- 53bc6169cca2281c48bee8c32f57143cf123e56ff724d53e0e70eb4e71bd1fa6
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